The prediction of construction cost of metro shield engineering is of great significance to project management. In this study, we used the rough set theory, a backpropagation (BP) neural network, and quantum particle swarm optimization (QPSO) to establish a prediction model for predicting the metro shield construction costs. The model accounts for the complexity of metro shield construction and the nonlinear relationship between the construction cost factors. First, the factors affecting the construction cost were determined by referring to the Chinese National Standards and analysing the engineering practice of typical metro shield projects. The rough set theory was used to simplify the system of influencing factors to extract the dominant influencing factors and reduce the number of input variables in the BP neural network. Since the BP neural network easily falls into a local minimum and has a slow convergence speed, QPSO was used to optimize the weights and thresholds of the BP neural network. This method combined the strong nonlinear analysis capabilities of the BP and the global search capabilities of the QPSO. Finally, we selected 50 projects in China for a case analysis. The results showed the dominant factors affecting the construction cost of these projects included ten indicators, such as the type of tunnelling machine and the geological characteristics. The determination coefficient, mean absolute percentage error, root mean square error, and mean absolute error, which are frequently used error analysis tools, were used to analyse the calculation errors of different models (the proposed model, a multiple regression method, a traditional BP model, a BP model optimized by the genetic algorithm, and the BP model optimized by the particle swarm optimization). The results showed that the proposed method had the highest prediction accuracy and stability, demonstrating the effectiveness and excellent performance of this proposed method.

Accurately evaluating the vulnerability of prefabricated components in prefabricated buildings is the basis of scientific management of its supply chain. Thus, a novel vulnerability evaluation method for the suppliers of prefabricated components in the context of prefabricated buildings was proposed in this study. First, a vulnerability evaluation index system was identified and constructed by the pressure–state–response (PSR) model. With the clear logic and strong applicability of PSR, this system, including 18 indexes, was easy to understand and exhibited strong practicability. The weights were then determined via the multiplicative analytic hierarchy process with the expansion of a probabilistic language terminology set (PLTS). PLTS could describe decision information more comprehensively, so the evaluation result is more scientific. Subsequently, the probability of the vulnerability level was superimposed in a parallel bottom-to-top method via the controlled interval and memory (CIM) models, and these were extended by the PLTS. This set handled the uncertain information in the decision-making process more efficiently and accurately, and finally determined the vulnerability distribution. Ultimately, the probability of each vulnerability level was determined by combining the weight information. The reconstruction and resettlement housing construction project of urban shanty towns in the Taohua South Road Community in Xihu District, Nanchang City, was selected as a case study. The case study proves the effectiveness of this method, which has higher resolution and is more convenient for managers to make decisions.

This study examines the impact of polyethylene terephthalate (PET) fibers on major mechanical properties of cement mortar including toughness and modulus of rupture (MOR). For this purpose, various mixes were designed and PET fibers were incorporated into mortar mixes at two dosage levels (0.5% and 1% by volume), along with two other varying parameters i.e. water-to-binder (w/b) ratios, and superplasticizer (SP) ratio to adjust the workability of the cement mixtures. The key mechanical characteristics of hardened cement composite including strength, toughness, and MOR were evaluated and a comprehensive parametric study was implemented. Besides, correlation analyses of each pair of the mechanical properties were performed and the coefficients of determination were determined. The experimental outcomes indicated that the optimum fiber volumes for compressive toughness and flexural toughness were 0.5% and 1%, respectively. While lower w/b ratios such as w/b = 0.35 resulted in higher strength properties, improved toughness was noted in higher rations. RSM analysis implemented showed that the optimum MOR can be achieved at the lowest level of w/b and middle levels of SP and Fiber%. Correlation analyses results demonstrated a strong correlation between compressive strength-toughness and compressive strength-MOR with R² = 0.90 and R² = 0.96, respectively. However, lower correlations were observed for flexural strength-toughness and flexural toughness-MOR with similar R² = 0.75.

The protective materials for cultural relic buildings generally have a deficiency of relatively shallow penetration depth. Based on the principle of changing the permeability coefficient of cultural relic buildings by “water blocking water” and considering the characteristics of magnesium acrylate polymer and the requirement of extending the curing time, a method of modifying magnesium acrylate polymer with glycerol and sodium methyl silicate is proposed. Experimental studies on magnesium acrylate, glycerol–magnesium acrylate, and sodium methyl silicate—glycerol–magnesium acrylate polymers were carried out, and tests and analyses on curing time, swelling performance, water loss rate, and soil sample protection were conducted. The results show that the initiator concentration is a key factor affecting the curing rate of magnesium acrylate polymers. When the initiator content is ≥4%, the curing time is significantly shortened to 20–67 min, and the incorporation of glycerol prolongs the curing time by more than 100 min through the dilution reaction system. Glycerol modification significantly enhanced the swelling capacity of the polymer, with the swelling rate increasing by approximately 15–20% compared to the unmodified system. Sodium methyl silicate effectively improved the construction performance of magnesium acrylate and prevented the occurrence of bubbles. The optimal formula of magnesium acrylate polymer is 25% magnesium acrylate, 40% glycerol, and 2% sodium methyl silicate. While maintaining curing for 120 min, it features a high swelling rate (equilibrium swelling ratio Ew ≈ 0.32) and a low dehydration rate (dehydration rate ≤ 35% after 48 h), and has volume stability after interaction with soil samples.

Vulnerability assessments of public service buildings (PSBs) are critical to life cycle management. An accurate evaluation can substantially improve the quality of public services and reduce the government’s financial burden. We proposed a WBS-VBS framework to build a vulnerability decomposition matrix for PSBs. A Chinese vocational education center relocation case study validated the method. Structural equation modeling (SEM) was used to normalize the path coefficients and obtain the index weights. The matter-element extension method was utilized to calculate correlation functions and determine the vulnerability levels of different indicators. The case study results demonstrate the effectiveness of the model.