Low-carbon reinforcement method and system for masonry structure based on recycled micro-powder modified mortar

Abstract

This application relates to a low-carbon reinforcement method and system for masonry structures based on recycled micro-powder modified mortar, and pertains to the field of building structure reinforcement technology. The method includes the following steps: Step 1, assessing the current condition of the target masonry structure to determine the specific parts requiring reinforcement, the current degree of damage, and the required strength level after reinforcement. Simultaneously, a stress sensor array is deployed on the surface of the masonry to be reinforced to collect real-time stress change data during the assessment and subsequent construction processes. At the material level, this application achieves high-value-added resource utilization of construction waste, significantly reducing cement usage and carbon emissions at the source, demonstrating significant advantages of green and low-carbon practices. At the technical level, through dynamic design based on machine learning models, it changes the traditional reinforcement model that relies on static and conservative calculations, achieving precise and optimized reinforcement schemes and avoiding material waste.

Description

Technical Field

[0001] This application relates to the field of building structure reinforcement technology, and in particular to a low-carbon reinforcement method and system for masonry structures based on recycled micro-powder modified mortar. Background Technology

[0002] In the current environment, there are a large number of existing masonry structures that need to be reinforced and repaired. Traditional reinforcement methods often rely on ordinary cement-based materials with high cement consumption and carbon emissions, which is not in line with the green and low-carbon development direction of the construction industry. At the same time, traditional reinforcement processes rely too much on manual experience, resulting in problems such as conservative design, large material waste, and high fluctuations in construction quality. It is difficult to scientifically guarantee the uniformity and reliability of the final reinforcement effect. In addition, the resource utilization of solid waste such as construction waste brick powder is limited and has low added value. Summary of the Invention

[0003] To address the aforementioned issues, this application provides a low-carbon reinforcement method and system for masonry structures based on recycled micro-powder modified mortar.

[0004] The low-carbon reinforcement method and system for masonry structures based on recycled micro-powder modified mortar provided in this application adopts the following technical solution: A low-carbon reinforcement method for masonry structures based on recycled micro-powder modified mortar includes the following steps: Step 1: Assess the current condition of the target masonry structure to determine the specific parts that need reinforcement, the current degree of damage, and the required strength level after reinforcement. At the same time, deploy a stress sensor array on the surface of the masonry to be reinforced to collect stress change data of the structure in real time during the assessment and subsequent construction process. Step 2: Based on the strength requirements determined in Step 1, prepare a special recycled micro-powder modified mortar. The components and mass ratio of the recycled micro-powder modified mortar include: cement accounting for 30%-50% of the total mass of cementitious materials, recycled brick powder accounting for 20%-40% of the total mass of cementitious materials, additives accounting for 0.5%-2.0% of the total mass of cementitious materials, and the water-cement ratio controlled between 0.4 and 0.6. The recycled brick powder is obtained by crushing and screening waste building bricks, and its particle size is less than 0.15 mm, so as to make full use of its micro-aggregate filling effect and potential pozzolanic activity, and ensure chemical compatibility and physical bond strength with old masonry materials. Step 3: Input the stress data collected in real time by the stress sensor array in Step 1 into a pre-set bearing capacity assessment model. This model is trained on historical reinforcement data based on machine learning algorithms. It can dynamically predict the weak points of the structure and calculate the optimal reinforcement thickness based on real-time stress data. Based on the output of the model, determine whether to use single-sided or double-sided steel mesh recycled mortar plastering reinforcement method, and generate a construction plan containing specific reinforcement thickness parameters.

