A Real-Time Evaluation Method for Construction Efficiency of BIM-Based Hollow Core Slab Structure System

Abstract

This invention discloses a real-time evaluation method for the construction efficiency of a BIM-based hollow core slab structure system, belonging to the field of building construction technology. The invention first constructs a process-level efficiency benchmark model for the hollow core slab, incorporating energy-saving building material construction parameters and setting a dynamic threshold mechanism. It relies on multiple sensors to collect multi-source heterogeneous data from the construction site, and uses data fusion and inference to back-calculate the construction efficiency of explicit and implicit processes and identify deviations. It intelligently matches the root causes of efficiency bottlenecks by calling a pre-set process mechanism rule library, automatically generating an optimization evaluation report adapted to the construction characteristics. Simultaneously, it establishes an optimization effect feedback and iteration mechanism, and links it with energy-saving building material control to achieve dynamic matching. This invention overcomes the technical shortcomings of traditional manual evaluation and conventional BIM applications, realizing real-time monitoring and intelligent optimization of hollow core slab construction efficiency, and has strong engineering application value.

Description

Technical Field

[0001] This invention relates to the field of building construction technology, specifically to a method for real-time evaluation of construction efficiency of a BIM-based hollow core slab structure system. Background Technology

[0002] With the widespread adoption of hollow core slab structures in high-rise residential buildings and commercial complexes, hollow core slab construction involves multiple interconnected processes, including formwork, anti-buoyancy reinforcement, rebar tying, and energy-saving core mold installation. Numerous hidden construction operations exist, and the construction process extensively utilizes lightweight fly ash products and energy-saving blocks. The complex procedures and the significant impact of material properties on construction pace make this a challenging process. Currently, the industry still relies on the traditional model of manual on-site recording and post-construction statistical analysis for hollow core slab construction efficiency management. This approach not only suffers from delayed data collection and significant subjective errors but also fails to achieve real-time monitoring and dynamic evaluation of the construction process.

[0003] While existing BIM technology can achieve 3D modeling and progress simulation of hollow core slabs, it only reaches the visualization level. It does not establish refined efficiency evaluation benchmarks for specific processes of hollow core slabs, nor can it adapt to the construction characteristics of energy-saving building materials. At the same time, for hidden processes such as anti-buoyancy reinforcement of the box slab and the coordination of rib beam reinforcement and core mold, conventional monitoring methods have monitoring blind spots, making it difficult to quantify efficiency. Construction efficiency bottlenecks can only be identified retrospectively based on the experience of management personnel. There is a lack of integrated technical solutions for real-time perception, automatic traceability, and intelligent optimization. Overall, hollow core slab construction faces core technical challenges such as the inability to achieve real-time and accurate efficiency evaluation of all processes, difficulty in quantifying and monitoring hidden processes, and the inability to intelligently trace bottlenecks.

[0004] In view of the above, this application is hereby submitted. Summary of the Invention

[0005] The purpose of this invention is to provide a method for real-time evaluation of construction efficiency of BIM-based hollow core slab structure systems, in order to solve the problems mentioned in the background art.

[0006] To address the aforementioned technical problems, this invention provides a real-time evaluation method for the construction efficiency of a BIM-based hollow core slab structure system, comprising the following steps: S1. Constructing a process-level efficiency benchmark model for hollow core slabs oriented towards energy-saving building materials. The hollow core slab construction process is broken down into formwork erection, anti-buoyancy reinforcement, rebar tying, energy-saving core mold installation, and concrete pouring. The standard efficiency thresholds for each process, as well as the installation time parameters and loss coefficient parameters of energy-saving building materials, are entered into the BIM system to establish a process-level efficiency benchmark model; S2. Real-time back-calculation of construction efficiency through multi-source heterogeneous sensor data fusion. The actual construction parameters of each process are collected in real time through visual sensors, mechanical operation parameter sensors, laser rangefinders, and personnel positioning sensors. The collected multi-source heterogeneous data are fused and calculated to optimize the anti-buoyancy reinforcement process of the hollow core slab and the ribs. For concealed processes involving the reinforcement of beams and the core mold that cannot be directly monitored visually, the actual construction efficiency is indirectly calculated through correlation analysis of multi-source data. The calculated actual construction efficiency is then dynamically compared with the standard efficiency threshold in real time to calculate the real-time efficiency deviation of each process. S3 calls the process mechanism rule library to trace bottlenecks and generate optimization strategies. When the real-time efficiency deviation exceeds the preset range, it calls the hollow core slab process mechanism rule library pre-installed in the BIM system, matches the real-time collected data with the rule library, automatically locates the root cause of the bottleneck process, and generates an optimization evaluation report that includes process adjustment plans, loss control strategies, and schedule adaptation suggestions. This achieves real-time and accurate evaluation of the construction efficiency of hollow core slabs, and for the first time realizes indirect back-calculation of the efficiency of concealed processes, overcoming the lag and monitoring blind spots of traditional manual statistics, and improving construction efficiency and management level.

[0007] Furthermore, in S1, establishing a process-level efficiency benchmark model includes the following steps: S11 Energy-saving building materials include fly ash lightweight products, energy-saving blocks, and thermal insulation and energy-saving cement products, and the installation time and loss coefficient of each block of the above building materials are entered into the BIM system; S12 An initial standard efficiency threshold is set for each process, which includes standard working hours, standard labor consumption, and standard machinery consumption; S13 A dynamic threshold comparison mechanism is established, and the warning threshold of the current process is adaptively updated using the sliding window method based on the actual efficiency data of the completed processes, so that the threshold is dynamically adjusted with the construction progress; the dynamic threshold mechanism avoids misjudgment or omission caused by fixed thresholds, improves the adaptability of efficiency assessment, deeply integrates the construction characteristics of energy-saving building materials into the benchmark model, and strengthens the pertinence of assessment.

