Prefabricated buildings have the characteristics of being energy saving, protecting the environment, being labor saving and having a fast construction speed, which meet the requirement for the sustainable development of the construction industry and are been strongly supported by the country. However, the promotion of prefabricated buildings in China is still in its infancy, with numerous barriers, and the correlation between these factors is not well tested. Therefore, the objective of this study was to identify the main obstacles to the promotion of prefabricated buildings, clarify the correlation between the main barriers and classify them, so as to propose targeted promotion strategies. Combined with the PEST theory and the technology–organization–environment framework, this study has established a list of 27 barriers including policy, the economy, society, technology and management, through a literature review. Experts were invited to complete a questionnaire survey to assess the relative importance of barriers, and 15 major barriers were identified based on the analytic hierarchy process method. We conducted semi-structured interviews, determined the direct relationship between various barriers, established the adjacency matrix and calculated the reachability matrix, established the multi-level hierarchical structure model based on interpretative structural modeling, used Matrices Impacts Croises–Multiplication Appliance Classement to classify factors, and then proposed the industry–organization–project three-level promotion strategy according to the classification results. The results showed that technical factors accounted for the largest proportion in the list of barriers, followed by society, the economy, political factors and, finally, management factors. The main obstacles to the popularization of prefabricated buildings can be divided into three categories: autonomous variables, dependent variables and driver variables. It is necessary to put forward countermeasures from different dimensions to promote the application of prefabricated buildings. This study provides a practical reference for future research, attempts to provide solutions to promote the popularization of prefabricated buildings and also provides methodological guidance for similar research.

Urban rail transit systems, represented by subways, have significantly alleviated the traffic pressure brought by urbanization and have addressed issues such as traffic congestion. However, as a commonly used construction method for subway tunnels, shield tunneling inevitably disturbs the surrounding soil, leading to uneven ground surface settlement, which can impact the safety of nearby buildings. Therefore, it is crucial to promptly obtain and predict the ground surface settlement induced by shield tunneling construction to enable safety warnings and evaluations. This study collects multi-point surface settlement data from monitoring sections and proposes a data preprocessing method based on tangent circles to transform discrete monitored data into continuous and smooth data. On this basis, the Particle Swarm Optimization (PSO) algorithm is employed to optimize a Back Propagation Neural Network(BPNN) for the subsequent prediction of ground surface settlement. The influence of network structure and prediction mode on prediction accuracy is investigated. The study predicts and verifies the settlement over a 5-day period during both the slow settlement stage and the stable stage to evaluate the predictive performance of the proposed method. The maximum relative error of the prediction based on the data preprocessing method and PSO-BP algorithm is 0.46%, demonstrating a good fitting effect. The study indicates that this method can provide a valuable reference for safety warnings and control of ground surface settlement during shield tunneling construction.

With the acceleration of urbanization, the construction of urban subway tunnel networks is advancing towards deeper, denser, and larger subterranean forms. Currently, there is a lack of systematic identification and dynamic reasoning analysis of factors throughout the entire process of subway tunnel construction using the mining method. To reduce the probability of accidents and improve safety risk management in the whole process of subway tunnel construction using the mining method, we propose a dynamic safety evaluation method based on Fuzzy Set Theory (FST) and Bayesian Network (BN). Firstly, based on the identification of main stages of the construction process using the Work Breakdown Structure, a safety risk evaluation index system for subway tunnel construction using the mining method was constructed according to the Risk Breakdown Structure. Secondly, by combining Fuzzy Set Theory, the Analytic Hierarchy Process, and the Bayesian Network, we established a dynamic safety risk evaluation model for subway tunnel construction using the mining method, based on FBN. Lastly, taking a large-section tunnel project using the mining method as an example, the effectiveness and accuracy of this model were verified. The results showed: (1) Causal reasoning analysis indicated that, under the condition of known prior probability, if the case reasoning result is greater than 5%, there is a significant possibility of a safety risk incident. The evaluation results of the model are basically consistent with the actual situation. (2) Diagnostic reasoning analysis revealed that factors such as the tunnel excavation method, the quality of the main waterproof construction, the quality of the detailed construction waterproofing, the design of the monitoring and measurement plan, and the results of the monitoring and measurements, are the main influences on the safety of subway tunnel construction using the mining method. (3) Secondary diagnostic reasoning demonstrated that repeated diagnostic reasoning for the main influencing factors, leading to an investigation path dominated by critical risk factors, can effectively reduce the overall project risk. This research is expected to provide useful insights for the scientific management of safety risks in the construction of subway tunnels using the mining method.

