Chunling Zhong

The schedule is a critical factor in the development of prefabricated buildings. This paper establishes the schedule risk influencing factors for prefabricated building projects across five dimensions—design, production, transportation, installation, and others—encompassing a total of 14 factors. By integrating DEMATEL and ISM, it constructs a hierarchical network model using expert knowledge and maps it to Bayesian networks (BN), and the node probabilities were calculated using fuzzy set theory combined with the noisy-OR gate model. This DEMATEL-ISM-BN model not only infers the probability of schedule risk occurrence in prefabricated construction projects through causal reasoning and controls the schedule risk of prefabricated construction projects, but it also deduces the posterior probabilities of other influencing factors when a schedule risk occurs through diagnostic reasoning. This approach identifies the key factors contributing to schedule risk and pinpoints the final influencing factors. Research has shown that the three influencing factors of “tower crane worker lifting level”, “construction worker component installation technology”, and “design changes” significantly affect project progress, providing a new risk assessment tool for prefabricated building project progress, effectively helping enterprises identify potential risks, formulate risk control strategies, improve project success rates, and overall benefits.

This study aims to address quality issues in the production of prefabricated steel structural components for buildings by investigating challenges in quality risk assessment. It identifies key factors contributing to quality problems and establishes an evaluation index system. Traditional methods encounter limitations in handling uncertainty and conducting quantitative analysis. Therefore, the fuzzy Bayesian network (FBN) theory is utilized to perform a probabilistic analysis of quality risks during the production phase. This research achieves a more accurate and dynamic risk assessment by integrating the strengths of fuzzy logic and Bayesian networks (BNs) and by utilizing expert knowledge, the similarity aggregation method (SAM), and the noisy-OR gate model. The study reveals that factors such as the “low professional level of designers”, “poor production refinement”, and “poor storage conditions for finished products” have a significant impact on quality risks. This study offers a scientific risk assessment tool designed to address the quality control challenges commonly experienced in the manufacturing of steel structural components. Identifying the critical risk factors that influence quality empowers actual production enterprises to develop risk management strategies and improvement measures in a more focused manner, thereby facilitating more effective resource allocation and risk prevention and control. Consequently, this approach has a significant impact on enhancing the overall production level and quality within the industry.

This study aims to address quality issues in the production of prefabricated steel structural components for buildings by investigating challenges in quality risk assessment. It identifies key factors contributing to quality problems and establishes an evaluation index system. Traditional methods encounter limitations in handling uncertainty and conducting quantitative analysis. Therefore, the fuzzy Bayesian network (FBN) theory is utilized to perform probabilistic analysis on quality risks during the production phase. This research achieves a more accurate and dynamic risk assessment by integrating the strengths of fuzzy logic and Bayesian networks (BNs) and by utilizing expert knowledge, the similarity aggregation method (SAM), and the noisy-OR gate model. The study reveals that factors such as the "low professional level of designers," "poor production refinement," and "poor storage conditions for finished products" have a significant impact on quality risks. This research offers a novel risk assessment tool for steel structural component production, effectively assisting enterprises in identifying potential risks, formulating risk reduction strategies, and enhancing production quality.

In order to promote the wide application of reactive powder concrete (RPC) in practical engineering. In this paper, RPC was prepared using conventional and economical natural river sand instead of quartz sand and economical and environmentally friendly basalt fiber (BF) instead of steel fiber, and the macroscopic properties of basalt fiber reactive powder concrete (BFRPC) with different fiber content, such as flowability, failure mode, compressive strength and splitting tensile strength were studied, and the strength calculation formula of BFRPC was established based on the mechanical property results. The microscopic morphology and structure of BFRPC were characterized by scanning electron microscope (SEM) and Image Pro Plus (IPP) image processing software. The results show that BF has a small effect on the compressive strength of RPC, while it has a significant increase on the splitting tensile strength. When BF content is at 2 kg/m³, the 28-day compressive strength reaches 95.2 MPa and splitting tensile strength reaches 7.78 MPa. Compared with the RPC with BF of 0 kg/m³, the BFRPC shows an improvement in its 28-day compressive strength by 25.70% and an increase in its splitting tensile strength by 83.92%. According to the microscopic analysis, reasonable fiber content can optimize the internal microstructure of BFRPC, but excessive BF content will produce agglomeration and overlap, resulting in strength loss. Based on the gray correlation analysis method, it was concluded that the particle area ratio and pore fraction dimension were the most correlated with the mechanical properties of BFRPC. In addition, the feasibility and applicability of the BFRPC strength calculation formula were summarized. This research results of this paper provides a valuable reference for the further research and promotion of BFRPC.

On the basis of determining the optimum content of polypropylene fiber reactive powder concrete (RPC), the influence of different steel fiber content on the compressive strength and splitting tensile strength of hybrid polypropylene-steel fiber RPC was studied. The particle morphology and pore parameters of hybrid polypropylene-steel fiber RPC were analyzed by combining scanning electron microscope (SEM) with image-pro plus (IPP) software. The results showed that the RPC ductility can be further improved on the basis of polypropylene fiber RPC, the compressive strength and splitting tensile strength of polypropylene fiber. The optimum content of hybrid polypropylene-steel fiber RPC is 0.15% polypropylene fiber, 1.75% steel fiber. Hybrid polypropylene-steel fiber RPC is mainly composed of particles with small particle size. The particle area ratio first increased and decreased with the increase of steel fiber content, and the maximum steel fiber content is 1.75%. The pore area ratio first decreased and increased with the increase of steel fiber content, and the pore area ratio is the smallest when the steel fiber content is 1.75%. The calculation methods of polypropylene fiber content and steel fiber content and 28-day RPC compressive strength and splitting tensile strength are proposed to select polypropylene fiber content and steel fiber content flexibly according to different engineering requirements, which can provide important guidance for the popularization and application of RPC in practical engineering.

This study investigated four factors (water/binder ratio, silica fume, fly ash, and sand/binder ratio) using the orthogonal experimental design method to prepare the mix proportions of a manufactured sand reactive powder concrete (RPC) matrix to determine the optimal matrix mix proportions. On this basis, we assessed the compressive and splitting tensile strengths of different steel fiber contents under natural, standard, and compound curing conditions to develop an economical and reasonable RPC for various engineering requirements. A calculation method for the RPC strength of the steel fiber contents was evaluated. The results showed that the optimum steel fiber content for manufactured sand RPC is 4% under natural, standard, and compound curing conditions. Compared with standard curing, compound curing can improve the early strength of manufactured sand RPC but only has a small effect on the enhancement of late strength. Although the strength of natural curing is slightly lower than that of standard curing, it basically meets project requirements and is beneficial for practical applications. The calculation formula of 28-day compressive and splitting tensile strengths of manufactured sand RPC steel fiber at 0%–4% is proposed to meet the different engineering requirements and the flexible selection of steel fiber content.