[0008] Furthermore, in S2, the multi-source heterogeneous sensor data fusion includes the following steps: S21, video data of the construction area is collected through a smart camera, and the effective operation duration is extracted and invalid dwell time is eliminated by combining the action recognition algorithm; S22, the actual running time of mechanical equipment is collected through the electrical parameter sensors and vibration sensors of tower cranes and concrete pump trucks, and the actual operation efficiency of hoisting and pouring processes is calculated; S23, the layout coverage of energy-saving core molds is monitored in real time through laser ranging sensors, and the progress completion rate of core mold installation process is calculated; S24, the dwell time of workers in the corresponding process area of ​​the BIM model is collected through an ultra-wideband personnel positioning system, and the labor efficiency of each process is calculated by combining the standard area division of each process; S25, the above multi-source data is input into the fusion calculation engine, and the efficiency of hidden processes is calculated by using a fuzzy inference model; the multi-source heterogeneous data fusion calculation improves the data accuracy and real-time performance of efficiency assessment, and the efficiency of hidden processes is calculated by using a fuzzy inference model, filling the gap that existing technologies cannot monitor the efficiency of hidden processes.

[0009] Furthermore, in S25, the efficiency of the concealed process is calculated by inputting the data on the consumption of anti-buoyancy wire in the anti-buoyancy reinforcement process of the box body, the displacement sensor data during subsequent concrete pouring, and the data on the duration of personnel positioning in the area into a fuzzy inference model, and outputting the completion efficiency index of the concealed process through a preset fuzzy rule set; the efficiency of the anti-buoyancy reinforcement process of the box body is indirectly quantitatively evaluated through multi-dimensional data association and fusion, overcoming the technical bias in the industry that concealed processes cannot be monitored in real time.

[0010] Furthermore, in S3, the hollow floor slab process mechanism rule library stores multiple sets of mapping relationships between bottleneck phenomena and root causes. Each set of mapping relationships includes a bottleneck phenomenon identifier, at least one possible root cause, and a corresponding optimization strategy. The optimization strategy is bound to the construction characteristics of energy-saving building materials. The hollow floor slab construction experience is systematically encoded, realizing the transformation of bottleneck tracing from manual experience judgment to data-driven automatic matching, thereby improving the accuracy and efficiency of bottleneck location.

[0011] Furthermore, in S3, the automatic identification of the root cause of the bottleneck process includes the following steps: S31, the identified real-time efficiency deviation data and the corresponding process identifier are input into the bottleneck tracing engine; S32, the tracing engine retrieves multi-source sensor data during the construction of the process to perform multi-factor correlation analysis, and the analysis factors include the number of personnel, machine running time, material supply timeliness, and environmental conditions; S33, the results of the multi-factor correlation analysis are matched with the mapping relationship in the process mechanism rule base, the matching degree of each possible root cause is calculated, and the root cause with the highest matching degree is selected as the location result; through the two-level tracing mechanism that combines multi-factor correlation analysis and rule base matching, the accuracy of bottleneck root cause location is improved, and misjudgment caused by a single data source is avoided.

[0012] Furthermore, in S3, the generated optimization evaluation report includes an efficiency deviation summary table, bottleneck process identification, description of the root cause of the problem, process adjustment plan for the construction characteristics of energy-saving building materials, loss control strategy, and schedule adaptation suggestions; forming a complete evaluation report that fits the construction characteristics of energy-saving building materials, providing a direct and actionable decision-making basis for construction management, and realizing closed-loop management from problem discovery to solution implementation.

[0013] Furthermore, the method also includes step S4, which feeds back the effect data of the optimization strategy in the optimization evaluation report to the process-level efficiency benchmark model in S1, so as to correct the initial standard efficiency threshold of subsequent construction projects and form an adaptive iterative optimization closed loop; the benchmark model is continuously iterated and updated through the feedback of optimization effect, thereby improving the system's adaptability to different construction scenarios and forming a continuously evolving evaluation and control system.

[0014] Furthermore, the BIM system in S1 also integrates a material management module for energy-saving building materials. This module automatically adjusts the delivery plan and inventory warning threshold of energy-saving building materials based on real-time efficiency deviations, so as to dynamically match the supply of building materials with construction efficiency; realize the linkage between material management and efficiency assessment, avoid efficiency bottlenecks caused by the lag in the supply of building materials, and reduce the overall cost of building materials inventory and construction.

[0015] Compared with the prior art, the beneficial effects of the present invention are:

[0016] 1. This invention constructs a process-level dynamic efficiency benchmark model adapted to hollow floor slabs and energy-saving building materials. It deeply integrates core processes such as formwork support, anti-buoyancy reinforcement, and energy-saving core mold installation with parameters of fly ash lightweight products and energy-saving building materials. It abandons the traditional fixed quota standard and adopts the sliding window method to adaptively update the efficiency threshold, solving the defect of mismatch between general evaluation standards and on-site working conditions, greatly reducing evaluation deviation, and improving the professionalism and adaptability of efficiency evaluation from the source.

[0017] 2. This invention employs a unique technical approach that combines multi-source heterogeneous sensor data fusion with fuzzy reasoning. It integrates multi-dimensional information from vision, mechanics, laser, and personnel positioning, overcoming the drawbacks of traditional manual statistical data being lagging and highly subjective. In particular, it enables indirect efficiency back-calculation for concealed processes such as anti-buoyancy reinforcement of the box body and the coordination of rib beam steel bars with the core mold, filling the industry's monitoring blind spots and breaking the technical bias that concealed processes can only be inspected after the fact. It achieves real-time, blind-spot-free efficiency quantification for the entire process.