Among the construction methods for subway projects, shield method construction technology has become a more widely used construction method for urban subway construction due to the advantages of a high degree of construction mechanization, low impact of the construction process on the environment, and strong adaptability of the shield machine to the stratum, etc. However, because of the complexity of the surrounding buildings (structures) in the subway construction, coupled with the diversity of the subway shield method construction activities and the uncertainties in the construction environment, to a certain extent, it is determined that the subway construction process is very complicated. The purpose of this study is based on the text mining method, where text is mined and utilized to realize the identification, extraction, and display of safety risk factors. Thus, it guides the safety management on site and provides a basis for knowledge reuse in other metro shield construction projects. Firstly, we analyze the shortcomings of safety risk management in domestic and international metro shield construction via a literature review, especially the utilization of safety risk text data. Secondly, we collect the risk reports submitted by all parties via the “Metro Project Safety Risk Early Warning System”, and manually screen the hidden danger statements with risk characterization to establish a corpus. Thirdly, we use the Jieba word separation package to extract and display the safety risk factors, so as to guide the on-site safety management. Subsequently, with the help of the Jieba word segmentation package for Chinese word segmentation, we develop a professional thesaurus to improve the effect of word segmentation; then, we use the TF-IDF parameter assignment to achieve the structural transformation of the text to extract high-frequency vocabulary; finally, from the high-frequency vocabulary to screen words containing the semantics of the risk to establish the risk of an initial set of words, we use the existing standards and norms to form the collection of safety risk factors of subway shield construction and generate the cloud diagram for visual display.

To address the common issues of lacking indicator system identification, causal relationship quantification, and path simulation analysis in the current research on safety performance in prefabricated construction, a method for improving safety performance in prefabricated construction based on the decision-making trial and evaluation laboratory (DEMATEL) and NK model is proposed. Firstly, through theoretical analysis and literature review, the indicator system for safety performance in prefabricated construction is identified using the grounded theory. Secondly, expert research and quantitative analysis are combined to analyze the causal relationship of the indicators using the DEMATEL method. Then, the DEMATEL method is integrated with the NK model to carry out a key indicator adaptability modeling analysis and three-dimensional simulation. Finally, a case study is conducted to validate the usability and effectiveness of the proposed model and method. The results show that X₆ (construction and implementation of safety management system) had the highest impact on the other indicators, and X₁₄ (quality and safety status of prefabricated components) was most influenced by other indicators. X₆ (construction and implementation of safety management system), X₁ (personnel safety awareness and attitude), X₁₄ (quality and safety status of prefabricated components), and X₁₂ (construction site working environment) were identified as key performance indicators. “X₆ (construction and implementation of safety management system) → X₁ (personnel safety awareness and attitude) → X₁₄ (quality and safety status of prefabricated components) → X₁₂ (construction site working environment)” was considered the optimal path to improve construction safety performance.

Old residential areas present unique challenges in terms of design, stakeholders, and renovation requirements compared to traditional building projects. However, unreasonable construction plans can lead to delays, cost overruns, poor quality, and conflicts between the construction party and local residents. This article proposes an optimization model that prioritizes progress, quality, and cost as the key control objectives, leveraging the actual conditions of renovating old residential areas. The NSGA-II genetic algorithm is employed to solve the mathematical model. To validate the effectiveness and scientific rigor of the algorithm, a renovation project in an old residential area in Wuhan is used as a case study. The findings of this study offer valuable theoretical support for decision makers in selecting appropriate construction plans.