Fall from height (FFH) is the most significant cause of occupational fatalities in the construction industry, accounting for approximately 54% of all accidents. Such fatalities have decreased considerably due to the use of personal protective equipment (PPE). However, the manual monitoring of compliance to PPE is complex and challenging for site managers. Automation in construction safety presents multiple solutions for monitoring safety at sites. In this study, a smart safety hook (SSH) monitoring method is proposed to eliminate the risk associated with FFH accidents by integrating computer vision [closed-circuit TV (CCTV)-imagery] and Internet-of-Things (IoT)-based [inertial measurement unit (IMU)IMU and altimeter] monitoring technologies. The proposed monitoring approach is validated through five real-time scenarios: (1) attached to the scaffolding and $h>1.82\mathrm{m}$ (6 ft), (2) attached to the worker and $h>1.82\mathrm{m}$ , (3) unattached and $h>1.82\mathrm{m}$ , (4) $h<1.82\mathrm{m}$ , and (5) outside of the risk zone. The proposed technique aims to relieve the site manager’s or safety engineer’s workload by smartly and instantaneously alerting of workers’ unsafe behavior (via alarm, LED blinking, and bounding box on live camera feed). Moreover, the IoT-based hardware setup goes to low power to extend the battery life when there is no unsafe behavior. The experimental results demonstrate that the proposed solution exhibits more than 98% accuracy for real-time detection and classification. Furthermore, it can be extended to monitor several workers and their location data in the future.

Detecting intrusion in hazardous areas is one of the priorities and duties of safety enhancement. With the emergence of vision intelligence technology, hazardous-area-detection algorithms can support safety managers in predicting potential hazards and making decisions. However, because of the dynamic and complex nature of the jobsite, high-risk zones have a different geometry and can be changed following the schedule and workspace of activity. This leads to hazardous areas being annotated manually. Thus, this study proposes a computer vision and a 4D BIM-based approach for intrusion detection in hazardous areas, called IDC4D. IDC4D comprises three modules: (1) the 4D BIM-based safety planning (4BSP) module, which analyzes the hazardous area; (2) the hazardous area registration (HAR) module, which delivers the hazardous area from the BIM model to the camera’s first frame image; and (3) the hazardous-area-intrusion-detection module (HAID), which applies the computer vision algorithm to identify the correlation between workers and hazardous areas. The efficiency of the IDC4D approach is validated by testing a maintenance project on the construction site. IDC4D supports the planner in choosing the plan and detecting the event of workers entering hazardous areas while working. It showed an average precision of 93% and 94% in phase 1 and phase 2, respectively. These findings provide insight into how varying geometries of diverse hazard areas can be handled for enhancing intrusion detection.

Fires pose an enormous threat to human safety and many spectacular fires in under-construction buildings were reported over the past few years. Many construction sites only rely on fire extinguishers, as under-construction buildings do not contain a permanent fire protection system. Traditional safety planning lacks a justified approach for the firefighting equipment installation planning in the construction job site. Even though many government agencies made safety regulations for firefighting equipment installations, it is still a challenge to translate and execute these rules at the job site. Currently, the construction industry is devoted to discovering all the possible applications of Building Information Modelling (BIM) technology in the entire phases of the project life cycle. BIM technology enables the presentation of facilities in 3-D and offers rule-based modeling through visual programming tools. Therefore, this paper focuses on a visual language approach for rule translation and a multi-agent-based construction fire safety planning simulation in BIM. The proposed approach includes three core modules, namely: (a) Rule Extraction and Logic Development (RELD) Module, (b) Design for Construction Fire Safety (DCFS) Module, and (c) Con-fire Safety Plan Simulation (CSPS) Module. In addition, the DCFS module further includes three submodules, named as (1) Firefighting Equipment Installation (FEI) Module, (2) Bill of Quantities (BoQs) for firefighting Equipment (BFE) Module, and (3) Escape Route Plan (ERP) Module. The RELD module converts the OSHA fire safety rule into mathematical logic, and the DCFS module presents the development of the Con-fire Safety Planning approach by translating the rules from mathematical logic into computer-readable language. The three sub-modules of the DCFS module visualize the outputs of this research work. The CSPS module uses a multi-agent simulation to verify the safety rule compliance of the portable firefighting equipment installation plan the system in a BIM environment. A sample project case study has been implemented to validate the proof of concept. It is anticipated that the proposed approach has the potential to helps the designers through its effectiveness and convenience while it could be helpful in the field for practical use.