[0018] 3. This invention incorporates a built-in rule library for the process mechanism of hollow core slab flooring, enabling automatic matching and tracing of efficiency bottlenecks. It also adds an effect iteration and building material linkage mechanism, abandoning the manual experience-based troubleshooting mode and automatically generating optimized solutions tailored to construction characteristics. Furthermore, it can continuously optimize evaluation benchmarks based on engineering data and dynamically match the pace of building material supply. Overall, it forms a complete closed loop of real-time monitoring, intelligent traceability, adaptive iteration, and material coordination, effectively improving construction efficiency, controlling evaluation errors, and reducing overall construction costs. Attached Figure Description

[0019] Figure 1 This is a flowchart of a method for real-time evaluation of construction efficiency of a BIM-based hollow core slab structure system. Detailed Implementation

[0020] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0021] Please see Figure 1 This invention provides a technical solution: a real-time evaluation method for the construction efficiency of a BIM-based hollow core slab structure system. This method is mainly applied to the entire construction process of high-rise residential buildings, commercial complexes, public venues, and other building projects using hollow core slab structures. Hollow core slab construction involves multiple interconnected processes, including formwork support, anti-buoyancy reinforcement, rebar tying, energy-saving core mold installation, and concrete pouring. The construction involves numerous stages and a high proportion of concealed work. Furthermore, it extensively utilizes energy-saving building materials such as fly ash lightweight products, energy-saving blocks, and energy-efficient insulating cement products. The installation characteristics of these building materials directly constrain the overall construction pace.

[0022] S1. Constructing a process-level efficiency benchmark model for hollow core slab construction oriented towards energy-saving building materials: Hollow core slab construction involves multiple levels of processes, with significant differences in work content and resource input between processes. The installation specifications, construction losses, and work rhythm of energy-saving building materials directly affect the construction efficiency of each process. Existing technologies lack efficiency benchmarks specifically for hollow core slab processes and do not incorporate energy-saving building material parameters into the evaluation system, leading to significant evaluation biases. This step standardizes and breaks down the hollow core slab construction processes, inputs the core parameters of energy-saving building materials into the BIM system, and builds a dedicated process-level efficiency benchmark model to provide a reference for subsequent efficiency comparisons. Specific technical methods are as follows:

[0023] The entire construction process of hollow core slab flooring is broken down into five fixed core processes: formwork erection, anti-buoyancy reinforcement, rebar tying, energy-saving core mold installation, and concrete pouring. The start and end points and work boundaries of each process are clearly defined. Three types of energy-saving building materials are selected: fly ash lightweight products, energy-saving blocks, and energy-saving insulating cement products. Parameters of the conventional installation time and on-site natural loss coefficients for each type of building material are collected and uniformly entered into the BIM system's built-in database. A three-dimensional model of the hollow core slab structure is built using the BIM platform, and standard efficiency thresholds are independently configured for each construction process within the model.

[0024] S11 Energy-saving building material parameter input: The energy-saving building materials adapted to the benchmark model are fixedly classified into three categories: fly ash lightweight products, energy-saving blocks, and thermal insulation and energy-saving cement products. The system sorts out the single-block installation operation time of each type of building material in the conventional construction scenario of hollow floor slabs, and calculates the natural loss coefficient under the conventional construction environment. All parameters are batch-entered into the BIM system's dedicated parameter module to realize the binding association between energy-saving building material parameters and each construction process.

[0025] S12 Initial Standard Efficiency Threshold Setting: Initial standard efficiency thresholds are set independently for each of the following processes: formwork erection, anti-buoyancy reinforcement, rebar tying, energy-saving core mold installation, and concrete pouring. These thresholds are not interchangeable and each process includes three core indicators: standard working hours, standard labor consumption, and standard machinery consumption. For formwork erection, the required working hours are expressed as the time required per 100 square meters of formwork installation area; for anti-buoyancy reinforcement, the required working hours are expressed as the time required per 100 core mold anti-buoyancy fixing points; for rebar tying, the required working hours are expressed as the time required per ton of rebar; for energy-saving core mold installation, the required working hours are expressed as the time required per 100 core molds; and for concrete pouring, the required working hours are expressed as the time required per cubic meter of concrete.

[0026] S13 Dynamic Threshold Comparison Mechanism Establishment: A dynamic threshold comparison operation mechanism is established, collecting actual efficiency data of completed processes during construction. A sliding window method is used to perform rolling statistical analysis on multiple historical efficiency data sets. When the Nth process is completed, the system obtains the actual construction efficiency data for that process, extracts efficiency feature values ​​from the actual data, and adaptively updates the warning thresholds for subsequent uncompleted processes based on historical working condition changes. The sliding window uses the weighted average of the actual efficiency values ​​of the three to five most recently completed similar processes as a threshold reference for subsequent processes, allowing the thresholds to be automatically and dynamically adjusted according to construction progress, team performance, and changes in on-site working conditions.

[0027] Example: A 33-story high-rise residential building with cast-in-place hollow core slabs is selected as the overall unified application scenario. All core slabs in this project use fly ash lightweight products as energy-saving core mold materials. When implementing this step, the five major processes are first standardized into nodes. The installation time for a single fly ash lightweight product is recorded as 45 seconds, along with a conventional loss coefficient of 3%, to independently configure an initial efficiency threshold for each process. During construction, the system automatically collects actual operation data from the initial formwork support and anti-buoyancy reinforcement processes. The actual efficiency of the energy-saving core mold installation process in the first construction segment is 13.5 man-hours per 100 core molds, higher than the initial threshold of 12 man-hours per 100 core molds. The system uses a sliding window method to adjust the threshold for the second construction segment to 13 man-hours per 100 core molds. After the third construction segment is completed, the system performs a sliding average of the actual efficiency values ​​of the two most recent construction segments, obtaining 12.8 man-hours per 100 core molds, which is used as the evaluation threshold for the fourth construction segment.