[0005] As a preferred technical solution of this application, it also includes: Step four: Roughen the original masonry surface to expose the fresh base layer, and clean it with high-pressure air or water to ensure that the surface is free of dust and oil stains in order to improve the interface adhesion. Then, lay the steel mesh according to the plan determined in step three. The mesh size of the steel mesh is 50mm×50mm to 100mm×100mm, and the diameter of the steel bars is 4mm-6mm. Finally, apply the recycled micro powder modified mortar prepared in step two in layers. The thickness of each layer is controlled at 15mm-25mm, and the total thickness is precisely controlled within the range of 30mm-50mm according to the dynamic design results. Step 5: During the troweling process, humidity and temperature sensors are embedded in the mortar layer. After construction is completed, the automatic spraying device is activated. The central processing unit dynamically adjusts the spraying frequency and water volume based on the real-time data fed back by the humidity and temperature sensors, maintaining the relative humidity of the curing environment above 90% and the temperature between 15℃ and 30℃. The curing time is no less than 14 days to ensure that the strength of the recycled micro-powder modified mortar is fully developed.

[0006] As a preferred technical solution of this application, the bearing capacity assessment model in step three is a machine learning model trained based on the gradient boosting decision tree algorithm. Its input features include at least: the real-time stress value and its distribution gradient collected by the stress sensor array, the initial strength estimate of the masonry material, the 28-day design compressive strength of the recycled micro-powder modified mortar and the reinforcement ratio of the steel mesh. The output of the model is the minimum reinforcement layer thickness required to meet the target safety factor and the optimal steel mesh configuration suggestion.

[0007] A low-carbon reinforcement system for masonry structures based on recycled micro-powder modified mortar includes: The sensing module includes a stress sensor array deployed on the surface of the masonry to be reinforced, as well as a humidity sensor and a temperature sensor embedded in the mortar layer, for real-time acquisition of structural stress state and curing environment parameters. The decision and control center includes a central processing unit and a material delivery control unit and a maintenance control unit connected thereto. The central processing unit has a pre-stored load-bearing capacity assessment model, which is used to receive data from the sensing module and dynamically calculate the required reinforcement thickness and maintenance strategy. The material delivery control unit controls the feeding and discharging of each component of the mortar mixing plant according to the instructions of the central processing unit. The maintenance control unit controls the start and stop of the automatic spraying device according to the instructions of the central processing unit. The execution module includes a mortar mixing station controlled by the material delivery control unit and an automatic spraying device controlled by the curing control unit, for specifically performing precise delivery and curing operations of mortar.

[0008] As a preferred technical solution of this application, both the sensing module and the execution module are communicatively connected to the decision and control center, and the output end of the decision and control center is also communicatively connected to a low-carbon energy supply module.

[0009] As a preferred technical solution of this application, the low-carbon energy supply module includes a solar photovoltaic panel array, a battery, and an energy management module. The energy management module is connected to the solar photovoltaic panel array, the battery, and the mains power grid, and prioritizes the dispatch of solar power to supply power to the execution module, the sensing module, and the decision and control center.

[0010] As a preferred technical solution of this application, the central processing unit in the decision and control center is further configured to continuously receive data from the stress sensor array after the reinforcement construction is completed, and to conduct long-term monitoring and evaluation of the reinforcement effect.

[0011] As a preferred technical solution of this application, when monitoring data indicates that the structural performance has deteriorated or exceeded a preset safety threshold, the central processing unit can generate early warning information and prompt maintenance.

[0012] In summary, this application includes at least the following beneficial technical effects of the low-carbon reinforcement method and system for masonry structures based on recycled micro-powder modified mortar: At the material level, this application achieves high-value-added resource utilization of construction waste, significantly reducing cement usage and carbon emissions at the source, demonstrating the significant advantages of green and low-carbon practices. At the technical level, through dynamic design based on machine learning models, it changes the traditional reinforcement model that relies on static and conservative calculations, achieving precision and optimization of reinforcement schemes and avoiding material waste. At the same time, by utilizing sensor networks and automatic control systems, it ensures the controllability and consistency of construction quality, greatly improving the reliability of reinforcement effects. In terms of overall efficiency, this system organically combines material innovation, intelligent decision-making, and automated execution, not only comprehensively improving the strength, durability, and safety of masonry structure reinforcement, but also forming a replicable and scalable new model of intelligent construction and low-carbon reinforcement. Attached Figure Description

[0013] Figure 1 This is a flowchart of the low-carbon reinforcement method for masonry structures in this application; Figure 2 This is the architecture diagram of the low-carbon reinforcement system for masonry structures in this application. Detailed Implementation

[0014] The following is in conjunction with the appendix Figure 1-2 This application will be described in further detail.

