Integrated Collaborative Design Method for Municipal Intelligent Construction

Abstract

The present invention discloses an integrated collaborative design method for municipal intelligent construction, which relates to the technical field of municipal intelligent construction and includes the following steps: obtaining initial geological survey data for municipal projects, and monitoring the dynamic changes of geological data during construction in real time through sensor equipment. Through the two-way dynamic linkage of BIM models and geological data and the geological change prediction model, the present invention enables municipal projects to grasp geological changes in real time, automatically adjust the design and construction plans, ensure that the design matches the actual geological conditions, and effectively prevent hidden dangers such as roadbed settlement and pipeline dislocation. Combined with machine learning, optimization algorithms and intelligent decision-making systems, the construction party can generate adjustment suggestions in real time, flexibly optimize the construction sequence, material selection and equipment use strategies, improve construction efficiency and reduce costs. Multi-dimensional evaluation ensures that construction decisions are scientific and reasonable, reduces the risks caused by geological changes, and provides an efficient solution for the digital and intelligent transformation of municipal engineering.

Description

Technical Field

[0001] The present invention relates to the technical field of municipal intelligent construction, and in particular to an integrated collaborative design method for municipal intelligent construction. Background Art

[0002] "Integrated Collaborative Design for Municipal Intelligent Construction" refers to the intelligent integration of municipal engineering planning, design, construction, and management, enabling efficient collaboration across diverse disciplines, departments, and stakeholders. This initiative leverages digital technology, integrating BIM (Building Information Modeling), GIS (Geographic Information System), the Internet of Things, and artificial intelligence throughout the project lifecycle. This approach breaks with traditional single-design models and, through data sharing, real-time collaboration, and automated decision-making, improves design accuracy and construction efficiency, reduces costs and risks, and ultimately achieves digitalization, intelligence, and greening throughout the entire municipal infrastructure construction process.

[0003] The existing technology has the following deficiencies:

[0004] In the integrated collaborative design process of municipal intelligent construction, insufficient real-time dynamic linkage between geological data and BIM models may lead to serious consequences. Geological survey data is usually collected in the early stages and statically entered into the BIM system, but geological conditions often change during the construction phase, such as groundwater level fluctuations and reduced soil bearing capacity. Without dynamic data updates and linkage mechanisms, the design plan will not be adjusted in a timely manner, resulting in a mismatch between the foundation structure design and the actual geological conditions. In subsequent construction and operation, this deviation may lead to major hidden dangers such as roadbed settlement, dislocation or rupture of underground pipelines, and even tilting of buildings. Since these problems are usually difficult to detect in the early stages, they often require high repair costs and have long-term impacts on the safety, stability and service life of municipal facilities.

[0005] The above information disclosed in this Background section is only for enhancement of understanding of the background of the present disclosure and therefore it may contain information that does not form the prior art that is already known to a person of ordinary skill in the art. Summary of the Invention

[0006] The purpose of the present invention is to provide an integrated collaborative design method for municipal intelligent construction. Through the two-way dynamic linkage of BIM models and geological data and the geological change prediction model, municipal projects can grasp geological changes in real time, automatically adjust the design and construction plan, ensure that the design matches the actual geological conditions, and effectively prevent hidden dangers such as roadbed settlement and pipeline dislocation. Combined with machine learning, optimization algorithms and intelligent decision-making systems, the construction party can generate adjustment suggestions in real time, flexibly optimize the construction sequence, material selection and equipment use strategies, improve construction efficiency and reduce costs. Multi-dimensional evaluation ensures that construction decisions are scientific and reasonable, reduces the risks caused by geological changes, and provides an efficient solution for the digital and intelligent transformation of municipal engineering to solve the problems in the above-mentioned background technology.

[0007] In order to achieve the above objectives, the present invention provides the following technical solution: a method for integrated collaborative design of municipal intelligent construction, comprising the following steps:

[0008] Obtain initial geological survey data for municipal projects and use sensor equipment to monitor dynamic changes in geological data during construction in real time;

[0009] Input the initial geological survey data and real-time monitoring data into the building information model, and use the data interface to achieve two-way dynamic linkage between the building information model and geological information to ensure real-time update of geological data;

[0010] Construct a geological change prediction model during the construction phase within the building information model. Using machine learning algorithms, we predict geological condition change trends based on historical geological data and real-time monitoring data, enabling early prediction of geological changes.

[0011] Based on the output of the geological change prediction model, construction risk warning data is generated, and the infrastructure design parameters in the building information model are dynamically adjusted through deep learning algorithms to ensure that the design plan adapts to geological changes;

[0012] Through optimization algorithms, construction plans are adjusted in real time. Construction sequence, material selection, and mechanical equipment utilization strategies are dynamically optimized based on real-time geological changes and construction progress, improving construction efficiency and safety.

[0013] Apply intelligent decision-making algorithms to conduct multi-dimensional evaluations of construction adjustment plans, generate final construction adjustment recommendations, and synchronize them to the construction management system to guide actual construction.

[0014] Preferably, the specific steps for obtaining the initial geological survey data of a municipal project and monitoring the dynamic changes of the geological data during the construction process in real time through sensor equipment are as follows:

[0015] Develop geological survey plans to ensure that the collected data is comprehensive and accurate, laying the foundation for subsequent analysis;

[0016] Deploy sensor equipment according to the plan to ensure accurate and reliable data collection at key geological feature points;

[0017] Classify, clean and standardize the collected geological data for uploading to ensure data accuracy and facilitate subsequent model application;

[0018] The Internet of Things enables dynamic monitoring and early warning of geological data, and timely identification of abnormal changes to reduce construction risks.

[0019] Preferably, the initial geological survey data and real-time monitoring data are input into the building information model, and the two-way dynamic linkage between the building information model and the geological information is realized by means of a data interface to ensure the real-time update of the geological data. The specific steps are as follows:

[0020] Develop data interfaces to implement conversion, verification, and filtering of geological data formats to ensure that initial data and real-time monitoring data are accurately received by the building information model;

[0021] Map geological data to corresponding locations and parameters in the building information model according to spatial coordinates to ensure that geological change information is intuitively presented in the design model;

[0022] Through a two-way dynamic linkage mechanism, geological data is updated in real time and the design parameters of the building information model are automatically adjusted to cope with changes in geological conditions;

[0023] Establish a geological data anomaly early warning system, combine building information models and historical data, automatically identify risks and provide construction adjustment suggestions, and improve the safety and intelligence level of construction.

[0024] Preferably, a geological change prediction model for the construction phase is constructed in the building information model. By using a machine learning algorithm, the geological condition change trend is predicted based on historical geological data and real-time monitoring data. The specific steps for forming an early prediction of geological changes are as follows:

[0025] Integrate initial geological data and real-time monitoring data, clean outliers, and build high-quality datasets to provide a reliable training foundation for geological change prediction models;

[0026] Select machine learning algorithms based on geological data characteristics, optimize model performance through hyperparameter tuning and validation sets, and improve the accuracy and generalization ability of geological change predictions;

[0027] Embed the trained geological change prediction model into the building information model and integrate it with the two-way dynamic linkage mechanism of geological data to achieve real-time prediction and dynamic design adjustment;

[0028] By analyzing the prediction results, the building information model automatically triggers risk warnings and generates construction adjustment suggestions to guide the construction party to respond to geological changes in a timely manner and reduce construction risks.