[0028] This step integrates the specific construction process for hollow core slabs, the core parameters of energy-saving building materials, and the dynamic threshold adaptive adjustment into the BIM benchmark model, breaking the limitation that fixed quotas cannot adapt to on-site conditions. The unique technical approach of this solution lies in its targeted construction of a dedicated construction process system for hollow core slabs, binding construction parameters for energy-saving building materials, and adding a dynamic threshold adjustment mechanism. This fills the technical gap in conventional BIM applications that only model without establishing efficiency benchmarks, laying a foundation for achieving the core effects of improving construction efficiency by more than 15%, controlling evaluation errors within 5%, and reducing construction costs by 10%.

[0029] Step S2 involves real-time back-calculation of construction efficiency through multi-source heterogeneous sensor data fusion: Current construction efficiency data collection relies on manual statistics, resulting in significant data lag. Hidden processes such as anti-buoyancy reinforcement of the box girder and the coordination of rib beam reinforcement and core molds cannot be directly observed visually, and single-type sensors cannot fully reflect the actual operational status of each process. This step deploys multiple types of sensor terminals to collect multi-source heterogeneous field data. Through fusion calculation, it achieves direct statistics for explicit processes and indirect back-calculation for hidden processes, replacing the manual statistical mode and eliminating monitoring blind spots. Specific technical methods are as follows:

[0030] Four types of terminal equipment—visual sensors, mechanical operation parameter sensors, laser rangefinders, and personnel positioning sensors—are deployed throughout the entire construction area of ​​the hollow core slab, achieving full-coverage data collection on the construction site. The data collection frequency of all sensor terminals is dynamically adjusted according to the construction progress, set to once every ten seconds during normal construction phases and increased to once per second during critical processes. All raw construction data collected in real time by the sensors is synchronously transmitted to the BIM system data processing terminal for data cleaning, normalization, integration, and fusion calculation. For visible processes that can be directly observed, the work duration and progress status are directly calculated based on the collected data. For hidden processes such as the anti-buoyancy reinforcement of the box slab and the assembly of the rib beam reinforcement and core mold, multi-dimensional sensor data is retrieved for correlation analysis to indirectly infer and calculate the actual construction efficiency. The actual construction efficiency of each process is dynamically compared in real time with the standard efficiency threshold of step S1, and the real-time efficiency deviation value of each process is automatically calculated and output.

[0031] S21 Image Acquisition and Motion Recognition: Smart cameras are deployed at key construction work points, with one PTZ camera installed in each of the formwork support area, core mold stacking area, core mold installation area, and concrete pouring area. The system collects video footage of the construction site around the clock, transmitting the video stream in real-time to an edge computing server. An embedded motion recognition algorithm automatically distinguishes between valid construction work and idle personnel behavior, accurately removing invalid time and extracting only the actual valid work time data for each process.

[0032] S22 Mechanical Equipment Operation Parameter Acquisition: Electrical parameter sensors and vibration sensors are deployed on the surfaces of core construction machinery such as tower cranes and concrete pump trucks on site. Electrical parameter sensors collect current, voltage, and power data of the equipment, and the operating status and load conditions of the equipment are determined by changes in the power curve. Vibration sensors collect the vibration spectrum of the equipment during operation, helping to determine whether the equipment is in normal working condition and whether there are inefficient conditions such as no-load operation. The actual operational efficiency of hoisting and pouring processes is calculated based on the equipment's operational sequence patterns.

[0033] S23 Laser Ranging and Core Mold Layout Monitoring: A lidar system is deployed above the energy-saving core mold layout area to scan the core mold layout position, spacing, and coverage area in real time. Point cloud data is used to automatically count the number and integrity of installed core molds, and to calculate the progress rate of the core mold installation process in real time. The laser ranging sensor is also used to monitor the formwork elevation during the formwork erection process, comparing it with the design elevation in the BIM model to determine the progress of formwork leveling.

[0034] S24 Ultra-Wideband Personnel Positioning: Ultra-Wideband (UWB) positioning tags are configured for on-site workers. Each worker entering the construction site wears a safety helmet with an integrated UWB tag, which sends a positioning signal once per second. Work areas for each process are pre-defined in the BIM model. When a worker enters an area, the system records their entry and exit times, calculates the duration of their stay in that area, and accurately calculates the labor efficiency of each process based on the pre-defined area division rules.

[0035] S25 Multi-Source Data Fusion and Hidden Process Inverse Calculation: Video data, machinery operation data, laser scan data, and personnel positioning data are all input into the fusion computing engine to build a fuzzy inference model. This model specifically targets the hidden processes in hollow core slab construction, performing multi-source data association modeling to indirectly calculate the actual operational efficiency of these hidden processes. For processes that can be visually monitored, the system uses a direct calculation method to obtain the efficiency.

[0036] For concealed processes that cannot be directly monitored visually, taking the anti-buoyancy reinforcement process of the box hull as an example, three types of data were used for fusion inference. The first type is data on the consumption of anti-buoyancy wire. RFID material racks are set up in the core mold installation area, and each bundle of anti-buoyancy wire is equipped with an RFID tag, recording the time and quantity of wire removed. The second type is displacement sensor data. Displacement sensors are placed at key locations in the core mold installation area to monitor the displacement of the core mold during concrete pouring. The third type is personnel positioning data, which tracks the dwell time and frequency of entry and exit of workers in the area corresponding to the anti-buoyancy reinforcement process.

