Disputes are unpleasant, resource consuming, and expensive, but they are inevitable in construction contracts. Dispute resolution practices in Nepal are different from the ones in developed countries. One distinct problem in the Nepalese context is that claims remain pending for a considerable amount of time. Two of the most common reasons for claims in Nepalese road construction contracts are owner-dominated contracts and a lack of knowledge about contractual rights. After the new Arbitration Act 1999 in Nepal, there were many changes in traditional and alternative dispute resolution (ADR). This paper discusses general practices of ADR in road construction contracts in Nepal. This research encompasses an extensive literature review, focus group studies, case studies, and surveys to investigate the causes, types, and frequencies of the disputes, along with their resolution practices in the Nepalese road construction industry. Relative importance index and rank analysis were performed to rank the causes of claims. The ADR practices in Nepal were also identified and ranked based on their frequencies. This research found that claims related to changes, site conditions, and delays are the most common types of disputes. Furthermore, this research identified that negotiation, adjudication, and arbitration are the most preferred ADR methods. Therefore, it is essential for project managers, owners, consultants, contractors, and legal professionals to understand frequencies and causes of claims and identify dispute resolution practices to settle them in a timely manner. The authors believe that this research adds to the body of knowledge in terms of offering best ADR practices for Nepalese road construction contracts.

Construction companies are facing unprecedented challenges in terms of coping with the new circumstances as well as understanding their legal and contractual impacts. Because the COVID-19 pandemic affected all businesses across the globe, identifying and analyzing the impact of COVID-19 on construction projects in terms of material supply disruption, labor shortage, construction delay, and disruption in cash flow is vital. The purpose of this study is to enhance construction companies’ ability to better tackle such situations in the future by identifying and addressing the impact of COVID-19 on the material supply disruptions and construction schedule. This research used a quantitative methodology using an online questionnaire as the instrument to collect data. The study analyzed responses from 32 construction company executive members across the United States. The survey collected cost and schedule impact due to material supply disruption in building construction divisions. The analysis shows that COVID-19 pandemic impacted project schedule, increased cost, and disrupted the supply chain of building materials. The findings can provide a better understanding and solution to reduce the pandemic’s impact on the construction industry in the future and understand the legal implications that can arise due to this pandemic disruption.

Contractors play an integral role in construction projects, and their qualifications directly impact various aspects of a project’s success. The unbiased selection of contractors is a challenge in the construction industry worldwide, particularly in public projects where impartiality in the final selection is essential. Numerous factors must be considered when evaluating contractors, making the selection process challenging for the human brain. This paper introduces and compares two methods for assessing contractor prequalification by applying the fuzzy theory. The idea is to facilitate using human judgments in a mathematical system for decision-making with regard to selecting contractors. The method is based on identifying a fuzzy weight for the selection criteria using the Buckley method. Fuzzy TOPSIS and Fuzzy SAW methods are then used for the qualification ranking of the contractors. The proposed models are assessed using a case study. A sensitivity analysis was also conducted to compare the two models. The introduced method is expected to improve the quality of the qualification-based selection of contractors and prevent possible losses from hiring unsuitable contractors.

This study focuses on the contractor’s claims on a hydropower project contracted under an international engineering procurement and construction (EPC) and turnkey contract in Nepal. The contractor faced challenges from the mobilization phase and submitted multiple claims to the employer for time extensions and additional overhead costs. This study identified 80 claims for a time extension and additional overhead costs. Through content analysis, this study grouped these claims under six categories: natural disaster, strike, unforeseen risk, material shortage, payment, and the COVID-19 pandemic. First, the study analyzed the claim type, outcome, and decision criteria of the claims. Next, the analysis presented decisions based on general conditions of contract (GCC) and the particular conditions of contract (PCC) clauses. The study found that the employer granted a time extension to most of the claims under natural disaster under force majeure and rejected most of the claims under the other five groups. Finally, the study presents findings and lessons learned from the case study, including the relevant GCC and PCC clauses used in claims resolution. The findings and lessons learned from this case study could help employers and international EPC contractors who want to build hydropower projects in Nepal.

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.

This study investigated the top three musculoskeletal pains (MSP) among Hispanic construction workers in Texas and the relationship between sleep hours, age, and MSPs on worker productivity. The study recruited 228 participants from 28 small construction companies and surveyed them on their occupation, age, sleep hours, MSP, and the impact of pain on productivity. The results indicated that Foot pain was the most common among these workers, followed by Back pain and others. Additionally, the study found that the Foot was the most common body part reported with MSP among roofers, drywall installers, laborers, and helpers. The study conducted a three-factor ANOVA test to analyze if there were any significant differences in productivity based on age, number of MSPs, and sleep hours. The study found that MSPs and sleep hours significantly impacted productivity. However, there was no significant effect of age. The results also showed that the number of MSPs significantly impacted productivity, with an increase in MSPs leading to a more severe impact on productivity. Additionally, those who slept less than 6 h per day had a more severe impact on productivity than those who slept more. The study suggests that targeted interventions to improve musculoskeletal health and productivity in this population are needed and highlights the importance of considering Hispanic construction workers' specific needs when implementing safety measures and addressing pain management in the workplace.