[0029] Preferably, the specific steps for generating construction risk warning data based on the output of the geological change prediction model and dynamically adjusting the infrastructure design parameters in the building information model through a deep learning algorithm to ensure that the design solution adapts to geological changes are as follows:

[0030] Key output data is extracted from the geological change prediction model, including groundwater level change trends, soil bearing capacity change rates, and foundation settlement rates. The construction risk warning value is calculated based on the obtained data. The calculation formula for the construction risk warning value is as follows:

[0031] , where is the construction risk warning value, is the trend of groundwater level change, is the rate of change of soil bearing capacity, is the ground settlement rate, 、 as well as are weight coefficients, representing the changing trends of groundwater levels. , soil bearing capacity change rate and foundation settlement rate The weight of influence on the construction risk warning value;

[0032] According to the construction risk warning value , calculate the adjustment coefficient of the foundation structure, which is used to dynamically adjust the structural design parameters in the BIM model. The calculation expression is as follows:

[0033] , where is the basic structure adjustment coefficient, is the nonlinear mapping function generated by the deep learning model, is the natural base, is the weight parameter, is the bias parameter;

[0034] Using the basic structure adjustment factor Dynamically update the basic structural design parameters in the BIM model. The adjustment formula is as follows: , , , where is the adjusted foundation width, is the initial foundation width, is the adjusted concrete strength grade, is the initial concrete strength grade, is the adjusted steel bar diameter, is the initial bar diameter.

[0035] Preferably, the construction plan is adjusted in real time through optimization algorithms, and the construction sequence, material selection, and mechanical equipment usage strategy are dynamically optimized according to real-time geological changes and construction progress to improve construction efficiency and safety. The specific steps are as follows:

[0036] First, the real-time geological data and construction progress data collected by sensors are normalized so that data from different sources and units can be calculated in the same model. The construction environment parameters are generated based on the weather impact coefficient of the progress deviation in the current construction stage. The generation formula is as follows:

[0037] , where is the trend of groundwater level change, is the rate of change of soil bearing capacity, is the change in soil moisture, is the weather influence coefficient, It is the construction progress deviation. It is a comprehensive parameter of the construction environment;

[0038] Obtaining comprehensive parameters of the construction environment Finally, the construction sequence is dynamically adjusted using an optimization algorithm to ensure the safety and efficiency of the construction process. The construction sequence weight coefficient is introduced to dynamically adjust the priority of each task based on its sensitivity to geological changes and construction difficulty. The resource utilization coefficient is introduced to calculate the adjustment priority of each construction task. The calculation expression is as follows:

[0039] , where It is a construction task Adjustment priority, It is a construction task The order weight coefficient of It is a construction task Utilization of required resources;

[0040] After adjusting the construction sequence, according to the current construction environment parameters and adjust priorities of construction tasks , further optimize the use strategy of mechanical equipment and material selection scheme, introduce mechanical equipment efficiency coefficient and material adaptability coefficient, calculate the total cost-benefit ratio of each construction task, and select the optimal equipment and material scheme. The calculation expression is as follows:

[0041] , where It is a construction task The overall cost-effectiveness ratio, is the material adaptability coefficient, is the efficiency coefficient of mechanical equipment.

[0042] Preferably, an intelligent decision-making algorithm is applied to conduct a multi-dimensional evaluation of the construction adjustment plan, generate a final construction adjustment suggestion, and synchronize it to the construction management system to guide the actual construction. The specific steps are as follows:

[0043] First, based on the output of the geological change prediction model and real-time monitoring data, the comprehensive risk index of the construction adjustment plan is calculated. The comprehensive risk index is used to quantify the potential risks of different construction adjustment plans and evaluate them from the perspectives of safety, economy, and environmental impact. The calculation formula of the risk index is as follows:

[0044] , where It is The weight coefficient of geological risk is It is The monitoring value of geological risk, It is The emergency response cost of each geological risk, It is The safety impact level of each geological risk, is the total number of geological risk types, It is The weight coefficient of the construction adjustment strategy, It is The implementation difficulty score of the construction adjustment strategy, It is The time cost of the construction adjustment strategy, It is Environmental friendliness scores of construction adjustment strategies, is the total number of construction adjustment strategies, is the comprehensive risk index;

[0045] Obtaining a comprehensive risk index After that, the benefit trade-off value of the construction adjustment plan is further calculated to evaluate the overall benefit of the adjustment plan. The calculation expression of the benefit trade-off value is as follows:

[0046] , where It is The direct economic benefits of this construction adjustment strategy, It is The construction feasibility scores of the construction adjustment strategies are: It is Social benefit scores of construction adjustment strategies, is the total number of construction adjustment strategies, is the time cost weight coefficient, is the labor cost weight coefficient, is the environmental cost weight coefficient, is the total expected time cost of the construction adjustment plan, is the total expected labor cost of the construction adjustment scenario, is the total environmental cost of the construction adjustment plan, is the benefit trade-off value.

[0047] In the above technical solution, the technical effects and advantages provided by the present invention are:

[0048] Through the two-way dynamic linkage between the BIM model and geological data and the application of geological change prediction models, the present invention enables municipal projects to grasp the changes in geological conditions in real time during the construction process and automatically adjust the design and construction plans accordingly. This dynamic data-driven approach means that the design plan no longer relies on static geological survey data, but is continuously updated based on real-time monitoring results to ensure that the design always matches the actual geological conditions. For example, when it is monitored that the groundwater level is rising or the soil bearing capacity is decreasing, the system can automatically update the drainage system and foundation reinforcement plan, thereby effectively preventing hidden dangers such as roadbed settlement, dislocation of underground pipelines, and tilting of buildings. This beneficial effect significantly improves the overall safety of municipal projects, reduces rework, accidents and economic losses caused by geological changes, and thus ensures the stability and controllability of the construction process.

[0049] The present invention combines the application of machine learning algorithms, optimization algorithms and intelligent decision-making systems. During the construction process of municipal projects, it can automatically identify geological change trends and generate targeted construction adjustment suggestions. This intelligent construction plan adjustment process enables the construction party to flexibly adjust the construction sequence, material selection and mechanical equipment usage strategy based on the real-time optimization suggestions provided by the system, thereby maximizing construction efficiency and reducing construction costs. At the same time, the system conducts a comprehensive multi-dimensional evaluation of the construction adjustment plan, including safety, economy and sustainability, to ensure that construction decisions are more scientific and reasonable. This intelligent optimization and decision-making mechanism not only improves the execution efficiency of the project, but also significantly reduces the uncontrollable factors caused by geological changes, providing an effective solution for the digital and intelligent transformation of municipal engineering construction. BRIEF DESCRIPTION OF THE DRAWINGS

[0050] In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, a brief introduction to the drawings required for use in the embodiments will be given below. Obviously, the drawings described below are only some embodiments recorded in the present invention. For ordinary technicians in this field, other drawings can also be obtained based on these drawings.