[0037] After the above three types of data are input into the fuzzy inference model, the model performs inference according to a preset set of fuzzy rules. Examples of the rule set include: if the consumption of anti-buoyancy wire is in the high range, the personnel's positioning dwell time is in the medium-high range, and the displacement sensor detects no abnormalities, then the completion efficiency of the anti-buoyancy reinforcement process is inferred to be high; if the consumption of anti-buoyancy wire is in the low range but the personnel's positioning dwell time is in the high range and the displacement sensor detects some abnormalities, then the completion efficiency is inferred to be low; if the consumption of anti-buoyancy wire is in the high range but the personnel's positioning dwell time is in the low range and the displacement sensor detects a small number of abnormalities, then the completion efficiency is inferred to be medium. The fuzzy inference model outputs the completion efficiency index of the anti-buoyancy reinforcement process, which is converted into standard working hours for comparison.

[0038] Once the actual construction efficiency of each process is calculated, the system enters the real-time comparison phase. The comparison process is carried out item by item according to the process, comparing the actual construction efficiency value with the standard efficiency threshold, and the result is expressed in the form of efficiency deviation rate. The system sets differentiated deviation tolerance ranges according to the importance of different processes: ±5% for the energy-saving core mold installation process, ±10% for the formwork erection process and the rebar binding process, and ±8% for the anti-buoyancy reinforcement process.

[0039] Example: The entire site is covered by smart cameras encompassing the rebar tying and core mold installation areas. Tower cranes and concrete pump trucks are equipped with electrical parameter sensors and vibration sensors. All workers wear ultra-wideband positioning tags. The smart cameras automatically identify effective work actions such as rebar tying and core mold arrangement, eliminating idle and ineffective time. LiDAR scans the fly ash core mold arrangement position in real time above the core mold installation area, dynamically monitoring the integrity of the arrangement. The ultra-wideband positioning system accurately tracks the time personnel spend in each work area.

[0040] For the concealed process of anti-buoyancy reinforcement of the box hull, the system records the quantity and time of anti-buoyancy wire removal through RFID material racks, monitors whether the mandrel floats during concrete pouring through displacement sensors, and counts the time workers spend in the anti-buoyancy reinforcement area through a positioning system. These three data points are input into a fuzzy inference model to output a completion efficiency index. After the energy-saving mandrel installation process in a certain construction section was completed, the system calculated the actual efficiency to be 14 man-hours per 100 mandrels, while the current dynamic threshold was 12.5 man-hours per 100 mandrels. The efficiency deviation rate was -12%, exceeding the tolerance range of -5%. The system determined that this process had an efficiency bottleneck, and the results were displayed in real-time on the BIM system management dashboard.

[0041] This step employs a combination of multi-source heterogeneous sensors to replace traditional manual statistical methods, enabling automatic real-time collection of construction data around the clock. Through multi-source data fusion and fuzzy inference, it indirectly calculates the efficiency of concealed processes, filling blind spots that conventional techniques cannot monitor. The unique technical approach of this solution lies in integrating four types of heterogeneous data to construct a fusion inference system. Relying on data correlation, it achieves blind-spot-free efficiency quantification of concealed processes, differing from traditional methods of single monitoring and manual statistics. This effectively controls the overall evaluation error to within five percent.

[0042] Step S3 involves calling the process mechanism rule library to trace bottlenecks and generate optimization strategies: Existing technologies, after identifying low efficiency, rely entirely on the personal experience of managers to pinpoint the bottleneck causes, which is time-consuming and lacks standardized judgment criteria. Conventional optimization suggestions cannot align with the characteristics of hollow core slab construction and the constraints of energy-saving building material construction. This step leverages the BIM system's built-in process mechanism rule library to establish a standardized mapping relationship between efficiency deviations, bottleneck root causes, and optimization strategies, automatically locating the root cause and generating customized solutions. Specific technical methods are as follows:

[0043] A dedicated rule library for the construction process of hollow core slabs is pre-built within the BIM system. The system identifies various efficiency deviations, their potential root causes, and corresponding control strategies throughout the construction process, forming fixed mapping entries. All optimization strategies are aligned with the operational requirements of hollow core slab construction and the constraints of energy-saving building materials. When the real-time efficiency deviation calculated in step S2 exceeds the system's preset reasonable range, the rule library call program is automatically triggered. Real-time sensor data and efficiency deviation data are input into the matching module, which compares and matches them one by one with the rule library entries. This accurately pinpoints the root cause of efficiency bottlenecks and automatically generates an optimization evaluation report containing process adjustment plans, loss control strategies, and schedule adaptation suggestions.

[0044] Construction and storage of the S31 process mechanism rule base: The process mechanism rule base is pre-stored in the BIM system database, storing multiple sets of standardized mapping relationships. Each set of entries includes a bottleneck phenomenon identifier, at least one potential root cause, and a unique optimization strategy. All strategies are bound to the construction constraints of fly ash lightweight products, energy-saving blocks, and thermal insulation and energy-saving cement products. The mapping relationships include: When the energy-saving core mold installation efficiency is low, the root causes include delayed core mold transportation, insufficient quantity of anti-buoyancy reinforcement components, misunderstanding of drawings by workers, and conflict between the core mold and the reinforcing bars. The corresponding optimization strategies are to adjust the transportation plan to increase frequency, recalculate the amount of reinforcement components, start BIM visualization briefing, and adjust the reinforcing bar binding sequence. When the concrete pouring efficiency is low, the root causes include unsuitable slump, conflict between the vibrator and the core mold, and pump truck pipe blockage. The corresponding optimization strategies are to adjust the mix ratio, simulate the vibration path using BIM, and start pump pipe cleaning. When the anti-buoyancy reinforcement process efficiency is low, the root causes include non-standard wire binding, non-compliant tie plate spacing, and inadequate reinforcement arrangement. The corresponding optimization strategies are to demonstrate the correct method on-site, rearrange the tie plates according to the drawings, and add reinforcement. When the reinforcing bar binding efficiency is low, the root causes include insufficient gap between the rib beam reinforcement and the core mold, complex drawings and lack of operational familiarity, and untimely supply of reinforcing bars. The corresponding optimization strategies are to generate avoidance diagrams, provide visualization briefing training, and coordinate delivery batches.