[0015] See Figure 1-2A low-carbon reinforcement method for masonry structures based on recycled micro-powder modified mortar includes the following steps: Step 1: Assess the current condition of the target masonry structure to determine the specific parts that need reinforcement, the current degree of damage, and the required strength level after reinforcement. At the same time, deploy a stress sensor array on the surface of the masonry to be reinforced to collect stress change data of the structure in real time during the assessment and subsequent construction process. Step 2: Based on the strength requirements determined in Step 1, prepare a special recycled micro-powder modified mortar. The components and mass ratio of the recycled micro-powder modified mortar include: cement accounting for 30%-50% of the total mass of cementitious materials, recycled brick powder accounting for 20%-40% of the total mass of cementitious materials, additives accounting for 0.5%-2.0% of the total mass of cementitious materials, and the water-cement ratio controlled between 0.4 and 0.6. Among them, the recycled brick powder is obtained by crushing and screening waste building bricks, and its particle size is less than 0.15 mm, so as to make full use of its micro-aggregate filling effect and potential pozzolanic activity, and ensure chemical compatibility and physical bond strength with old masonry materials. Step 3: Input the stress data collected in real time by the stress sensor array in Step 1 into a pre-set bearing capacity assessment model. This model is trained on historical reinforcement data based on machine learning algorithms. It can dynamically predict the weak points of the structure and calculate the optimal reinforcement thickness based on real-time stress data. Based on the output of the model, determine whether to use single-sided or double-sided steel mesh recycled mortar plastering reinforcement method, and generate a construction plan containing specific reinforcement thickness parameters.

[0016] Step four: Roughen the original masonry surface to expose the fresh base layer, and clean it with high-pressure air or water to ensure that the surface is free of dust and oil stains in order to improve the interface adhesion. Then, lay the steel mesh according to the plan determined in step three. The mesh size of the steel mesh is 50mm×50mm to 100mm×100mm, and the diameter of the steel bars is 4mm-6mm. Finally, apply the recycled micro powder modified mortar prepared in step two in layers. The thickness of each layer is controlled at 15mm-25mm, and the total thickness is precisely controlled within the range of 30mm-50mm according to the dynamic design results. Step 5: During the troweling process, embed humidity and temperature sensors into the mortar layer. After construction is completed, start the automatic spraying device. The central processor dynamically adjusts the spraying frequency and water volume based on the real-time data from the humidity and temperature sensors to maintain the relative humidity of the curing environment above 90% and the temperature between 15℃ and 30℃. The curing time is no less than 14 days to ensure that the strength of the recycled micro-powder modified mortar is fully developed.

[0017] The bearing capacity assessment model in step three is a machine learning model trained based on the gradient boosting decision tree algorithm. Its input features include at least: real-time stress values ​​and their distribution gradients collected by the stress sensor array, the initial strength estimate of the masonry material, the 28-day design compressive strength of the recycled micro-powder modified mortar, and the reinforcement ratio of the steel mesh. The output of the model is the minimum reinforcement layer thickness required to meet the target safety factor and the optimal steel mesh configuration suggestion.