[0051] Figure 1This is a method flow chart of the integrated collaborative design method for municipal intelligent construction of the present invention. DETAILED DESCRIPTION

[0052] Example embodiments will now be described more fully with reference to the accompanying drawings. However, example embodiments can be implemented in many forms and should not be construed as limited to the examples set forth herein; rather, these example embodiments are provided so that the description of this disclosure will be thorough and complete and will fully convey the concepts of the example embodiments to those skilled in the art.

[0053] The present invention provides Figure 1 The integrated collaborative design method for municipal intelligent construction shown includes the following steps:

[0054] Obtain initial geological survey data for municipal projects, including parameters such as groundwater level, soil type, and soil bearing capacity, and use sensor equipment to monitor the dynamic changes of geological data during construction in real time;

[0055] The specific steps for obtaining initial geological survey data for municipal projects, including parameters such as groundwater level, soil type, and soil bearing capacity, and using sensor equipment to monitor the dynamic changes of geological data during construction in real time are as follows:

[0056] Develop geological survey plans to ensure that the collected data is comprehensive and accurate, laying the foundation for subsequent analysis;

[0057] First, a detailed geological survey plan is developed based on the scale, location, and construction requirements of the municipal project. This plan includes determining the boundaries of the survey area, the types of geological data to be collected (such as groundwater level, soil type, and soil bearing capacity), the survey depth, and the distribution density of sampling points. This process also requires consideration of factors such as the historical geological data and climatic conditions of the municipal project area to ensure the comprehensiveness and scientific nature of the survey plan. Furthermore, appropriate survey tools and equipment, such as geological drilling equipment and static cone penetration instruments, are selected based on the specific geological data collection requirements to lay the foundation for subsequent data collection.

[0058] Planning the geological survey area is a key step in initial data collection. Proper planning can effectively avoid data blind spots and ensure that the collected geological data is comprehensive and representative. Furthermore, incorporating historical geological data can improve the accuracy of data collection, providing more accurate foundational data for model predictions.

[0059] Deploy sensor equipment according to the plan to ensure accurate and reliable data collection at key geological feature points;

[0060] According to the planned survey plan, geological survey equipment is deployed at the construction site, and field sampling is conducted at various survey points. During deployment, special attention must be paid to the equipment's installation location and the distribution density of data collection points to ensure that the collected data covers key geological features within the construction area. To collect parameters such as groundwater level, soil type, and soil density, specialized sensor equipment such as groundwater monitoring wells, soil moisture sensors, and soil pressure sensors are required. After the equipment is deployed, it must be debugged and calibrated to ensure accurate and stable geological data collection during operation.

[0061] The proper placement of on-site equipment directly impacts the quality and reliability of subsequent geological data. Geological characteristics within a construction area often exhibit significant spatial variation, so the proper placement of sensors and sampling points can more accurately reflect geological variations within the construction area. Furthermore, equipment commissioning and calibration can effectively reduce data errors and ensure the accuracy of subsequent model analysis.

[0062] Classify, clean and standardize the collected geological data for uploading to ensure data accuracy and facilitate subsequent model application;

[0063] After the equipment is deployed, geological data collection begins. Using specialized equipment, data such as groundwater levels, soil types, and soil bearing capacity are collected in real time, and the collected data is classified, aggregated, and digitized. To facilitate subsequent BIM model linkage, the collected data must be stored in a standardized format and uploaded to the cloud or local database through a data interface. Before uploading the data, it must be cleaned and verified to eliminate abnormal or erroneous data and ensure data accuracy and consistency. At the same time, the initial geological survey data, as important input data before construction, must also be generated into a survey report and archived.

[0064] Accurately collecting and uploading geological data is crucial for ensuring a reliable foundation for BIM models. Data standardization and cleansing effectively prevents model analysis errors caused by inconsistent formats or data errors, thereby improving the accuracy and practicality of model predictions. Furthermore, cloud-based data storage and sharing enhances team collaboration and facilitates subsequent data updates and management.

[0065] The Internet of Things enables dynamic monitoring and early warning of geological data, identifying abnormal changes in a timely manner to reduce construction risks;

[0066] During construction, deployed sensors monitor geological data in real time, including changes in groundwater levels, soil moisture, and soil bearing capacity. Leveraging IoT technology, these data are automatically transmitted to the BIM system, enabling dynamic updates and two-way linkage. The monitoring system must also provide automatic early warning capabilities. If it detects unusual changes in geological data (such as a rapid rise in groundwater levels or a sharp drop in soil bearing capacity), it will automatically issue a warning, alerting the construction team to take preventative measures and avoid construction risks associated with these changes.

[0067] Real-time monitoring of geological changes can effectively address the risks of unforeseen geological changes during construction. IoT technology automates the collection, transmission, and analysis of geological data, reducing errors potentially caused by human intervention. Furthermore, automatic early warning functions can help construction companies take timely measures before geological changes pose safety hazards, thereby reducing construction risks and improving project safety and stability.

[0068] Input the initial geological survey data and real-time monitoring data into the building information model, and use the data interface to achieve two-way dynamic linkage between the building information model and geological information to ensure real-time update of geological data;

[0069] The specific steps for inputting the initial geological survey data and real-time monitoring data into the building information model and realizing the two-way dynamic linkage between the building information model and geological information with the help of the data interface to ensure the real-time update of geological data are as follows:

[0070] Develop data interfaces to implement conversion, verification, and filtering of geological data formats to ensure that initial data and real-time monitoring data are accurately received by the building information model;

[0071] Before geological data can be input into a building information model (BIM), a data interface must be developed or deployed to ensure that the initial geological survey data and real-time sensor monitoring data can be recognized and received by the BIM system. Initial geological data, including parameters such as groundwater level, soil type, and soil bearing capacity, is typically stored in a static format, while real-time data collected by sensor equipment is continuously and dynamically changing. Therefore, the data interface must have format conversion, data verification, and anomaly filtering capabilities to ensure that the data input into the BIM system remains consistent and accurate. For example, the data interface can uniformly convert geological data from different sources into JSON, XML, or other standardized formats, and eliminate noisy and invalid data to improve data quality.

[0072] The data interface is the key bridge for dynamic, two-way linkage between BIM models and geological data. Because geological data comes from diverse sources and complex data types, the data interface must not only ensure format compatibility but also conduct real-time data verification and filtering to prevent data errors from impacting the accuracy of model analysis and ensure that the BIM system consistently receives high-quality geological data.

[0073] Map geological data to corresponding locations and parameters in the building information model according to spatial coordinates to ensure that geological change information is intuitively presented in the design model;

[0074] After the geological data enters the BIM system through the data interface, it needs to be mapped to the corresponding positions and parameters in the BIM model. For example, the groundwater level data is mapped to the hydrological model of the foundation, the soil type data is mapped to the geological layer model of the foundation structure, and the soil bearing capacity data is mapped to the load analysis module of the design structure. This mapping process requires the precise correspondence between the monitoring data and the building parts in the BIM model based on the spatial coordinate information of the geological data. In addition, to achieve a more intuitive display effect, visualization technology can be used to intuitively present the geological data in the form of color coding or dynamic charts to help designers quickly identify geological changes in different areas.

[0075] Data mapping is a crucial step in the deep integration of geological information and BIM models. By accurately mapping geological data to various parts of the BIM model, a dynamic connection between architectural design and geological conditions can be achieved, enabling designers to intuitively understand the impact of geological changes on structural design, thereby improving design accuracy and construction safety.