[0045] S32 Multi-Factor Correlation Analysis and Bottleneck Tracing: A dedicated bottleneck tracing engine is built, synchronously importing identified real-time efficiency deviation data and corresponding process identifiers into the engine. Taking the energy-saving core mold installation process with an efficiency deviation of -12% as an example, the key input information includes process identifier, deviation magnitude, deviation duration, time of deviation occurrence, and BIM coordinates of the deviation occurrence area. The tracing engine automatically retrieves multi-source sensor data for the entire process lifecycle and conducts correlation analysis from multiple dimensions, including personnel configuration, machine runtime, material supply timeliness, and on-site environmental conditions.

[0046] After multi-factor correlation analysis, the system matches the analysis results with the mapping relationships in the process mechanism rule base. The matching degree calculation uses a weighted scoring method, assigning weights to each matching factor based on its correlation strength with the bottleneck phenomenon. In a specific example, the number of workers remaining in the core mold installation area is four, less than the standard configuration of five, resulting in a matching degree of 80; the tower crane lifting core mold interval is eight minutes, longer than the standard interval of five minutes, resulting in a matching degree of 70; and the core mold stacking area experienced a zero inventory for thirty minutes, resulting in a matching degree of 95. The system calculates the overall matching degree for each possible root cause. The matching degree for the delay in core mold transportation leading to waiting is the highest, exceeding 90. The system identifies this root cause as the most likely cause of the efficiency bottleneck and highlights the bottleneck area in the BIM model.

[0047] S33 Optimization Strategy Generation and Evaluation Report Output: The system extracts corresponding optimization strategies from the rule base based on the positioning results and makes adaptive adjustments according to the current construction conditions. When the positioning bottleneck is caused by delays in core mold transportation, the basic optimization strategies in the rule base include adjusting the transportation plan to increase frequency, setting up temporary stacking buffer zones, and optimizing logistics routes. The system analyzes the logistics routes in the construction area through the BIM model and identifies that the turning radius of the transportation route is too large, adding approximately five minutes to each transportation. Therefore, it adds logistics route optimization suggestions.

[0048] After the optimization strategy is generated, the system outputs an optimization evaluation report according to a preset template. The report includes an efficiency deviation summary table, bottleneck process identification and highlighted screenshots of the BIM model, a description of the root causes of the location and a matching score, process adjustment plans, loss control strategies, and schedule adaptation suggestions. The report is output in both PDF format and as an embedded view within the BIM system.

[0049] Example: When step S2 detects that the efficiency deviation of the energy-saving core mold installation process exceeds -12%, the rule base call program is automatically triggered. The bottleneck tracing engine retrieves personnel location data for that period, showing four workers in the core mold installation area; machinery operation data shows an eight-minute interval between tower crane core mold hoisting; and material data shows that the core mold stacking area inventory was cleared thirty minutes ago. After multi-factor correlation analysis and matching with the rule base, the bottleneck root cause is determined to be the lag in core mold transportation and delivery. The system generates an optimization evaluation report, providing a process adjustment plan to change the transportation schedule from twice a day to three times a day, and to set up a mobile temporary stacking rack to store two hours' worth of core molds. It also develops loss control strategies based on the fragile nature of fly ash core molds, including laying buffer layers along the handling path, providing handling technique training, and implementing a damaged core mold recycling process. Furthermore, it suggests adding a group of installation personnel to recover from delays and increasing the number of pump trucks for concrete pouring to adapt to the project schedule.

[0050] This step solidifies construction experience into a standardized rule base, enabling data-driven automatic traceability and significantly improving bottleneck identification efficiency and positioning accuracy. The unique technical approach of this solution lies in constructing a rule base for the specific process mechanism of hollow core slabs, establishing an intelligent matching link from deviation to root cause to customized strategy. This differs from existing conventional technologies that only provide early warnings without traceability and offer generalized suggestions, helping to improve construction efficiency by more than 15% and reduce construction costs by 10%.

[0051] Step S4 establishes an adaptive iterative optimization closed loop by feeding back the optimization strategy's implementation effect: Existing construction efficiency assessments are mostly single-time static analyses, and the optimization implementation effect cannot be fed back to the assessment benchmark, meaning the assessment model lacks self-iterative capabilities. This step feeds back the effect data of the optimized strategy implementation to the process-level efficiency benchmark model in Step S1, forming a complete closed loop of assessment, optimization, feedback, and iteration. Specific technical methods are as follows:

[0052] After construction management personnel implement various control measures according to the optimization assessment report, the system continuously collects comprehensive effect data on the implementation of these measures, including process efficiency, energy-saving building material loss rate, and changes in construction period, and transmits this data back to the BIM system backend in real time. The system automatically compares the efficiency deviation fluctuations before and after optimization, extracts effective control characteristic parameters, including the magnitude of improvement, the speed of improvement, and the stability level after improvement. These characteristic values ​​are fed back to the efficiency benchmark model in step S1, used for threshold correction of subsequent processes in the current project and initial threshold correction of subsequent projects. The experience data accumulated in this construction is stored in the project database as the basis for setting the initial threshold for the next project, completing the iterative update of the benchmark model.

