Abstract
This study establishes a model of prefabricated building project risk management system based on the Modified Teaching-Learning-Based-Optimization (MTLBO) algorithm and a prediction model of deep learning multilayer feedforward neural network (Backpropagation, BP neural network) to improve the requirements of risk management during the construction of large prefabricated building projects. First, we introduced the BP neural network algorithm based on deep learning. Second, the traditional Teaching-Learning-Based Optimization (TLBO) algorithm was modified by using information entropy, and the modified algorithm was simulated and tested in five test functions. Then, based on the BP neural network and MTLBO algorithm, we established the MTLBO-BP neural network prediction model and tested its performance. Finally, based on the MTLBO-BP neural network prediction model, MATLAB software was used to establish an intelligent model of the risk management system during the construction of prefabricated building projects, and the example verification was performed. In addition, the MTLBO algorithm was verified by test function simulation and established that global searchability is stronger than the TLBO algorithm. Of note, it is not easy to fall into a local optimum. The test results of the MTLBO-BP neural network prediction model revealed that the prediction model converges faster and exerts a better prediction effect. The example verification of the intelligent model of the risk management system during the construction of prefabricated building projects established in this study revealed that the algorithm proposed is more accurate in the reliability and cost prediction of the risk management of prefabricated building projects. Moreover, the algorithm proposed provides theoretical support for intelligent management and decision-making of prefabricated building projects. Overall, this study validates that this algorithm is essential for construction project management, decision-making, and quality assurance.
Highlights
The construction industry is a crucial pillar industry in China and plays an irreplaceable role in promoting the development of the national economy and national urbanization
We selected five functions with local optimal characteristics to validate the efficacy of the Modified Teaching-Learning-Based-Optimization (MTLBO) algorithm in this study
Based on the test results of the average deviation and standard deviation of the MTLBO algorithm and the basic Teaching-Learning-Based Optimization (TLBO) algorithm in the five test functions (Table 4), we observed that the MTLBO algorithm achieved high accuracy in the operation of these five high-dimensional complex functions, especially the global optimal solution was obtained in the functions f1, f2, f3, and f5
Summary
The construction industry is a crucial pillar industry in China and plays an irreplaceable role in promoting the development of the national economy and national urbanization. Prefabricated building project risk management system teaching and learning optimization some hidden safety hazards and environmental problems have become increasingly prominent with the massive development of the construction industry [1]. At the current stage of the construction industry, it is crucial for the development of prefabricated buildings to address the hidden safety hazards and prevent or minimize human casualties during the construction process. Maryam et al [7] investigated the safety hazards of 125 prefabricated building construction sites by analyzing the data of occupational safety accident investigation in the United States; they identified the factors causing the injury and the potential factors, including the instability of the connection between components, resulting in the fall from high altitude [7]. A growing number of scholars in China have explored the safety aspect of prefabricated building construction, including safety management measures and risk assessment methods. Combining prefabricated procedures and the most advanced construction technology platform, the model was used to supervise the construction status; notably, it can improve the success rate of daily operations and decision-making in the full life-cycle management of the building, thereby reducing key schedule risks and ensuring timely project delivery [8]
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