Construction sites are dynamic and complicated systems. The movement and interaction of people, goods and energy make construction safety management extremely difficult. Due to the ever-increasing amount of information, traditional construction safety management has operated under difficult circumstances. As an effective way to collect, identify and process information, sensor-based technology is deemed to provide new generation of methods for advancing construction safety management. It makes the real-time construction safety management with high efficiency and accuracy a reality and provides a solid foundation for facilitating its modernization, and informatization. Nowadays, various sensor-based technologies have been adopted for construction safety management, including locating sensor-based technology, vision-based sensing and wireless sensor networks. This paper provides a systematic and comprehensive review of previous studies in this field to acknowledge useful findings, identify the research gaps and point out future research directions.

Tower cranes are the most commonly used large-scale equipment on construction site. Because workers can’t always pay attention to the environment at the top of the head, it is often difficult to avoid accidents when heavy objects fall. Therefore, safety construction accidents such as struck-by often occurs. In order to address crane issue, this research recorded video data by a tower crane camera, labeled the pictures, and operated image recognition with the MASK R-CNN method. Furthermore, The RGB color extraction was performed on the identified mask layer to obtain the pixel coordinates of workers and dangerous zone. At last, we used the pixel and actual distance conversion method to measure the safety distance. The contribution of this research to safety problem area is twofold: On one hand, without affecting the normal behavior of workers, an automatic collection, analysis, and early-warning system was established. On the other hand, the proposed automatic inspection system can help improve the safety operation of tower crane drivers.

In certain circumstances, near-miss falls can evolve into fall accidents in construction sites. Insight into near-miss falls offers an efficient way to better understand fall accidents. In this context, this paper explores potential applications of the smartphone as a data-acquisition tool to detect and identify near-miss falls on the basis of an artificial neural network (ANN). In training and evaluation experiments, a loss-of-balance (LOB) environment was artificially established by means of a balance board to simulate the scenarios in near-miss falls. Through a transition model between static and dynamic near-miss falls, the similarity between simulated and actual scenes of near-miss falls was improved. Furthermore, the feasibility of adopting ANN to correctly identify near-miss falls was verified. The results showed that the average precision, recall, and F1 score were 90.02%, 90.93%, and 90.42%, respectively, with an average error-detection rate of 16.26%. In test cases, the thresholds $H_{20\%}$ (0.07692) and $H_{10\%}$ (0.06061) were acquired and illustrated from the perspective of probability. This approach, which demonstrates the feasibility of integrating smartphones and ANN to measure near-miss falls, will help detect near-miss fall events and identify hazardous elements and vulnerable workers. In addition, it provides a new perspective for measuring the relationship between near-miss falls and fall accidents quantitatively, laying a solid foundation for better understanding the occurrence mechanisms of both events.

As a mean of supplying water, Water distribution system (WDS) is one of the most important complex infrastructures. The stability and reliability are critical for urban activities. WDSs can be characterized by networks of multiple nodes (e.g. reservoirs and junctions) and interconnected by physical links (e.g. pipes). Instead of analyzing highest failure rate or highest betweenness, reliability of WDS is evaluated by introducing hydraulic analysis and cascading failures (conductive failure pattern) from complex network. The crucial pipes are identified eventually. The proposed methodology is illustrated by an example. The results show that the demand multiplier has a great influence on the peak of reliability and the persistent time of the cascading failures in its propagation in WDS. The time period when the system has the highest reliability is when the demand multiplier is less than 1. There is a threshold of tolerance parameter exists. When the tolerance parameter is less than the threshold, the time period with the highest system reliability does not meet minimum value of demand multiplier. The results indicate that the system reliability should be evaluated with the properties of WDS and the characteristics of cascading failures, so as to improve its ability of resisting disasters.

This research on identification and classification of construction workers’ activity contributes to the monitoring and management of individuals. Since a single sensor cannot meet management requirements of a complex construction environment, and integrated multiple sensors usually lack systemic flexibility and stability, this paper proposes an approach to construction-activity recognition based on smartphones. The accelerometers and gyroscopes embedded in smartphones were utilized to collect three-axis acceleration and angle data of eight main activities with relatively high frequency in simulated floor-reinforcing steel work. Data acquisition from multiple body parts enhanced the dimensionality of activity features to better distinguish between different activities. The CART algorithm of a decision tree was adopted to build a classification training model whose effectiveness was evaluated and verified through cross-validation. The results showed that the accuracy of classification for overall samples was up to 89.85% and the accuracy of prediction was 94.91%. The feasibility of using smartphones as data-acquisition tools in construction management was verified. Moreover, it was proved that the combination of a decision-tree algorithm with smartphones could achieve complex activity classification and identification.