Construction sites are considered as complicated work environments. Various concurrent activities may overlap apropos to time and workspace, predisposing them to spatial–temporal exposure and repetitive accidents. Detecting the characteristics of repetitive accidents before the construction stage contributes to prevent injuries and fatalities caused by spatial—temporal conditions at construction job sites. To resolve this problem, this study proposes a novel hazard identification approach through spatial–temporal exposure analysis called HISTEA, which integrated scenario analysis of accident cases into 4D building information modeling (BIM). The proposed approach consists of three modules: (1) spatial–temporal hazard investigation (SHI) to analyze the accident cases and develop the hazard database of the spatial–temporal overlap condition of pair-wise activities; (2) spatial–temporal condition identification (SCI) to determine the conflict among different activities, considering the workspace and time overlap from the 4D BIM model; and (3) safety information integration (SII) to deliver safety knowledge to the project team through a web-based application. To illustrate and validate this approach, a HISTEA prototype for foundation work has been developed to be used at the pre-construction stage. The developed prototype is based on the analysis of 496 accident reports extracted from the integrated management information system (IMIS) of the Occupational Safety and Health Administration for the SHI module database. The proposed approach is expected to proactively aid project teams in detecting hazards that ultimately reduce repetitive accidents caused by overlapping activities.

The construction sector has long been known for its complicated safety requirements, which are critical to ensure the well-being of workers on site. However, interpreting these requirements and maintaining compliance can be difficult due to the amount and complexity of the paperwork involved. This leads to difficulty extracting safety information from requirement documents. Besides, information that is presented in a manner similar to human-like responses can improve employee understanding. This study proposed a Construction Safety Query Assistant (CSQA) approach to enhance the extraction and knowledge of construction safety regulations using Large Language Models (LLMs). CSQA comprises of three primary components: (1) the Construction Safety Investigation Module (CSI), which gathers and processes safety regulation documents through text extraction and preprocessing to build a searchable database; (2) the Safety Condition Identification Module (SCI), which utilizes LLMs to interpret user queries and extract relevant information from the CSI database, capitalizing on the models' ability to understand context and subtle textual nuances; and (3) the Safety Information Delivery Module (SID), which presents the retrieved information to users and integrates a feedback loop to refine the accuracy and relevance of responses based on user interaction. The CSQA approach was validated with 2 case studies that offered more contextually relevant, possibly lowering non-compliance risks, improving worker safety, and simplifying the consultation process in the construction sector. This study emphasizes its potential to transform access to crucial safety information in the construction industry.

In the construction industry, the imperative for visual surveillance mechanisms is underscored by the need for safety monitoring, resources, and progress tracking, especially with the adoption of vision intelligence technology. Traditional camera installation plans often move toward coverage and cost objectives without considering substantial coverage overlap, inflating processing and storage requirements, and complicating subsequent analyses. To address these issues, this research proposes a voxel-based site coverage and overlapping analysis for camera allocation planning in parametric BIM environments, called the PBA approach. The first step is to collect information from the BIM model, which is the input for the parametric modeling step. After that, the PBA approach simulates the virtual devices and the construction layout by employing visual language programming and then generates a coverage area. Lastly, the performance simulation and evaluation of various placement scenarios against predefined criteria are conducted, including visual coverage and overlapping optimization for eliminating data redundancy purposes. The proposed approach is evaluated through its application to construction projects. The results from these various implementations indicate a marked decrease in data overlap and an overall enhancement in surveillance efficacy. This research contributes a novel, BIM-centric solution to visual information adoption in the construction industry, offering a scalable approach to optimize camera placement while mitigating overlapping areas.