[0018] This application first conducts a structural condition assessment, determining the current strength grade and damage distribution of the masonry using non-destructive testing techniques. Simultaneously, a stress sensor array is deployed on the surface to be reinforced to establish a monitoring network. Subsequently, recycled micro-powder modified mortar is prepared according to strength enhancement requirements. In its cementitious material system, cement accounts for 30%-50%, recycled brick powder accounts for 20%-40%, additives account for 0.5%-2.0% of the total cementitious material mass, and the water-cement ratio is strictly controlled between 0.4 and 0.6. In key decision-making stages, real-time stress data is input into a pre-trained gradient boosting decision tree model. This model analyzes stress distribution gradients and material parameters to dynamically output the optimal reinforcement thickness and reinforcement scheme. During construction, the base layer is roughened, and a steel mesh with dimensions ranging from 50mm×50mm to 100mm×100mm is laid. Recycled micro-powder modified mortar is then applied in layers, each layer 15mm-25mm thick, with the total thickness precisely controlled within the range of 30mm-50mm. During the curing phase, embedded sensors monitor temperature and humidity in real time, and an automatic sprinkler system maintains a relative humidity of over 90% and an ambient temperature of 15℃-30℃. The curing period is no less than 14 days. The entire process forms a closed-loop control system encompassing assessment, decision-making, construction, and curing.

[0019] A low-carbon reinforcement system for masonry structures based on recycled micro-powder modified mortar includes: The sensing module includes an array of stress sensors deployed on the surface of the masonry to be reinforced, as well as humidity and temperature sensors embedded in the mortar layer, for real-time acquisition of structural stress state and curing environment parameters. The sensing module consists of a stress monitoring unit and an environmental monitoring unit, forming a complete data acquisition system. The stress monitoring unit uses a dense array of resistance strain gauge sensors, fixed to the masonry surface in a grid-like distribution, to collect stress and strain data of the structure in real time throughout the construction process. The environmental monitoring unit includes an embedded temperature and humidity composite sensor. All sensors are connected to the data acquisition instrument via shielded twisted-pair cables. The sampling frequency can be set to an adjustable 1Hz-10Hz. The collected data is transmitted to the decision control center in real time via an RS485 bus. This module has an IP67 protection rating and can operate stably in harsh construction environments.

[0020] The decision-making and control center comprises a central processing unit (CPU) and connected material delivery and maintenance control units. The CPU pre-stores a load-bearing capacity assessment model, receiving data from the sensing modules and dynamically calculating the required reinforcement thickness and maintenance strategy. The material delivery control unit, according to instructions from the CPU, controls the input and output of each component of the mortar mixing plant. The maintenance control unit, also according to instructions from the CPU, controls the start and stop of the automatic spraying device. The CPU is further configured to continuously receive data from the stress sensor array after reinforcement construction, performing long-term monitoring and evaluation of the reinforcement effect. When monitoring data indicates that structural performance has deteriorated or exceeded a preset safety threshold, the CPU can generate an early warning message and prompt maintenance. The load-bearing capacity assessment model of the decision-making and control center operates as follows: The model built into the central processing unit (CPU) is a serialized binary file, loaded into memory upon system startup. When real-time data from the sensing modules is transmitted to the CPU via the bus, the built-in data preprocessing program automatically cleans, standardizes, and extracts features from the data, forming feature vectors that meet the model's input requirements. These feature vectors are then fed into the loaded GBDT model for forward inference calculations. The model consists of multiple decision trees; the input data is processed sequentially through each tree, and the outputs of all trees are weighted and summed to generate the predicted thickness value. The entire inference process is completed within seconds, enabling real-time dynamic design of the reinforcement scheme.

[0021] The decision-making and control center adopts an industrial-grade embedded computer platform, equipped with a multi-core processor and a real-time operating system. Its core load-bearing capacity assessment model is built based on the gradient boosting decision tree algorithm. The input feature dimensions include real-time stress values ​​and their spatial distribution gradient, initial strength of masonry, 28-day design strength of mortar, and reinforcement ratio. The model output is the minimum reinforcement layer thickness and optimized configuration scheme of steel mesh that meet the safety factor requirements. The material delivery control unit precisely adjusts the feeding ratio of cement, recycled powder and additives through the PID algorithm. The curing control unit dynamically generates the spraying strategy based on environmental monitoring data. When the humidity is below the 90% threshold, the spraying command is automatically triggered to achieve a control accuracy of ±5%. All control commands are transmitted to the execution equipment through industrial Ethernet to form a complete closed-loop control system.