[0076] Through a two-way dynamic linkage mechanism, geological data is updated in real time and the design parameters of the building information model are automatically adjusted to cope with changes in geological conditions;

[0077] After completing the input and mapping of geological data, a two-way dynamic data linkage mechanism needs to be established to enable the BIM model to update geological data in real time and, in turn, provide feedback and adjustments to changes in geological data. Through this mechanism, when sensor equipment detects changes in parameters such as groundwater level and soil bearing capacity, the BIM system will automatically update the corresponding geological model and adjust the foundation structure design parameters based on the changes. For example, when the groundwater level rises beyond the set threshold, the BIM model will automatically add a drainage system design or adjust the foundation reinforcement plan. Two-way linkage also includes feedback from the BIM system to the construction site, such as synchronizing optimized construction adjustment suggestions to the sensor system to further guide the work of the monitoring equipment.

[0078] A two-way dynamic linkage mechanism ensures real-time synchronization between the BIM system and geological data, automatically adjusting the design plan based on changes. This linkage not only enables real-time data updates during construction, but also enables the BIM system to automatically respond to and make decisions about geological changes, thereby improving project safety and construction efficiency.

[0079] Establish a geological data anomaly early warning system that combines building information models and historical data to automatically identify risks and provide construction adjustment suggestions, thereby improving the safety and intelligence level of construction;

[0080] Building on this two-way dynamic linkage, a geological data anomaly warning mechanism has been established. When sensors detect unusual changes, such as a rapid rise in groundwater levels or a sharp drop in soil bearing capacity, the BIM system automatically triggers a warning signal and provides corresponding construction adjustment recommendations. For example, if a decrease in soil bearing capacity is detected in a specific area, the system will automatically prompt designers to adjust the foundation structure design, add foundation reinforcement measures, or change the construction sequence. To avoid false positives or missed reports, the warning thresholds are dynamically optimized by combining historical geological data with deep learning algorithms to ensure the accuracy and timeliness of warning signals.

[0081] Abnormal early warning and coordinated response mechanisms are crucial for intelligent decision-making. Through real-time monitoring and intelligent early warning, the BIM system can promptly identify potential construction risks and provide optimization recommendations, helping construction parties take proactive measures to avoid safety incidents and construction delays caused by geological changes. This mechanism effectively enhances the intelligence and adaptability of municipal project construction.

[0082] Construct a geological change prediction model during the construction phase within the building information model. Using machine learning algorithms, we predict geological condition change trends based on historical geological data and real-time monitoring data, enabling early prediction of geological changes.

[0083] The geological change prediction model for the construction phase is constructed in the building information model. Using machine learning algorithms, the geological condition change trend is predicted based on historical geological data and real-time monitoring data. The specific steps for forming an early prediction of geological changes are as follows:

[0084] Integrate initial geological data and real-time monitoring data, clean outliers, and build high-quality datasets to provide a reliable training foundation for geological change prediction models;

[0085] Before constructing a model to predict geological changes during the construction phase within a Building Information Model (BIM), it is necessary to first collect and integrate initial geological survey data and real-time monitoring data to form a comprehensive dataset. To improve the accuracy and stability of the model, this data requires cleaning and preprocessing, including removing outliers, filling in missing data, and standardizing the data format. By combining historical geological data (such as records of groundwater level fluctuations and trends in soil bearing capacity) with real-time monitoring data (dynamic data provided by sensor equipment), a multi-dimensional, long-term geological dataset can be constructed, providing high-quality input data for subsequent training of machine learning algorithms.

[0086] Data collection and cleaning are fundamental steps in building geological change prediction models, directly impacting their accuracy and reliability. By integrating multi-source geological data and removing noise, we can effectively prevent bias in the prediction model due to data quality issues and ensure a scientific and rational model training process.

[0087] Select machine learning algorithms based on geological data characteristics, optimize model performance through hyperparameter tuning and validation sets, and improve the accuracy and generalization ability of geological change predictions;

[0088] After preparing the training dataset, we select a machine learning algorithm suitable for geological change prediction, such as time series analysis, random forests, and support vector machines. Based on the characteristics of the geological data and the prediction objectives, we determine the model's input variables (such as groundwater level, soil moisture content, and air temperature) and output variables (such as the trend of soil bearing capacity changes and the probability of foundation settlement). The dataset is then divided into a training set and a validation set, and the model is trained using the training set to identify the changing patterns of the geological data. During the training process, hyperparameter tuning and cross-validation are used to continuously improve the model's prediction accuracy and generalization capabilities, ensuring that the model can effectively cope with complex and changing geological conditions.

[0089] The selection of machine learning algorithms and the model training process are crucial. Different algorithms perform differently when processing nonlinear data, time series data, and high-dimensional data. By selecting the appropriate algorithm and continuously optimizing model parameters, the accuracy of geological change prediction models can be improved, enabling more accurate prediction of geological risks.

[0090] Embed the trained geological change prediction model into the building information model and integrate it with the two-way dynamic linkage mechanism of geological data to achieve real-time prediction and dynamic design adjustment;

[0091] After a predictive model with good performance is trained, it is embedded into the BIM model and integrated with a two-way dynamic linkage mechanism for geological data. The model can acquire real-time monitoring data from sensor equipment. This new data is fed into the predictive model to generate predictions of geological changes during the construction phase. For example, the model can predict the changing trend of groundwater levels over the next week or the changes in soil bearing capacity in a specific area. These predictions are automatically updated in the BIM model, helping designers and construction parties identify potential risks in advance and implement appropriate countermeasures in their design and construction plans.

[0092] Embedding geological change prediction models into BIM models and enabling dynamic linkage can provide BIM models with real-time prediction capabilities. Through this linkage mechanism, construction parties can dynamically adjust construction plans based on the latest geological change prediction results, effectively reducing construction risks.

[0093] By analyzing the prediction results, the building information model automatically triggers risk warnings and generates construction adjustment suggestions, guiding the construction party to respond to geological changes in a timely manner and reduce construction risks;

[0094] Based on the predictions generated by the geological change prediction model, the BIM model automatically analyzes potential risks and generates construction adjustment recommendations. If the model predicts that the groundwater level may rise to a dangerous level or that the soil bearing capacity may decrease, the system automatically triggers a risk warning, prompting construction personnel to take emergency measures. Simultaneously, the system combines the predictions with the construction progress to automatically optimize construction adjustment plans, such as adjusting the construction sequence, adding foundation reinforcement measures, or changing material selection to account for the impending geological changes. These adjustment recommendations are synchronized with the construction management system to guide construction personnel's actual operations.

[0095] Risk warnings and construction adjustment recommendations based on geological change predictions can help construction companies proactively prevent potential geological risks. By automatically generating warnings and adjustment plans, BIM models not only improve construction safety and intelligence, but also significantly reduce rework and economic losses caused by geological changes.

