Evaluating the effectiveness of strategies for mitigating fatigue among construction workers in Abuja, Nigeria: a quantitative analysis.

Fatigue is one of the menaces that contribute to the rising number of construction-related accidents and fatalities in projects. Therefore, the purpose of this study is to identify several strategies that can be adopted to mitigate fatigue in construction projects. This was achieved through a quantitative study. A questionnaire was the main instrument for data collection in the quantitative study. The study revealed that frustration/depression or work pressure is one of the significant causes of fatigue in construction projects. Fatigue risk management education and bonding among workers are some of the underlying strategies that can be used to mitigate the identified causes. Depending on the nature of the fatigue causative elements, the study further established that some mitigation strategies are more effective than others. Therefore, further studies should be conducted by prospective researchers on a range of attributes that may influence the success of fatigue mitigation strategies in construction projects.

Evaluating the effectiveness of strategies for implementation of health and safety programs on construction sites in Nigeria: A mixed-method study.

INTRODUCTION: Empirical and anecdotal evidence show that construction projects are delivered on work sites where unsafe acts and conditions abound. Researchers have investigated the strategies that can be adopted to effectively implement health and safety (H&S) in projects so as to reduce the high rates of accidents, injuries and fatalities. However, the effectiveness of these strategies have not been marginally established. Therefore, this study established the effectiveness of H&S implementation strategies on accidents, injuries, and fatalities reduction in Nigerian construction projects. METHOD: A mixed-method research design was adopted for data collection in the study. Physical observations, interviews, and a questionnaire were the instruments used for data collection in the mixed-method research design. RESULTS: The resultant data identified six appropriate strategies for enabling the desired levels of H&S program implementation on construction sites. Setting up statutory bodies such as the Health and Safety Executive to promote awareness, good practices, and standardization was adjudged pertinent as one of the effective H&S implementation programs that can be used to reduce accidents, incidents, and fatalities in projects. It is expected that the adoption of these strategies would culminate in effective H&S program implementation and subsequently a reduction in the prevalence of accidents, injuries, and fatalities in projects.

Establishing the root causes of unsafe behaviors among construction workers: an integrative interpretive structural modeling analysis.

There has been a significant decline in worker productivity at construction sites globally owing to the increase in accidents and fatalities due to unsafe behavior among workers. Although many studies have explored the incidence of unsafe behaviors among construction workers, limited studies have attempted to evaluate the causal factors and to determine the root causes. An integrative interpretive structural modeling analysis of the interrelationships that exist between these causal factors established from relevant literature was conducted in this study to determine the root factors hence bridging this gap. Fifteen causal factors were identified through literature review, and the nature of interrelationships between them was determined using interpretive structural modeling (ISM) and a Cross-impact matrix multiplication applied to classification (MICMAC) analysis. Data was obtained from a purposively selected cohort of experts using semi-structured interviews. The emergent data was subsequently analyzed using the ISM and MICMAC analysis to ascertain the interrelationships between the causal factors. The results of the study showed that age, sleep quality, degree of interaction and workers' skillsets were the root causes of unsafe behavior among construction workers. Besides engendering the establishment of the root causes of unsafe behavior among construction workers, the results of this study will facilitate the prioritization of appropriate solutions for tackling the menace.

Durability Assessment and Microstructure of High-Strength Performance Bricks Produced from PET Waste and Foundry Sand.

Fired clay brickwork in buildings is prone to cracks and deterioration upon exposure to long-time acidic contamination and water absorption, hence decreasing the bearing capacity of masonry walls. As its contribution toward resolving this challenge, this study assessed the durability and morphological characteristics of high-strength performance bricks produced from a mixture of PET waste (PW) and foundry sand (FS). The PET waste bricks (PWBs) were produced through different proportioning (PW: FS) of 20%, 30%, and 40% of the dry mass of FS. The PWBs produced were tested for durability and compressive and tensile strengths and compared to fired clay bricks to evaluate their load-bearing capacity under compression and tension. Furthermore, scanning electron microscopy (SEM) tests were employed to analyze the morphological structure of the bricks. The test results revealed that the PWBs recorded an appreciable strength of 1.5-2 times that of fired clay bricks, and lower water absorption whilst retaining their ultimate strengths after complete immersion in water and acidic concentrations. The morphology of PWB possessed greater intercluster bonds on the surface compared to clay bricks. The findings demonstrate a reasonable methodological approach toward the production of masonry bricks using a mixture of PET waste and spent foundry sands.

Pavement Quality Index Rating Strategy Using Fracture Energy Analysis for Implementing Smart Road Infrastructure.

Developing a responsive pavement-management infrastructure system is of paramount importance, accentuated by the quest for sustainability through adoption of the Road Traffic Management System. Technological advances have been witnessed in developed countries concerning the development of smart, sustainable transportation infrastructure. However, the same cannot be said of developing countries. In this study, the development of a pavement management system at network level was examined to contribute towards a framework for evaluating a Pavement Quality Index and service life capacity. Environmental surface response models in the form of temperature and moisture variations within the pavement were applied, using sensor devices connected to a data cloud system to carry out mathematical analysis using a distinctive mesh analysis deformation model. The results indicated variation in the Resilient Modulus of the pavement, with increasing moisture content. Increase in moisture propagation increased saturation of the unbound granular base which reduced the elastic modulus of the sub-base and base layer and reduced the strength of the pavement, resulting in bottom-up cracks and cracking failure. The horizontal deformation reduced, indicating that the material was experiencing work hardening and further stress would not result in significant damage. Increasing temperature gradient resulted in reduced stiffness of the asphalt layer. In tropical regions, this can result in rutting failure which, over time, results in top-down cracks and potholes, coupled with increasing moisture content.

Review of Big Data Analytics, Artificial Intelligence and Nature-Inspired Computing Models towards Accurate Detection of COVID-19 Pandemic Cases and Contact Tracing.

The emergence of the 2019 novel coronavirus (COVID-19) which was declared a pandemic has spread to 210 countries worldwide. It has had a significant impact on health systems and economic, educational and social facets of contemporary society. As the rate of transmission increases, various collaborative approaches among stakeholders to develop innovative means of screening, detecting and diagnosing COVID-19's cases among human beings at a commensurate rate have evolved. Further, the utility of computing models associated with the fourth industrial revolution technologies in achieving the desired feat has been highlighted. However, there is a gap in terms of the accuracy of detection and prediction of COVID-19 cases and tracing contacts of infected persons. This paper presents a review of computing models that can be adopted to enhance the performance of detecting and predicting the COVID-19 pandemic cases. We focus on big data, artificial intelligence (AI) and nature-inspired computing (NIC) models that can be adopted in the current pandemic. The review suggested that artificial intelligence models have been used for the case detection of COVID-19. Similarly, big data platforms have also been applied for tracing contacts. However, the nature-inspired computing (NIC) models that have demonstrated good performance in feature selection of medical issues are yet to be explored for case detection and tracing of contacts in the current COVID-19 pandemic. This study holds salient implications for practitioners and researchers alike as it elucidates the potentials of NIC in the accurate detection of pandemic cases and optimized contact tracing.