[0022] The execution module includes a mortar mixing plant controlled by the material delivery control unit and an automatic spraying device controlled by the curing control unit, which are used to carry out precise delivery and curing operations of mortar.

[0023] The execution module consists of a material delivery system and an intelligent maintenance system. The material delivery system includes a forced mixer and a precision metering device. The feeding system uses an electronic weighing module. The intelligent maintenance system includes a solenoid valve group and a rotary nozzle array. The module adopts a modular design, and all actuators have manual / automatic dual-mode switching functions to ensure reliable system operation.

[0024] Both the sensing module and the execution module are communicatively connected to the decision-making and control center, and the output of the decision-making and control center is also communicatively connected to the low-carbon energy supply module.

[0025] The low-carbon energy supply module includes a solar photovoltaic panel array, a battery, and an energy management module. The energy management module is connected to the solar photovoltaic panel array, the battery, and the mains power grid, and prioritizes the dispatch of solar power to supply the execution module, the sensing module, and the decision and control center.

[0026] At the material level, this application achieves high-value-added resource utilization of construction waste, significantly reducing cement usage and carbon emissions at the source, demonstrating the significant advantages of green and low-carbon practices. At the technical level, through dynamic design based on machine learning models, it changes the traditional reinforcement model that relies on static and conservative calculations, achieving precision and optimization of reinforcement schemes and avoiding material waste. At the same time, by utilizing sensor networks and automatic control systems, it ensures the controllability and consistency of construction quality, greatly improving the reliability of reinforcement effects. In terms of overall efficiency, this system organically combines material innovation, intelligent decision-making, and automated execution, not only comprehensively improving the strength, durability, and safety of masonry structure reinforcement, but also forming a replicable and scalable new model of intelligent construction and low-carbon reinforcement.

[0027] The above are all preferred embodiments of this application, and are not intended to limit the scope of protection of this application. Therefore, all equivalent changes made in accordance with the structure, shape and principle of this application should be covered within the scope of protection of this application.

Claims

1. A low-carbon reinforcement method for masonry structures based on recycled micro-powder modified mortar, characterized in that, Includes the following steps: Step 1: Assess the current condition of the target masonry structure to determine the specific parts that need reinforcement, the current degree of damage, and the required strength level after reinforcement. At the same time, deploy a stress sensor array on the surface of the masonry to be reinforced to collect stress change data of the structure in real time during the assessment and subsequent construction process. Step 2: Based on the strength requirements determined in Step 1, prepare a special recycled micro-powder modified mortar. The components and mass ratio of the recycled micro-powder modified mortar include: cement accounting for 30%-50% of the total mass of cementitious materials, recycled brick powder accounting for 20%-40% of the total mass of cementitious materials, additives accounting for 0.5%-2.0% of the total mass of cementitious materials, and the water-cement ratio controlled between 0.4 and 0.

6. The recycled brick powder is obtained by crushing and screening waste building bricks, and its particle size is less than 0.15 mm, so as to make full use of its micro-aggregate filling effect and potential pozzolanic activity, and ensure chemical compatibility and physical bond strength with old masonry materials. Step 3: Input the stress data collected in real time by the stress sensor array in Step 1 into a pre-set bearing capacity assessment model. This model is trained on historical reinforcement data based on machine learning algorithms. It can dynamically predict the weak points of the structure and calculate the optimal reinforcement thickness based on real-time stress data. Based on the output of the model, determine whether to use single-sided or double-sided steel mesh recycled mortar plastering reinforcement method, and generate a construction plan containing specific reinforcement thickness parameters.

