Enhancing construction safety: predicting worker sleep deprivation using machine learning algorithms.

Sleep deprivation is a critical issue that affects workers in numerous industries, including construction. It adversely affects workers and can lead to significant concerns regarding their health, safety, and overall job performance. Several studies have investigated the effects of sleep deprivation on safety and productivity. Although the impact of sleep deprivation on safety and productivity through cognitive impairment has been investigated, research on the association of sleep deprivation and contributing factors that lead to workplace hazards and injuries remains limited. To fill this gap in the literature, this study utilized machine learning algorithms to predict hazardous situations. Furthermore, this study demonstrates the applicability of machine learning algorithms, including support vector machine and random forest, by predicting sleep deprivation in construction workers based on responses from 240 construction workers, identifying seven primary indices as predictive factors. The findings indicate that the support vector machine algorithm produced superior sleep deprivation prediction outcomes during the validation process. The study findings offer significant benefits to stakeholders in the construction industry, particularly project and safety managers. By enabling the implementation of targeted interventions, these insights can help reduce accidents and improve workplace safety through the timely and accurate prediction of sleep deprivation.

Modelling the mechanical properties of concrete produced with polycarbonate waste ash by machine learning.

India's cement industry is the second largest in the world, generating 6.9% of the global cement output. Polycarbonate waste ash is a major problem in India and around the globe. Approximately 370,000 tons of scientific waste are generated annually from fitness care facilities in India. Polycarbonate waste helps reduce the environmental burden associated with disposal and decreases the need for new raw materials. The primary variable in this study is the quantity of polycarbonate waste ash (5, 10, 15, 20 and 25% of the weight of cement), partial replacement of cement, water-cement ratio and aggregates. The mechanical properties, such as compressive strength, split tensile strength and flexural test results, of the mixtures with the polycarbonate waste ash were superior at 7, 14 and 28 days compared to those of the control mix. The water absorption rate is less than that of standard concrete. Compared with those of conventional concrete, polycarbonate waste concrete mixtures undergo minimal weight loss under acid curing conditions. Polycarbonate waste is utilized in the construction industry to reduce pollution and improve the economy. This study further simulated the strength characteristics of concrete made with waste polycarbonate ash using least absolute shrinkage and selection operator regression and decision trees. Cement, polycarbonate waste, slump, water absorption, and the ratio of water to cement were the main components that were considered input variables. The suggested decision tree model was successful with unparalleled predictive accuracy across important metrics. Its outstanding predictive ability for split tensile strength (R2 = 0.879403), flexural strength (R2 = 0.91197), and compressive strength (R2 = 0.853683) confirmed that this method was the preferred choice for these strength predictions.

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.

Evaluation of sleep quality and duration using wearable sensors in shift laborers of construction industry: A public health perspective.

Approximately 21% of the workers in developing and developed countries are shift laborers. The laborer's work shifts can affect personal life and sleep standards, adversely impacting laborers and their manage. This study assesses the impact of various shift plans (seven evenings/7 days, fixed-night or fixed-day, and backup shifts) on shift laborers, considering four shift schedules. Most laborers were on rotational shifts, whereas others were on a permanent day, permanent night, and standby shifts. In a cross-sectional study, 45 development laborers from the National Construction firm were enlisted. Bio-wearable sensors were provided to monitor sleep. Participants were approached and asked to complete a survey bundle comprising the Pittsburgh sleep quality index (PSQI) and Epworth sleepiness scale (ESS). Differences in sleep models were estimated using a Fitbit watch at various shift schedules. The average age of laborers who participated in the study was 37.5 years, and their average experience in the construction company was 6.5 years. The average total sleep time was 346 ± 46 min. The rotational shift laborers yielded the minimum total sleep time compared to the average PSQI and ESS scores of 7.66 ± 1.3 and 6.94 ± 3.4, respectively. Fifteen shift laborers (33.33%) were affected by a sleeping disorder in the present experimental investigation, and 30 participants had inadequate standards of sleep based on the PSQI scores. Poor sleep quality and duration among construction shift laborers decrease productivity at work. Additional studies are expected to assess sleep-related issues affecting construction shift laborers.