Construction sites need to be monitored continuously to detect unsafe conditions and protect workers from potential injuries and fatal accidents. In current practices, construction-safety monitoring relies heavily on manual observation, which is labor-intensive and error-prone. Due to the complex environment of construction sites, it is extremely challenging for safety inspectors to continuously monitor and manually identify all incidents that may expose workers to safety risks. There exist many research efforts applying sensing technologies to construction sites to reduce the manual efforts associated with construction-safety monitoring. However, several bottlenecks are identified in applying these technologies to the onsite safety monitoring process, including (1) recognition and registration of potential hazards, (2) real-time detection of unsafe incidents, and (3) reporting and sharing of the detected incidents with relevant participants in a timely manner. The objective of this study was to create and evaluate a low-cost automated safety monitoring system to assist in the construction-safety monitoring process. This paper presents a framework for this safety monitoring system as a cloud-based real time on-site application. The system integrates Bluetooth low-energy (BLE)-based location detection technology, building information model (BIM)-based hazard identification, and a cloud-based communication platform. Potential unsafe areas are defined automatically or manually in a BIM model. Real-time worker locations are acquired to detect incidents where workers are exposed to predefined risks. Then, the safety monitoring results are instantly communicated over the cloud for effective safety management. Through a real-world construction site case study, the system successfully demonstrated the capability to detect unsafe conditions and collect and analyze the trajectories of workers with respect to potential safety hazards. The results indicate that the proposed approach can assist in the construction site monitoring process and potentially improve site safety.

Construction progress estimation to ensure high productivity and quality is an essential component of the daily construction cycle. However, using three-dimensional (3D) laser-scanned point clouds for the purpose of measuring deviations between as-built structures and as-planned building information models (BIMs) remains cumbersome due to difficulties in data registration, segmentation, annotation, and modeling in large-scale point clouds. This research proposes the use of a data-driven deep learning framework to automatically detect and classify building elements from a laser-scanned point cloud scene. The point cloud is first converted into a graph representation, in which vertices represent points and edges represent connections between points within a fixed distance. An edge-based classifier is used to discard edges connecting points from different objects and to form connected components from points in the same object. Next, a point-based object classifier is used to determine the type of building component based on the segmented points and augmented with context from surrounding points. Finally, each detected object is matched with a corresponding BIM entity based on the nearest neighbor in the feature space.

In the field of construction, indoor localization of mobile construction resources remains a universal challenge. Although discussions have focused on the tracking accuracy and affordability of indoor localization technologies, few efforts have focused on practical criteria such as ease of use, latency, and data visualization and remote sharing. To close this gap in knowledge, this study introduces a building information modeling (BIM) and cloud-enabled radio-frequency identification (RFID) localization system. The system consists of three main components: the passive RFID localization system, the BIM visualization system, and the cloud computing system. The proposed system is tested in a full-scale implementation on an actual construction site. The test was designed and conducted to evaluate the localization accuracy, data latency, and real-time data processing and visualization for remote monitoring. A comprehensive analysis is made of various practical issues based on the test results and panel discussion. The findings in this study indicate that the BIM and cloud-enable RFID indoor localization solution has a great potential in practical applications such as site security control, safety management, asset management, and productivity monitoring.

The dynamic nature and limited work space of roadway work zones contribute to dangerous working environments for construction workers. This environment can result in hazardous proximity situations because pedestrian workers are required to operate in close proximity to heavy construction equipment. A total of 609 work zone personnel fatalities were reported in 2012. Previous analysis of work zone fatality data found that the majority of the pedestrian worker and mobile object struck-by fatalities resulted when pedestrian workers were struck by construction equipment. These statistics indicate that current safety practices for pedestrian workers and equipment operators are inadequate. The objective of this study was to create and evaluate a proximity detection and alert system using Bluetooth sensing technology. The scope included hazardous proximity situations between pedestrian workers and construction equipment in roadway work zones at grade. Evaluation metrics were implemented to assess the tested proximity sensing systems including the cost, time and ease of calibration, required hardware, system capabilities, and many others. Commercially available radio frequency identification (RFID) and magnetic field proximity sensing systems were also evaluated to provide a basis for comparison. Various interaction scenarios between pedestrian workers and construction equipment were used in the evaluation of the system. The performance evaluation based on the statistical results showed that all the tested systems were considered reliable with minimal false alarm rates. However, the magnetic system showed a significant drop in its coverage range, while still providing reliable coverage measures, with a set of tests that were more dynamic than other sets of tests. The created Bluetooth system provided the highest level of simplicity with its minimized infrastructure, ease of calibration, and ease of installation. In sum, experimental results demonstrate that the created proximity detection and alert system (1) requires minimal infrastructure; (2) provides adequate alerts to equipment operators and pedestrian workers; and (3) provides, through an alert, an additional layer of hazard avoidance in real time during hazardous-proximity situations in roadway work zones.

With the rapid advancement of sensing and computing technology and the wide adoption of mobile computing, the construction industry has faced a rise in the amount of information and data generated during the lifecycle of the construction project. To deal with a large variety of project data and information to support efficient and effective decision making, visualization, information modeling, and simulation (VIMS) has become critical in the development of capital facilities and infrastructures. The objective of this paper is to identify and investigate grand challenges in VIMS for the construction industry, to assist the academic and industry communities in establishing a future research agenda to solve VIMS challenges. In particular, 17 VIMS grand challenges were identified by an expert task force in the VIMS committee of the ASCE Computing and Information Technology Division, and then VIMS experts in the civil and construction areas from both academia and industry participated in a survey to assess the identified challenges, examine the relative importance of the identified challenges, and investigate current practices and future directions of VIMS. The survey results indicate that several knowledge gaps regarding VIMS challenges between academia and industry still exist, and it is the contention of this research that these particular gaps need to be addressed in future research. These research directions apply to technical issues and sociological/cultural/organizational issues in VIMS challenges. The major contribution of this paper is its claim that the provision of shared views on VIMS challenges lays a firm foundation in which collaborative actions between academia and industry can take place, which will, in turn, advance VIMS for the construction industry. By incorporating the whole project lifecycle, not only project execution but also planning, operation, and maintenance, the analysis provides meaningful hints for VIMS challenges in architecture, engineering, and facility management industries.