2. The low-carbon reinforcement method for masonry structures based on recycled micro-powder modified mortar according to claim 1, characterized in that, Also includes: Step four: Roughen the original masonry surface to expose the fresh base layer, and clean it with high-pressure air or water to ensure that the surface is free of dust and oil stains in order to improve the interface adhesion. Then, lay the steel mesh according to the plan determined in step three. The mesh size of the steel mesh is 50mm×50mm to 100mm×100mm, and the diameter of the steel bars is 4mm-6mm. Finally, apply the recycled micro powder modified mortar prepared in step two in layers. The thickness of each layer is controlled at 15mm-25mm, and the total thickness is precisely controlled within the range of 30mm-50mm according to the dynamic design results. Step 5: During the troweling process, humidity and temperature sensors are embedded in the mortar layer. After construction is completed, the automatic spraying device is activated. The central processing unit dynamically adjusts the spraying frequency and water volume based on the real-time data fed back by the humidity and temperature sensors, maintaining the relative humidity of the curing environment above 90% and the temperature between 15℃ and 30℃. The curing time is no less than 14 days to ensure that the strength of the recycled micro-powder modified mortar is fully developed.

3. The low-carbon reinforcement method for masonry structures based on recycled micro-powder modified mortar according to claim 1, characterized in that, The bearing capacity assessment model described in step three is a machine learning model trained based on the gradient boosting decision tree algorithm. Its input features include at least: the real-time stress values ​​and their distribution gradients collected by the stress sensor array, the initial strength estimate of the masonry material, the 28-day design compressive strength of the recycled micro-powder modified mortar, and the reinforcement ratio of the steel mesh. The output of the model is the minimum reinforcement layer thickness required to meet the target safety factor and the optimal steel mesh configuration suggestion.

4. A low-carbon reinforcement system for masonry structures based on recycled micro-powder modified mortar, comprising any one of the low-carbon reinforcement methods for masonry structures based on recycled micro-powder modified mortar according to claims 1-3, characterized in that, include: The sensing module includes a stress sensor array deployed on the surface of the masonry to be reinforced, as well as a humidity sensor and a temperature sensor embedded in the mortar layer, for real-time acquisition of structural stress state and curing environment parameters. The decision and control center includes a central processing unit and a material delivery control unit and a maintenance control unit connected thereto. The central processing unit has a pre-stored load-bearing capacity assessment model, which is used to receive data from the sensing module and dynamically calculate the required reinforcement thickness and maintenance strategy. The material delivery control unit controls the feeding and discharging of each component of the mortar mixing plant according to the instructions of the central processing unit. The maintenance control unit controls the start and stop of the automatic spraying device according to the instructions of the central processing unit. The execution module includes a mortar mixing station controlled by the material delivery control unit and an automatic spraying device controlled by the curing control unit, for specifically performing precise delivery and curing operations of mortar.

5. The low-carbon reinforcement system for masonry structures based on recycled micro-powder modified mortar according to claim 4, characterized in that, Both the sensing module and the execution module are communicatively connected to the decision-making and control center, and the output of the decision-making and control center is also communicatively connected to a low-carbon energy supply module.

6. The low-carbon reinforcement system for masonry structures based on recycled micro-powder modified mortar according to claim 5, characterized in that, The low-carbon energy supply module includes a solar photovoltaic panel array, a battery, and an energy management module. The energy management module is connected to the solar photovoltaic panel array, the battery, and the mains power grid, and prioritizes the dispatch of solar power to supply power to the execution module, the sensing module, and the decision and control center.

7. The low-carbon reinforcement system for masonry structures based on recycled micro-powder modified mortar according to claim 4, characterized in that, The central processor in the decision-making and control hub is further configured to continuously receive data from the stress sensor array after the reinforcement construction is completed, and to conduct long-term monitoring and evaluation of the reinforcement effect.

8. The low-carbon reinforcement system for masonry structures based on recycled micro-powder modified mortar according to claim 7, characterized in that, When monitoring data indicates that structural performance has deteriorated or exceeded a preset safety threshold, the central processing unit can generate an early warning message and prompt maintenance.

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