This article presents a review on the applications of basalt fibers and their composites in infrastructures. The characteristics and advantages of high-performance basalt fibers and their composites are firstly introduced. Then, the article discusses strengthening using basalt fiber sheets and BFRP bars or grids, followed by concrete structures reinforced with BFRP bars, asphalt pavements, and cementitious composites reinforced with chopped basalt fibers in terms of mechanical behaviors and application examples. The load-bearing capacity of the strengthened structures can be increased by up to 60%, compared with those without strengthening. The lifespan of the concrete structures reinforced with BFRP can be extended by up to 50 years at least in harsh environments, which is much longer than that of ordinary reinforced concrete structures. In addition, the fatigue cracking resistance of asphalt can be increased by up to 600% with basalt fiber. The newly developed technologies including anchor bolts using BFRPs, self-sensing BFRPs, and BFRP–concrete composite structures are introduced in detail. Furthermore, suggestions are proposed for the forward-looking technologies, such as long-span bridges with BFRP cables, BFRP truss structures, BFRP with thermoplastic resin matrix, and BFRP composite piles.

This study investigates the performance of alkali-activated mortar incorporating slag, fly ash, and desert sand, with a focus on flowability, mechanical properties, sulfate resistance, and microstructural characteristics. A four-factor, three-level orthogonal experimental design was used to analyze the effects of the fly ash substitution rate, alkali content (Na₂O/b), activator modulus, and desert sand replacement rate for natural sand. The results indicate that increased slag and desert sand contents reduce mortar flowability. Despite this, the mortar exhibits excellent mechanical strength, with compressive strength reaching 77.7 MPa at 28 days and increasing to 89.34 MPa under sulfate exposure. However, after 120 days of sulfate erosion, a decline in strength is observed due to the formation of expansive products such as gypsum and caliche, leading to cracking. Microstructural analyses (XRD, SEM/EDS, MIP) reveal partial dissolution of desert sand under alkali activation, enhancing gel formation and reducing cumulative porosity. The pore structure predominantly consists of harmless pores. These findings demonstrate the potential of slag–fly ash–desert sand alkali-activated mortar as a durable and sustainable material for structural and construction engineering applications, especially in sulfate-rich environments or arid regions where desert sand is abundant.

The current research on smart construction sites is mainly from the perspective of the whole life cycle of the project, and often focuses on the identification of factors at the macro level. It lacks in-depth quantitative analysis of the complex interdependence between influencing factors, and it is difficult to accurately identify key driving factors and weight distribution. This paper takes engineering project management as the perspective, constructs a smart site construction model. The advantages of DEMATEL method and ANP method are innovatively combined to construct the DEMATEL-ANP evaluation model, which overcomes the limitations of single method in weight determination and relationship analysis, and provides a more detailed and scientific analysis framework for the evaluation of smart site construction, and, taking the Urumqi region as an example, its smart construction is evaluated and analyzed. The results of the study show that the correlation between the indicators affecting the construction of smart construction sites is strong, in which the comprehensive influence of personnel safety management, construction quality management, and construction safety management play a greater role, with the comprehensive weights of 0.0917, 0.0817 and 0.0767, respectively; the total score of smart construction site construction of Urumqi region is 63.959, which is in the primary construction stage. Among them, the construction and application of meteorological monitoring are the best, scoring 70.26; the construction and application of most indicators, such as personnel safety management, cost comparison decision-making, dust monitoring, and noise monitoring, are the second best; the construction progress control and wastewater monitoring construction are poor, scoring 55.21 and 57.741. The results of this study can provide direct value to key audiences such as construction enterprise managers, government regulators, and smart site solution providers. This paper considers regions with unique climate types to provide a reference for the construction of intelligent construction sites in the same type of regions.

Excessive carbonation curing can impair later-age properties; therefore, determining an appropriate carbonation duration is critical for practical low-CO₂ utilization. In this study, cement paste and mortar specimens were subjected to accelerated carbonation curing under a diluted CO₂ atmosphere (3%) using a concentration-controlled scheme (2.5–3.0%), followed by standard hydration curing for up to 28 d. Carbonation durations of 500, 1000, 2000, and 6000 min were examined. The results show that an early carbonation duration of 1000 to 2000 min achieves an optimal balance between performance enhancement and subsequent hydration development. Compared with the reference specimens, compressive and flexural strengths, as well as durability-related indicators (including electrical flux and EIS parameters), are improved. In addition, the surface microstructure is refined, with a higher proportion of highly crystalline CaCO₃ (>70%). In contrast, prolonged carbonation (>2000 min) induces unfavorable microstructural evolution during subsequent hydration, leading to reductions in mechanical performance and durability. These findings provide a practical duration-control strategy for accelerated carbonation curing using low-concentration CO₂ streams (3–10%), which are typical of light-industry flue gases.

Unsafe behaviors among construction workers constitute a major contributing factor to construction accidents, making it critically important to explore their underlying mechanisms and temporal dynamics from a cognitive perspective. This study employed an exploratory sequential mixed-methods approach. Initially, grounded theory was used to conduct three-level coding of in-depth interview data from 35 construction workers, resulting in the development of a cognitive theory model of unsafe behavior among construction workers comprising two main categories: ‘ perceptual recognition’ and ‘cognitive response’. Subsequently, a questionnaire was designed based on this model, and a 10-day longitudinal survey was conducted among 300 workers. Multi-group structural equation modelling was employed to analyze the temporal variation in the relationship between cognitive ability and unsafe behavior. The results indicate that: workers’ cognitive abilities can be decomposed into four dimensions—perceiving danger, identifying hazards, perceptual response, and decision-making response—and further summarized into two higher-order factors: perceptual recognition and cognitive response; (2) cognitive abilities are significantly negatively correlated with unsafe behavior; (3) this relationship exhibits significant temporal variations, with the inhibitory effect on Day 5 (path coefficient −0.95) being stronger than that on Day 1 (−0.88) and Day 10 (−0.50); furthermore, the ‘cognitive response → decision-making response’ path also shows significant differences between Day 5 and Day 10. The study reveals the pattern of fluctuations over time in the inhibitory effects of workers’ cognitive ability on unsafe behavior, providing a theoretical basis for construction companies to implement dynamic and targeted safety interventions.

Edge computing plays an important role in structural health monitoring (SHM) for transportation infrastructure because it enables local data processing and low-latency decision support. However, SHM systems encounter challenges due to data anomalies caused by sensor faults, transmission errors, or irregular structural behavior. This study evaluates the deployment performance of a SHM anomaly data-processing workflow on resource-constrained edge devices. The workflow includes data cleaning, response separation, and anomaly detection. Missing data are processed using cubic spline interpolation. Jump points and drift are corrected using the Laida criterion, and noise is reduced using wavelet threshold denoising. Response separation is then performed using the detrending method based on time windows, the 3σ criterion, or wavelet packet decomposition. Anomaly detection is then performed using autoregressive integrated moving average with explanatory variables, support vector machines, and recurrent neural networks. Simulation data and field monitoring data from bridge displacement and highway pavement strain are used to evaluate the workflow. The evaluation focuses on runtime, memory usage, central processing unit usage, and a data-processing throughput proxy. This analysis helps to understand the performance and trade-offs of algorithms on edge devices under the resource constraints typical of SHM applications.