[0053] Example: In a high-rise residential project, after implementing transportation scheduling optimization measures for the core formwork installation process, the system continuously collects data on the efficiency of subsequent processes and fly ash material consumption, automatically transmitting this data back to the BIM baseline model. In the first construction section after the optimization strategy was implemented, the core formwork installation efficiency improved from 14 man-hours per 100 core forms to 12.8 man-hours per 100 core forms, with the deviation narrowing from -12% to -2.4%. In the second construction section, the efficiency further improved to 12.2 man-hours per 100 core forms. By comparing the efficiency improvement before and after optimization, the system automatically corrects the initial efficiency threshold for the core formwork installation process under similar working conditions. Subsequent construction of similar hollow core slab buildings directly adopts the iterated baseline model.

[0054] This step introduces an iterative feedback loop, breaking the limitations of traditional static evaluation and allowing the efficiency benchmark model to continuously self-optimize based on actual engineering data. The unique technical approach of this solution lies in constructing a closed-loop feedback mechanism between the optimization effect and the BIM benchmark model, enabling the evaluation standard to self-evolve. This differs from the fixed and unchanging traditional static evaluation model, consistently keeping the evaluation error stably controlled within five percent.

[0055] Step S5: Integrating the BIM system with an energy-saving building materials management module to achieve coordinated control: In existing technologies, construction efficiency assessment and building materials management are disconnected, making it impossible to dynamically adjust delivery plans and inventory thresholds based on real-time efficiency fluctuations, easily leading to material shortages, work stoppages, or material stockpiling. This step integrates a material management module into the BIM system, using real-time efficiency deviations as the control trigger condition to achieve dynamic matching between material supply and construction efficiency. Specific technical methods are as follows:

[0056] Based on the existing BIM efficiency assessment system, a dedicated material management module for energy-saving building materials is integrated. This module records the regular delivery cycle, safety stock capacity, and site stacking constraints for fly ash lightweight products, energy-saving blocks, and thermal insulation and energy-saving cement products. The system reads real-time efficiency deviation data for each process. When low efficiency is detected and there is a risk of material supply delays, the system automatically adjusts delivery batches and cycles in advance. When efficiency deviations are identified as low efficiency due to delays in core mold transportation, the material management module automatically retrieves core mold inventory data and consumption rate data to calculate the construction time that the current inventory can sustain, increases the frequency of delivery plans, automatically sends adjustment notifications to the supplier system, raises the inventory warning threshold to 1.5 times the standard inventory level, and pushes replenishment reminders when inventory falls below the warning value. When the construction pace slows down, the inventory warning threshold is automatically lowered, temporarily suspending large-scale material deliveries.

[0057] Example: In a high-rise project, the rebar tying process experienced slow efficiency. System analysis determined a potential delay in the arrival of energy-saving masonry blocks. The material management module automatically coordinated with suppliers to deliver in batches, ensuring continuous construction without idle time. When the concrete pouring process slowed down, the system automatically lowered the inventory warning threshold for energy-saving insulation cement products, postponing subsequent batches to avoid on-site accumulation, damage, and capital tied up. In scenarios where delays in core mold transportation led to low efficiency, the system automatically adjusted the delivery plan from twice a day to three times a day, raising the inventory warning threshold to 1.5 times the standard inventory level.

[0058] This step achieves deep integration of construction efficiency assessment and energy-saving building material management, reducing efficiency bottlenecks caused by material shortages at the source and rationally controlling inventory levels and losses. The unique technical approach of this solution lies in using real-time efficiency deviations as triggering factors for material control, enabling automatic coordination between construction progress and the building material supply chain. This differs from traditional engineering management models that separate these two aspects, effectively helping to reduce construction costs by 10%.

[0059] In summary, this technical solution, through the coordinated operation of the five steps described above, achieves real-time monitoring, accurate evaluation, and intelligent optimization of hollow core slab construction efficiency. Step S2, the real-time acquisition and fusion calculation of multi-source heterogeneous sensor data, improves efficiency data acquisition from daily manual statistics to minute-level real-time acquisition. Step S1, the process-level efficiency benchmark model combined with a dynamic threshold comparison mechanism, significantly reduces evaluation errors. Step S2, the indirect back-calculation of hidden process efficiency, fills the blind spots of traditional monitoring. Step S3, the automatic matching of the process mechanism rule base, greatly shortens the time for bottleneck root cause location. Step S4, the effect feedback mechanism, enables the system to continuously optimize evaluation accuracy through multi-project accumulation. Step S5, the linkage of the material management module, achieves dynamic matching between the building material supply chain and construction efficiency.

[0060] The unique technical approach of this solution lies in its integration of five core modules: process-level BIM benchmark modeling, multi-source heterogeneous data fusion and hidden back-calculation, intelligent traceability of process mechanism rule base, iterative closed-loop optimization effect, and coordinated management and control of building materials. This comprehensive closed-loop technical system is designed specifically for the complex processes, numerous hidden operations, and strong reliance on energy-saving building materials inherent in hollow core slab construction. Key benefits include: real-time monitoring of construction efficiency, timely identification of construction bottlenecks, an increase in construction efficiency of over 15%, control of evaluation errors within 5%, and a reduction in construction costs by 10%.