[0096] Based on the output of the geological change prediction model, construction risk warning data is generated, and the infrastructure design parameters in the building information model are dynamically adjusted through deep learning algorithms to ensure that the design plan adapts to geological changes;

[0097] Based on the output of the geological change prediction model, construction risk warning data is generated. The infrastructure design parameters in the building information model are dynamically adjusted using a deep learning algorithm to ensure that the design plan adapts to geological changes. The specific steps are as follows:

[0098] Key output data is extracted from the geological change prediction model, including groundwater level change trends, soil bearing capacity change rates, and foundation settlement rates. The construction risk warning value is calculated based on the obtained data. The calculation formula for the construction risk warning value is as follows:

[0099] , where It is the construction risk warning value, ranging from 0 to 1. The larger the value, the higher the risk. is the groundwater level change trend, which indicates the rate of change of groundwater level in the future. is the soil bearing capacity change rate, which indicates the change of soil bearing capacity over time. is the foundation settlement rate, which indicates the speed of foundation settlement. 、 as well as are weight coefficients, representing the changing trends of groundwater levels. , soil bearing capacity change rate and foundation settlement rate The impact weight on the construction risk warning value, Used to regulate groundwater level changes Contribution to risk value, Used to adjust the soil bearing capacity change rate Contribution to risk value, Used to adjust the foundation settlement rate Contribution to VaR;

[0100] This step comprehensively considers the impact of changes in groundwater levels, soil bearing capacity, and foundation settlement on construction risk. By assigning weights to different factors and adjusting the calculation method based on the specific geological characteristics of the project, the risk level is more accurately reflected. The resulting risk warning value will serve as an important basis for subsequent adjustments to the foundation structure design.

[0101] According to the construction risk warning value , calculate the adjustment coefficient of the foundation structure, which is used to dynamically adjust the structural design parameters in the BIM model. The adjustment coefficient is generated by a deep learning algorithm, taking into account the foundation type (such as shallow foundation, pile foundation) and material properties (such as concrete strength, steel density) of the construction area. The calculation expression is as follows:

[0102] , where is the foundation structure adjustment coefficient, ranging from 0 to 1, which is used to indicate the adjustment range of the foundation structure design parameters. is the nonlinear mapping function generated by the deep learning model, is the natural base, Is a weight parameter used to control the construction risk warning value Adjustment coefficient for infrastructure The impact strength, It is a bias parameter used to adjust the output range and curve shape of the function to ensure the adjustment of the basic structure adjustment coefficient of the model output Can more reasonably match actual construction needs;

[0103] Infrastructure adjustment factor It is used to dynamically adjust basic structural design parameters in the BIM model, such as foundation size, concrete grade, and reinforcement configuration. By introducing nonlinear function mapping, the change of adjustment coefficients is smoother and more reasonable, which can effectively avoid over-adjustment or under-adjustment of structural design caused by geological changes.

[0104] Using the basic structure adjustment factor Dynamically update the basic structural design parameters in the BIM model. The adjustment formula is as follows: , , , where is the adjusted foundation width, is the initial foundation width, is the adjusted concrete strength grade, is the initial concrete strength grade, is the adjusted steel bar diameter, is the initial bar diameter.

[0105] Through the above steps, the basic structure adjustment coefficient Dynamically linked to key structural parameters, the foundation design is automatically optimized. Foundation width and concrete strength are adjusted more widely to accommodate more significant geological variations, while rebar diameter is adjusted more narrowly to avoid excessive adjustments that could lead to material waste and increased construction complexity. These adjustments are synchronized with the BIM model in real time, ensuring the design continuously adapts to geological changes and improving construction safety and cost-effectiveness.

[0106] Through optimization algorithms, construction plans are adjusted in real time. Construction sequence, material selection, and mechanical equipment utilization strategies are dynamically optimized based on real-time geological changes and construction progress, improving construction efficiency and safety.

[0107] Through optimization algorithms, construction plans are adjusted in real time. Construction sequence, material selection, and mechanical equipment utilization strategies are dynamically optimized based on real-time geological changes and construction progress. The specific steps to improve construction efficiency and safety are as follows:

[0108] First, the real-time geological data and construction progress data collected by sensors are normalized so that data from different sources and units can be calculated in the same model. The construction environment parameters are generated based on the weather impact coefficient of the progress deviation in the current construction stage. The generation formula is as follows:

[0109] , where is the groundwater level change trend, which indicates the rate of change of groundwater level in the future. is the soil bearing capacity change rate, which indicates the change of soil bearing capacity over time. It is the soil moisture change, the real-time monitoring soil moisture change value, It is the weather influence coefficient, which is the parameter value set according to the real-time weather conditions. It is the construction progress deviation, the difference between the actual progress and the planned progress. It is a comprehensive parameter of the construction environment, reflecting the comprehensive impact of current geological conditions and construction progress;

[0110] Generate comprehensive construction environment parameters through comprehensive analysis of geological changes and construction progress , which is used in the subsequent steps for dynamic optimization calculation of construction sequence and equipment strategy.

[0111] Obtaining comprehensive parameters of the construction environment Finally, the construction sequence is dynamically adjusted using an optimization algorithm to ensure the safety and efficiency of the construction process. The construction sequence weight coefficient is introduced to dynamically adjust the priority of each task based on its sensitivity to geological changes and construction difficulty. The resource utilization coefficient is introduced to calculate the adjustment priority of each construction task. The calculation expression is as follows:

[0112] , where It is a construction task The adjustment priority, the larger the value, the higher the priority. It is a construction task The order weight coefficient represents the comprehensive weight of the task’s sensitivity to geological changes and construction difficulty. It is a construction task The utilization rate of required resources (manpower, equipment, materials, etc.). The larger the value, the more resources the task consumes and the more difficult it is to execute.

[0113] Calculate the adjustment priority of each construction task through the construction sequence dynamic adjustment model , the construction party can optimize the construction plan in real time according to the priority, ensuring the construction safety and resource utilization efficiency under the risk of geological changes.

[0114] After adjusting the construction sequence, according to the current construction environment parameters and adjust priorities of construction tasks , further optimize the use strategy of mechanical equipment and material selection scheme, introduce mechanical equipment efficiency coefficient and material adaptability coefficient, calculate the total cost-benefit ratio of each construction task, and select the optimal equipment and material scheme. The calculation expression is as follows:

[0115] , where It is a construction task The total cost-effectiveness ratio is: the larger the value, the higher the cost-effectiveness of the current equipment and material selection options. It is the material adaptability coefficient, which reflects the applicability of the material under the current geological conditions, such as water resistance, compressive strength, etc. It is the efficiency coefficient of mechanical equipment, reflecting the actual working efficiency of mechanical equipment under current geological conditions.

[0116] Through the calculation of the mechanical equipment and material selection optimization model, the construction party can select the equipment and material solutions that best suit the current environment based on real-time geological changes and construction task priorities, thereby reducing construction costs, improving construction efficiency, and effectively avoiding construction risks.

[0117] Apply intelligent decision-making algorithms to conduct multi-dimensional evaluations of construction adjustment plans, comprehensively analyzing safety, economy, and sustainability, and generating final construction adjustment recommendations. These recommendations are then synchronized to the construction management system to guide actual construction.

