Within the hazardous construction industry, roofers represent one of the most at-risk workforces, with high fatalities and injury rates largely driven by Work-Related Musculoskeletal Disorders (WMSDs). The primary precursor to these disorders is muscle fatigue, yet its objective assessment remains a significant challenge for implementing proactive safety management. To address this gap, this study details the implementation and validation of an AI-driven predictive analytics framework for automated fatigue detection using surface electromyography (sEMG) signals. Data was collected as participants (novice roofers) performed strenuous, simulated roofing tasks involving sustained standing, stooping, and kneeling postures. A key innovation is a data-driven labeling methodology using Weak Monotonicity (WM) trend analysis to automate the generation of objective labels. After a feature selection process yielded seven significant features, an evaluation of standard models confirmed that their classification performance was highly posture-dependent, motivating a more robust, hybrid solution. The framework culminates in a high-performance hybrid machine learning model. This architecture synergistically combines a Transformer–LSTM network for deep feature extraction with an XGBoost classifier. The model outperformed all standalone approaches, achieving over 82% accuracy across all postures with consistently strong fatigue F1-scores (0.77–0.78). The entire framework was validated using a stringent Leave-One-Subject-Out (LOSO) cross-validation protocol to ensure subject-independent generalizability. This research provides a validated component for AI-enhanced safety management systems. Future work should prioritize field validation with professional workers to translate this framework into practical, real-world ergonomic monitoring systems.

Falls are the most common type of accident in the construction industry, and falls to a lower level are among the leading causes of fatalities. Work-related fatalities due to falls, slips, and trips have been increasing, with Hispanic workers among the highest fatalities. This study investigated the association between fall accidents and attributes such as age, musculoskeletal pain (MSPs), sleep hours, safety knowledge, use of personal protective equipment (PPE), and working hours among Hispanic construction workers involved in building construction. This study collected 220 valid responses and used nonparametric chi-square tests and binary logistic regression to analyze the data. This study found that the location of the fall, MSPs, and use of personal protective equipment have a significant effect on the likelihood of having fall accidents. The strongest predictor of fall accidents was “fall from a ladder”, followed by having two or three MSPs. The use of PPE had the highest decreasing ratio in odds of fall accidents, indicating the importance of wearing PPE properly. The results show the importance of integrating safety management strategies within construction projects’ broader life cycle management. The insights list how project engineers can incorporate these findings into Building Information Modeling (BIM) systems to enhance project planning and safety measures in reducing fall-related accidents and their severe consequences. This study highlights the importance of addressing MSPs, properly using PPE, and reducing falls from ladders in the construction industry to prevent fall accidents among Hispanic workers and minimize their severe consequences.

Falls are a major safety concern in physically demanding occupations such as roofing, where workers operate on inclined surfaces under unstable postures. While inertial measurement units (IMUs) are widely used in wearable fall detection systems, they often fail to capture early indicators of instability related to foot–ground interactions. This study evaluates the effectiveness of plantar pressure sensors, alone and combined with IMUs, for fall detection on sloped surfaces. We collected data in a controlled laboratory environment using a custom-built roof mockup with incline angles of 0°, 15°, and 30°. Participants performed roofing-relevant activities, including standing, walking, stooping, kneeling, and simulated fall events. Statistical features were extracted from synchronized IMU and plantar pressure data, and multiple machine learning models were trained and evaluated, including traditional classifiers and deep learning architectures, such as MLP and CNN. Our results show that integrating plantar pressure sensors significantly improves fall detection. A CNN using just three IMUs and two plantar pressure sensors achieved the highest F1 score of 0.88, outperforming the full 17-sensor IMU setup. These findings support the use of multimodal sensor fusion for developing efficient and accurate wearable systems for fall detection and physical health monitoring.

One of the problems causing subcontractors to have back charges is the lack of contract clauses in the subcontract document to prevent from back charging. This paper identifies the items that are frequently back charged and determines the primary reasons for subcontractor back charges during the construction of the passenger terminal complex of the international airport project. The authors collected 16 items that were back charged, worth USD 658,000. The authors collected and analyzed contract documents, site reports, emails, meeting minutes, and photos to investigate the back charge claims of the prime contractor to the subcontractor. From this case study, the authors found the primary reasons for back charges were a frequent change of construction managers, subcontractor’s lack of knowledge about the contract clauses, lack of proper documentation, and late notice of back charges made to the subcontractor. The analysis and study findings can help subcontractors to understand the potential sources of back charges, identify contract clauses in overcoming back charges, be aware of the possible abuse of back charges, and make more informed decisions before signing a subcontract document.