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The construction industry has long been recognized for its complex safety regulations, which are essential to ensure the well-being of on-site employees. However, navigating these regulations and ensuring compliance can be challenging due to the volume and complexity of the documents involved. This study proposes a novel approach to extracting information from construction safety documents utilizing Large Language Models (LLM), called CSQA, to provide real-time, precise answers to queries related to safety regulations. The approach comprises three modules: (1) the construction safety investigation module (CSI) collects safety regulations for building the information needed. By leveraging a collection of safety regulation PDFs, the system follows a process of text extraction, preprocessing, and global indexing for efficient search. (2) The safety condition identification module (SCI) retrieves the CSI database; after that, the LLM, with its extensive training, processes user queries, searches the indexed regulations, and retrieves pertinent information. (3) the safety information delivery (SID) would provide the answer to the user and incorporate a feedback mechanism to further refine system accuracy based on user responses. Preliminary evaluations reveal the system's superior performance over traditional search engines, owing to its ability to grasp query context and nuances. The CSQA presents a promising method for accessing safety regulations, with potential benefits including reduced non-compliance incidents, enhanced worker safety, and streamlined regulatory consultations in construction

Efficient forklift operation is critical for construction site safety and project progress; yet, the construction industry deals with recurrent issues, including unauthorized forklift operation, operator drowsiness, visibility challenges, blind spots, and load placement errors. This paper introduces the "iSafe ForkLift," a comprehensive safety framework powered by computer vision, specifically designed to tackle these multifaceted safety challenges associated with forklift operations. The framework provides an array of integrated solutions, encompassing facial recognition for authorization, anomaly detection for behavior monitoring, stereo cameras for improved visibility, blind spot solutions, and load placement monitoring. Aligned with OSHA safety standards, it offers opportunities for enhanced forklift safety by addressing a broad spectrum of potential risks within a single, efficient framework. Systematically addressing multiple safety risks within this unified framework significantly elevates overall safety. Future studies should prioritize enhancing technology by merging computer vision with IoT to boost precision and safety, especially on challenging terrains, thereby elevating construction industry standards' reliability