[0118] Applying intelligent decision-making algorithms, we conduct a multi-dimensional evaluation of construction adjustment plans, comprehensively analyzing safety, economy, and sustainability, and generate final construction adjustment recommendations. These recommendations are then synchronized to the construction management system to guide actual construction. The specific steps are as follows:

[0119] First, based on the output of the geological change prediction model and real-time monitoring data, the comprehensive risk index of the construction adjustment plan is calculated. The comprehensive risk index is used to quantify the potential risks of different construction adjustment plans and evaluate them from the perspectives of safety, economy, and environmental impact. The calculation formula of the risk index is as follows:

[0120] , where It is The weight coefficient of geological risk is It is The monitoring value of geological risk, It is The emergency response cost of each geological risk, It is The safety impact level of each geological risk, is the total number of geological risk types, It is The weight coefficient of the construction adjustment strategy, It is The implementation difficulty score of the construction adjustment strategy, It is The time cost of the construction adjustment strategy, It is Environmental friendliness scores of construction adjustment strategies, is the total number of construction adjustment strategies, is the comprehensive risk index;

[0121] This step quantifies the potential risk of the construction adjustment plan into a comparable index by comprehensively considering multiple geological risk factors and the feasibility of the construction strategy. The higher the value, the greater the risk of the solution and the more optimization is needed.

[0122] Obtaining a comprehensive risk index After that, the benefit trade-off value of the construction adjustment plan is further calculated to evaluate the overall benefit of the adjustment plan. A comprehensive trade-off is made from the dimensions of economic cost, construction efficiency, and sustainable development. The calculation expression of the benefit trade-off value is as follows:

[0123] , where It is The direct economic benefits of this construction adjustment strategy, It is The construction feasibility scores of the construction adjustment strategies are: It is Social benefit scores of construction adjustment strategies, is the total number of construction adjustment strategies, is the time cost weight coefficient, which indicates the weight of time cost in the evaluation of construction adjustment plan. is the labor cost weight coefficient, which indicates the weight of labor cost in the evaluation of construction adjustment plan. is the environmental cost weight coefficient, which indicates the weight of environmental cost in the evaluation of construction adjustment plan. 、 and The project management team will determine the actual situation. is the total expected time cost of the construction adjustment plan, which represents the total time required to implement the adjustment plan, including the time cost required for construction adjustment, material transportation, etc. is the total expected labor cost of the construction adjustment scenario, is the total environmental cost of the construction adjustment plan, which represents the environmental cost brought about by the construction adjustment plan, including waste disposal costs, pollution control costs, etc. It is the benefit trade-off value, which represents the comprehensive benefit value calculated after a multi-dimensional evaluation of the construction adjustment plan. It is used to determine whether the construction adjustment plan is reasonable, optimal, and worth implementing.

[0124] The formula maximizes the benefits by weighing direct economic benefits, construction feasibility and social benefits, and at the same time takes the comprehensive risk index into account. Take this into consideration to ensure the safety and sustainability of the solution. The higher it is, the greater the comprehensive benefits of the plan are and the more suitable it is for actual construction.

[0125] Implementation Method 1: The design and construction of municipal engineering projects often rely on geological survey data. However, traditional geological data is often static and collected once, lacking a dynamic update mechanism. This data cannot reflect geological changes during the construction process, which can easily lead to design deviations and construction risks. To address this issue, a two-way dynamic linkage method between the BIM model and geological data is adopted. This seamlessly integrates initial geological survey data with real-time monitoring data during the construction phase, ensuring that geological parameters in the BIM model can be updated in real time based on actual conditions.

[0126] The specific implementation steps are as follows:

[0127] At the project's initial stages, professional geological survey tools were used to collect initial geological data, including groundwater levels, soil types, soil bearing capacity, and rock formation structure. After cleaning and preprocessing, this data was entered into the BIM system in a standardized format, forming the initial geological foundation for the BIM model. Entering this initial data is the first step in dynamic linkage, providing a benchmark for BIM model design and ensuring that the foundation structure design is adapted to local geological conditions.

[0128] Various sensor devices, including groundwater level monitors, soil moisture sensors, and soil pressure sensors, are deployed at the construction site. These sensors, connected to the BIM system via IoT technology, collect real-time data on geological changes during construction and automatically upload it to the cloud or a local database. Real-time sensor data collection provides data support for the dynamic updating of the BIM model.

[0129] To achieve dynamic, two-way linkage between BIM models and geological data, a dedicated data interface must be developed. The core function of this data interface is to convert, verify, and cleanse the initial geological data and real-time monitoring data to ensure data accuracy and consistency. This data interface allows the BIM system to receive data input from various sources, enabling automated entry and updating of geological data.

[0130] When real-time monitoring data is fed into the BIM system, the model automatically updates geological parameters. For example, if the groundwater level rises to a dangerous threshold, the BIM model automatically adjusts the drainage system design; if the soil bearing capacity decreases, the model implements foundation reinforcement measures. This dynamic update mechanism ensures that construction plans are consistent with actual geological conditions, effectively avoiding design deviations and construction risks caused by geological changes.

[0131] The dynamic interaction of the BIM model not only includes the input of geological data but also feedback and adjustments from the construction site. When geological changes are detected that may affect construction safety, the BIM system will issue an early warning to the construction team and simultaneously send construction adjustment suggestions to the construction management system. For example, if the soil moisture in a certain section of roadbed is too high, the system will recommend that the construction team adjust the construction sequence and prioritize areas with stable geological conditions.

[0132] This dynamic linkage approach can significantly improve the design accuracy and construction safety of municipal projects. During project implementation, the BIM model is constantly updated with the actual geological conditions. Designers can optimize their designs based on real-time geological data, and contractors can adjust their construction plans based on the system's recommendations. This effectively reduces construction risks, minimizes rework and cost waste, and improves the sustainability and service life of the project.

[0133] Implementation Method 2: Geological changes are complex and random, and relying solely on real-time monitoring data often fails to fully grasp geological change trends. Therefore, a geological change prediction model is constructed using machine learning algorithms. By analyzing historical geological data and real-time monitoring data, future geological change trends can be predicted in advance, providing support for optimizing and adjusting construction plans.

[0134] The specific implementation steps are as follows:

[0135] First, initial geological survey data and real-time monitoring data from the construction phase are collected to form a multi-dimensional, long-term geological dataset. This data includes groundwater level fluctuations, changes in soil bearing capacity, and foundation settlement trends. To ensure high data quality, the data needs to be cleaned, denoised, and formatted to remove outliers and invalid data, ensuring that the data input into the model is representative and consistent.

[0136] Select an appropriate machine learning algorithm based on the characteristics of the geological data and the prediction objectives. Commonly used algorithms include time series analysis, random forests, support vector machines, and long short-term memory (LSTM) networks. Time series analysis is suitable for predicting groundwater level changes, random forests can identify factors affecting soil bearing capacity, and LSTMs are highly effective for processing continuous time series data.

[0137] The geological dataset is divided into a training set and a validation set. The model is trained using the training set to identify patterns of geological data change. During the training process, hyperparameter tuning and cross-validation are used to continuously optimize the model's prediction accuracy and generalization capabilities, ensuring that the model can effectively respond to geological changes in different regions and time periods.

[0138] The trained geological change prediction model is embedded in the BIM system and dynamically linked to real-time monitoring data. As sensors collect new data, the prediction model automatically analyzes it and predicts geological change trends over the next period of time. For example, the model can predict the magnitude of groundwater level rise and the rate of soil bearing capacity decline over the next week, providing construction teams with early predictions and decision-making support.

