Machine learning optimization of bio-sandcrete brick modelling using response surface methodology.

In this study, raw grinded groundnut shell (RGGNS) was used as a fine aggregate in the brick industry to reuse agricultural waste in building materials. In this study, an experimental approach was used to examine a new cement brick with raw groundnut shells integrated with compressive strength, water absorption and dry density optimization utilizing response surface methodology (RSM). The raw ground-nut shell content improved the fine aggregate performance of the 40%, 50%, and 60% samples. The 28-day high compressive strength with the raw ground-nut shell was 6.1 N/mm2 maximum, as needed by the technical standard. Samples made from 40%, 50%, and 60% raw groundnut shells yielded densities of 1.7, 2.2, and 1.9 kg/cm3 for groundnut shell (GNS) brick, respectively. A product's mechanical properties meet the IS code standard's minimum requirements. RSM was then utilized to develop a model for the addition of raw groundnut shell to concrete. R-square and Adeq precision values indicated that the results are highly significant, and equations for predicting compressive strength, water absorption, and dry density have been developed. In addition, optimization was performed on the RSM findings to determine the efficiency optimization of the model. Following the optimization results, experiments were conducted to determine the applicability of the optimized model.

Soft computing techniques for predicting the properties of raw rice husk concrete bricks using regression-based machine learning approaches.

In this study, the replacement of raw rice husk, fly ash, and hydrated lime for fine aggregate and cement was evaluated in making raw rice husk-concrete brick. This study optimizes compressive strength, water absorption, and dry density of concrete brick containing recycled aggregates via Response Surface Methodology. The optimized model's accuracy is validated through Artificial Neural Network and Multiple Linear Regression. The Artificial Neural Network model captured the 100 data's variability from RSM optimization as indicated by the high R threshold- (R > 0.9997), (R > 0.99993), (R > 0.99997). Multiple Linear Regression model captured the data's variability the decent R2 threshold confirming- (R2 > 0.9855), (R2 > 0.9768), (R2 > 0.9155). The raw rice husk-concrete brick 28-day compressive strength, water absorption, and density prediction were more accurate when using Response Surface Methodology and Artificial Neural Network compared to Multiple Linear Regression. Lower MAE and RMSE, coupled with higher R2 values, unequivocally indicate the model's superior performance. Additionally, employing sensitivity analysis, the influence of the six input parameters on outcomes was assessed. Machine learning aids efficient prediction of concrete's mechanical properties, conserving time, labor, and resources in civil engineering.

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.