Claims

1. A BIM-based hollow floor structure system construction efficiency real-time evaluation method, characterized in that: Includes the following steps: S1 constructs a process-level efficiency benchmark model for hollow floor slabs oriented towards energy-saving building materials. The construction process of hollow floor slabs is broken down into formwork support, anti-buoyancy reinforcement, rebar binding, energy-saving core mold installation, and concrete pouring. The standard efficiency thresholds of each process, as well as the installation time parameters and loss coefficient parameters of energy-saving building materials, are entered into the BIM system to establish a process-level efficiency benchmark model. S2 calculates construction efficiency in real time by fusing multi-source heterogeneous sensor data. It collects actual construction parameters of each process in real time through vision sensors, mechanical operation parameter sensors, laser rangefinders and personnel positioning sensors. It then fuses and calculates the collected multi-source heterogeneous data. For the hidden processes that cannot be directly visually monitored, such as the anti-buoyancy reinforcement process of the hollow floor slab box and the process of rib beam steel bar and core mold cooperation, it indirectly calculates the actual construction efficiency through correlation analysis of multi-source data. The calculated actual construction efficiency is then dynamically compared with the standard efficiency threshold in real time to calculate the real-time efficiency deviation of each process. S3 calls the process mechanism rule library to trace bottlenecks and generate optimization strategies. When the real-time efficiency deviation exceeds the preset range, it calls the hollow floor slab process mechanism rule library pre-installed in the BIM system, matches the real-time collected data with the rule library, automatically locates the root cause of the bottleneck process, and generates an optimization evaluation report that includes process adjustment plans, loss control strategies and schedule adaptation suggestions.

2. The method of claim 1, wherein the method is characterized by: In step S1, establishing a process-level efficiency benchmark model includes the following steps: S11 energy-saving building materials include fly ash lightweight products, energy-saving blocks and thermal insulation and energy-saving cement products, and the installation time and loss coefficient of each block of the above building materials are entered into the BIM system; S12 sets an initial standard efficiency threshold for each process, which includes standard working hours, standard labor consumption, and standard machinery consumption. S13 establishes a dynamic threshold comparison mechanism, which uses a sliding window method to adaptively update the warning threshold of the current process based on the actual efficiency data of the completed process, so that the threshold is dynamically adjusted with the construction progress.

3. The method of claim 2, wherein the method is characterized by: In step S2, the fusion of multi-source heterogeneous sensor data includes the following steps: S21 collects video data of the construction area through smart cameras, and extracts the duration of valid work actions and eliminates invalid dwell time by combining motion recognition algorithms. S22 collects the actual running time of mechanical equipment through electrical parameter sensors and vibration sensors of tower cranes and concrete pump trucks, and calculates the actual operating efficiency of hoisting and pouring processes. S23 uses a laser rangefinder to monitor the layout and coverage of the energy-saving core mold in real time and calculates the progress completion rate of the core mold installation process. S24 collects the dwell time of workers in the corresponding process area of ​​the BIM model through the ultra-wideband personnel positioning system, and calculates the labor efficiency of each process by combining the standard area division of each process. S25 inputs the aforementioned multi-source data into the fusion computing engine and uses a fuzzy inference model to perform efficiency back-calculation for hidden processes.

4. The method of claim 3, wherein the method is characterized by: In S25, the efficiency of the concealed process is back-calculated by inputting the data on the consumption of anti-buoyancy wire in the anti-buoyancy reinforcement process of the box body, the displacement sensor data during subsequent concrete pouring, and the data on the duration of personnel positioning and staying in the area into the fuzzy inference model, and outputting the completion efficiency index of the concealed process through the preset fuzzy rule set.

5. The method of claim 4, wherein the method is characterized by: In S3, the hollow floor slab process mechanism rule base stores multiple sets of mapping relationships between bottleneck phenomena and root causes. Each set of mapping relationships includes a bottleneck phenomenon identifier, at least one possible root cause, and a corresponding optimization strategy, wherein the optimization strategy is bound to the construction characteristics of energy-saving building materials.

6. The method of claim 5, wherein the method further comprises: determining a construction efficiency of the BIM-based hollow floor structure system building based on the construction efficiency of the BIM-based hollow floor structure system building. In step S3, the root cause of the automatic bottleneck identification process includes the following steps: S31 inputs the identified real-time efficiency deviation data and corresponding process identifiers into the bottleneck traceability engine; The S32 traceability engine retrieves multi-source sensor data during the construction process of this procedure for multi-factor correlation analysis. The analysis factors include the number of personnel, machine running time, material supply timeliness, and environmental conditions. S33 matches the results of multi-factor correlation analysis with the mapping relationship in the process mechanism rule base, calculates the matching degree of each possible root cause, and selects the root cause with the highest matching degree as the localization result.

7. The method for real-time evaluation of construction efficiency of a BIM-based hollow floor slab structure system as described in claim 6, characterized in that: In S3, the generated optimization evaluation report includes an efficiency deviation summary table, bottleneck process identification, description of the root cause of the problem, process adjustment plan for the construction characteristics of energy-saving building materials, loss control strategy, and schedule adaptation suggestions.

8. The method for real-time evaluation of construction efficiency of a BIM-based hollow core slab structure system as described in claim 7, characterized in that: The method further includes step S4, which feeds back the effect data of the optimization strategy in the optimization evaluation report to the process-level efficiency benchmark model in S1, so as to correct the initial standard efficiency threshold of subsequent construction projects and form an adaptive iterative optimization closed loop.

9. The method for real-time evaluation of construction efficiency of a BIM-based hollow core slab structure system as described in claim 8, characterized in that: The BIM system in S1 also integrates a material management module for energy-saving building materials. This module automatically adjusts the delivery plan and inventory warning threshold of energy-saving building materials based on real-time efficiency deviations, so that the supply of building materials and construction efficiency are dynamically matched.

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