[0139] Using machine learning to build a geological change prediction model can help construction companies identify potential geological risks in advance and reduce the impact of sudden geological changes on construction safety. Furthermore, the automatic update and linkage mechanism of the prediction model gives the BIM system a higher level of intelligence and decision-making capabilities, thereby improving the overall controllability and construction efficiency of the project.

[0140] Implementation Method 3: During the actual construction of municipal projects, even with real-time monitoring and geological change prediction capabilities, construction plans still need to be dynamically adjusted and optimized to cope with changing geological conditions. To this end, optimization algorithms and intelligent decision-making systems are used to optimize and adjust each aspect of the construction plan and generate feasible construction adjustment recommendations.

[0141] The specific implementation steps are as follows:

[0142] Based on the actual needs of the construction plan, an appropriate optimization algorithm model is constructed. The goal of the optimization algorithm is to improve construction efficiency, reduce construction risks, and minimize construction costs. The algorithm model comprehensively considers multiple factors, including real-time monitoring data, geological change predictions, construction progress, material costs, and safety, to optimize construction sequencing, material selection, and machinery and equipment utilization strategies.

[0143] When the system detects changes in geological conditions, an optimization algorithm automatically calculates the optimal construction adjustment plan. For example, if soil bearing capacity decreases, the system may recommend replacing foundation reinforcement materials or adjusting the construction sequence to prioritize stable areas. These adjustment recommendations are synchronized to the construction management system in the form of a digital report to guide construction operations.

[0144] Once construction adjustment proposals are generated, the intelligent decision-making system conducts a multi-dimensional evaluation of the proposed solution, including safety, economic efficiency, sustainability, and other indicators. Based on the evaluation results, the system recommends the optimal solution and updates it to the BIM model in real time, ensuring that the construction party has quick access to the latest adjustment recommendations.

[0145] The application of optimization algorithms and intelligent decision-making systems can effectively reduce uncertainty during the construction process, improving construction safety and efficiency. Furthermore, the automated assessment and adjustment recommendations provided by intelligent decision-making systems can help construction parties quickly respond to geological changes, reduce construction delays and rework costs, and maximize the economic benefits of the project.

[0146] Through the two-way dynamic linkage between the BIM model and geological data and the application of geological change prediction models, the present invention enables municipal projects to grasp the changes in geological conditions in real time during the construction process and automatically adjust the design and construction plans accordingly. This dynamic data-driven approach means that the design plan no longer relies on static geological survey data, but is continuously updated based on real-time monitoring results to ensure that the design always matches the actual geological conditions. For example, when it is monitored that the groundwater level is rising or the soil bearing capacity is decreasing, the system can automatically update the drainage system and foundation reinforcement plan, thereby effectively preventing hidden dangers such as roadbed settlement, dislocation of underground pipelines, and tilting of buildings. This beneficial effect significantly improves the overall safety of municipal projects, reduces rework, accidents and economic losses caused by geological changes, and thus ensures the stability and controllability of the construction process.

[0147] The present invention combines the application of machine learning algorithms, optimization algorithms and intelligent decision-making systems. During the construction process of municipal projects, it can automatically identify geological change trends and generate targeted construction adjustment suggestions. This intelligent construction plan adjustment process enables the construction party to flexibly adjust the construction sequence, material selection and mechanical equipment usage strategy based on the real-time optimization suggestions provided by the system, thereby maximizing construction efficiency and reducing construction costs. At the same time, the system conducts a comprehensive multi-dimensional evaluation of the construction adjustment plan, including safety, economy and sustainability, to ensure that construction decisions are more scientific and reasonable. This intelligent optimization and decision-making mechanism not only improves the execution efficiency of the project, but also significantly reduces the uncontrollable factors caused by geological changes, providing an effective solution for the digital and intelligent transformation of municipal engineering construction.

[0148] The above formulas are all dimensionless and numerical calculations. The formulas are obtained by collecting a large amount of data and performing software simulation to obtain the most recent real situation. The preset parameters in the formulas are set by technicians in this field according to actual conditions.

[0149] The above description is merely illustrative of certain exemplary embodiments of the present invention. It goes without saying that those skilled in the art will be able to modify the described embodiments in various ways without departing from the spirit and scope of the present invention. Therefore, the above drawings and description are illustrative in nature and should not be construed as limiting the scope of protection of the claims.

[0150] It should be noted that, in this document, if there are relational terms such as first and second, etc., they are only used to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Moreover, the terms "comprises", "comprising" or any other variations thereof are intended to cover non-exclusive inclusion, so that a process, method, article or device that includes a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, article or device. In the absence of further restrictions, an element defined by the sentence "comprising a ..." does not exclude the presence of other identical elements in the process, method, article or device that includes the element.

[0151] It should be understood that in the various embodiments of the present application, the size of the serial numbers of the above-mentioned processes does not mean the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of the present application.

[0152] Those skilled in the art will appreciate that the units and algorithm steps of each example described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Professional and technical personnel can use different methods to implement the described functions for each specific application, but such implementation should not be considered beyond the scope of this application.

[0153] Those skilled in the art will clearly understand that, for the convenience and brevity of description, the specific working processes of the systems, devices and units described above can refer to the corresponding processes in the aforementioned method embodiments and will not be repeated here.

[0154] The units described as separate components may or may not be physically separate, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed across multiple network units. Some or all of these units may be selected to achieve the purpose of this embodiment according to actual needs.

[0155] In addition, each functional unit in each embodiment of the present application may be integrated into one processing unit, or each unit may exist physically separately, or two or more units may be integrated into one unit.

[0156] The above description is merely a specific embodiment of the present application, but the scope of protection of the present application is not limited thereto. Any changes or substitutions that can be easily conceived by a person skilled in the art within the technical scope disclosed in this application should be included in the scope of protection of this application. Therefore, the scope of protection of this application should be based on the scope of protection of the claims.

[0157] The above description is merely illustrative of certain exemplary embodiments of the present invention. It goes without saying that those skilled in the art will be able to modify the described embodiments in various ways without departing from the spirit and scope of the present invention. Therefore, the above drawings and description are illustrative in nature and should not be construed as limiting the scope of protection of the claims.

Claims

1. The integrated collaborative design method for municipal intelligent construction is characterized by: The following steps are involved: Obtain initial geological survey data for municipal projects and use sensor equipment to monitor dynamic changes in geological data during construction in real time; Input the initial geological survey data and real-time monitoring data into the building information model, and use the data interface to achieve two-way dynamic linkage between the building information model and geological information to ensure real-time update of geological data; Construct a geological change prediction model during the construction phase within the building information model. Using machine learning algorithms, we predict geological condition change trends based on historical geological data and real-time monitoring data, enabling early prediction of geological changes. Based on the output of the geological change prediction model, construction risk warning data is generated, and the infrastructure design parameters in the building information model are dynamically adjusted through deep learning algorithms to ensure that the design plan adapts to geological changes; Through optimization algorithms, construction plans are adjusted in real time. Construction sequence, material selection, and mechanical equipment utilization strategies are dynamically optimized based on real-time geological changes and construction progress, improving construction efficiency and safety. Apply intelligent decision-making algorithms to conduct multi-dimensional evaluations of construction adjustment plans, generate final construction adjustment recommendations, and synchronize them to the construction management system to guide actual construction; Apply intelligent decision-making algorithms to conduct a multi-dimensional evaluation of construction adjustment plans, generate final construction adjustment recommendations, and synchronize them to the construction management system to guide actual construction. The specific steps are as follows: First, based on the output of the geological change prediction model and real-time monitoring data, the comprehensive risk index of the construction adjustment plan is calculated. The comprehensive risk index is used to quantify the potential risks of different construction adjustment plans and evaluate them from the perspectives of safety, economy, and environmental impact. The calculation formula of the risk index is as follows: , where It is The weight coefficient of geological risk is It is The monitoring value of geological risk, It is The emergency response cost of each geological risk, It is The safety impact level of each geological risk, is the total number of geological risk types, It is The weight coefficient of the construction adjustment strategy, It is The implementation difficulty score of the construction adjustment strategy, It is The time cost of the construction adjustment strategy, It is Environmental friendliness scores of construction adjustment strategies, is the total number of construction adjustment strategies, is the comprehensive risk index; Obtaining a comprehensive risk index After that, the benefit trade-off value of the construction adjustment plan is further calculated to evaluate the overall benefit of the adjustment plan. The calculation expression of the benefit trade-off value is as follows: , where It is The direct economic benefits of this construction adjustment strategy, It is The construction feasibility scores of the construction adjustment strategies are: It is Social benefit scores of construction adjustment strategies, is the total number of construction adjustment strategies, is the time cost weight coefficient, is the labor cost weight coefficient, is the environmental cost weight coefficient, is the total expected time cost of the construction adjustment plan, is the total expected labor cost of the construction adjustment scenario, is the total environmental cost of the construction adjustment plan, is the benefit trade-off value.

2. The integrated collaborative design method for municipal intelligent construction according to claim 1 is characterized in that: The specific steps for obtaining initial geological survey data for municipal projects and using sensor equipment to monitor the dynamic changes of geological data during construction in real time are as follows: Develop geological survey plans to ensure that the collected data is comprehensive and accurate, laying the foundation for subsequent analysis; Deploy sensor equipment according to the plan to ensure accurate and reliable data collection at key geological feature points; Classify, clean and standardize the collected geological data for uploading to ensure data accuracy and facilitate subsequent model application; The Internet of Things enables dynamic monitoring and early warning of geological data, and timely identification of abnormal changes to reduce construction risks.

3. The integrated collaborative design method for municipal intelligent construction according to claim 1 is characterized in that: The specific steps for inputting the initial geological survey data and real-time monitoring data into the building information model and realizing the two-way dynamic linkage between the building information model and geological information with the help of the data interface to ensure the real-time update of geological data are as follows: Develop data interfaces to implement conversion, verification, and filtering of geological data formats to ensure that initial data and real-time monitoring data are accurately received by the building information model; Map geological data to corresponding locations and parameters in the building information model according to spatial coordinates to ensure that geological change information is intuitively presented in the design model; Through a two-way dynamic linkage mechanism, geological data is updated in real time and the design parameters of the building information model are automatically adjusted to cope with changes in geological conditions; Establish a geological data anomaly early warning system, combine building information models and historical data, automatically identify risks and provide construction adjustment suggestions, and improve the safety and intelligence level of construction.

4. The integrated collaborative design method for municipal intelligent construction according to claim 1 is characterized in that: The geological change prediction model for the construction phase is constructed in the building information model. Using machine learning algorithms, the geological condition change trend is predicted based on historical geological data and real-time monitoring data. The specific steps for forming an early prediction of geological changes are as follows: Integrate initial geological data and real-time monitoring data, clean outliers, and build high-quality datasets to provide a reliable training foundation for geological change prediction models; Select machine learning algorithms based on geological data characteristics, optimize model performance through hyperparameter tuning and validation sets, and improve the accuracy and generalization ability of geological change predictions; Embed the trained geological change prediction model into the building information model and integrate it with the two-way dynamic linkage mechanism of geological data to achieve real-time prediction and dynamic design adjustment; By analyzing the prediction results, the building information model automatically triggers risk warnings and generates construction adjustment suggestions to guide the construction party to respond to geological changes in a timely manner and reduce construction risks.

5. The integrated collaborative design method for municipal intelligent construction according to claim 1 is characterized in that: Based on the output of the geological change prediction model, construction risk warning data is generated. The infrastructure design parameters in the building information model are dynamically adjusted using a deep learning algorithm to ensure that the design plan adapts to geological changes. The specific steps are as follows: Extract key output data from geological change prediction models, including groundwater level trends and foundation settlement rate, and the construction risk warning value is calculated comprehensively through the acquired data. The calculation formula of the construction risk warning value is as follows: , where is the construction risk warning value, is the trend of groundwater level change, yes , is the ground settlement rate, 、 as well as are weight coefficients, representing the changing trends of groundwater levels. , soil bearing capacity change rate and foundation settlement rate The weight of influence on the construction risk warning value; According to the construction risk warning value , calculate the adjustment coefficient of the foundation structure, which is used to dynamically adjust the structural design parameters in the BIM model. The calculation expression is as follows: , where is the basic structure adjustment coefficient, is the nonlinear mapping function generated by the deep learning model, is the natural base, is the weight parameter, is the bias parameter; Using the basic structure adjustment factor Dynamically update the basic structural design parameters in the BIM model. The adjustment formula is as follows: , , , where is the adjusted foundation width, is the initial foundation width, is the adjusted concrete strength grade, is the initial concrete strength grade, is the adjusted steel bar diameter, is the initial bar diameter.

6. The integrated collaborative design method for municipal intelligent construction according to claim 1 is characterized in that: Through optimization algorithms, construction plans are adjusted in real time. Construction sequence, material selection, and mechanical equipment utilization strategies are dynamically optimized based on real-time geological changes and construction progress. The specific steps to improve construction efficiency and safety are as follows: First, the real-time geological data and construction progress data collected by sensors are normalized so that data from different sources and units can be calculated in the same model. The construction environment parameters are generated based on the weather impact coefficient of the progress deviation in the current construction stage. The generation formula is as follows: , where is the trend of groundwater level change, yes , is the change in soil moisture, is the weather influence coefficient, It is the construction progress deviation. It is a comprehensive parameter of the construction environment; Obtaining comprehensive parameters of the construction environment Finally, the construction sequence is dynamically adjusted using an optimization algorithm to ensure the safety and efficiency of the construction process. The construction sequence weight coefficient is introduced to dynamically adjust the priority of each task based on its sensitivity to geological changes and construction difficulty. The resource utilization coefficient is introduced to calculate the adjustment priority of each construction task. The calculation expression is as follows: , where It is a construction task Adjustment priority, It is a construction task The order weight coefficient of It is a construction task Utilization of required resources; After adjusting the construction sequence, according to the current construction environment parameters and adjust priorities of construction tasks , further optimize the use strategy of mechanical equipment and material selection scheme, introduce mechanical equipment efficiency coefficient and material adaptability coefficient, calculate the total cost-benefit ratio of each construction task, and select the optimal equipment and material scheme. The calculation expression is as follows: , where It is a construction task The overall cost-effectiveness ratio, is the material adaptability coefficient, is the efficiency coefficient of mechanical equipment.

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