The COVID-19 pandemic disrupted work systems, family, and social life. The mandatory lockdown forced employees to shift to work-from-home (WFH) setup which exposes them to WFH conflicts. This study provides a machine learning—based approach for prioritization of factors affecting WFH conflicts during the COVID-19 pandemic. These factors include time spent with the family (F1), leisure activities (F2), household task (F3), family quality of life (F4), agitation and anger from work (F5), financial obligations (F6), family presence (F7), family issues (F8), health-related (F9), and work burn-out (F10). Using the backpropagation (BP)-artificial neural network (ANN) modeling and Olden’s connection weights (CW) approach, the order of influence of these parameters to the productivity rating (PR) was observed. Based on the results, the 10-21-1 network structure is the best performing model (BPM) with correlation coefficient (R) = 0.98173 and mean squared error (MSE) of 0.02607. This network topology also provided the least Akaike Information Criterion (AIC) value showing that it is the best model. Using its connection weights (CW) through Olden’s approach, the results showed that the financial obligations are the most influential parameter (MIP) while the household task is the least influential parameter to the productivity model. The utilization of machine learning techniques proved to be effective in determining the influence of predictors on the target output. The obtained findings from the study could assist the organization and managers in resolving work-from-home conflict and productivity issues.

Corrosion stands as the primary cause behind the diminished service life of reinforced concrete structures, particularly in environments such as ports, harbors, bridges, and other offshore and near-shore locations where chloride-induced corrosion poses a significant threat. This study investigates the efficacy of three key parameters polypropylene fiber ratio (FR), concrete cover (CC), and bar diameter (∅)—in minimizing corrosion (CL) in reinforced concrete structures. Central Composite Design (CCD) of Response Surface Methodology (RSM) is employed to determine the optimal conditions for these parameters. The number of samples for Impressed Current (IC) testing is determined through this methodology. Initial analysis utilizing the full quadratic model yields a Predicted value R2 of 60.77%. However, employing backward elimination enhances the predictive capability, resulting in an improved R2 value of 87.53%. Sensitivity analysis utilizing the coded units of the RSM model reveals that the polypropylene fiber ratio exerts the most significant impact on corrosion levels, with a sensitivity value of -4.932. Consequently, optimization efforts are focused on this parameter, leading to the identification of an optimized value of 1.17% for FR, which results in minimal corrosion. This research underscores the effectiveness of employing RSM techniques in optimizing corrosion mitigation strategies in reinforced concrete structures, with FR emerging as a critical determinant in achieving corrosion resistance.

Sustainable development on remote islands encounters hurdles such as environmental, logistical, and economic constraints. Small islands face insufficiency and limitations in construction materials due to limited natural resources. Lightweight structures emerge as transformative solutions to overcome challenges in construction. Santiago Island faces construction complications intensified by adverse weather and logistical issues, particularly disruptions in material transport. This study proposed a lightweight structure by addressing logistical challenges, environmental challenges, and structural costs, assessing existing materials including bamboo, exploring sustainable design principles, incorporating structural standards, and constructing and evaluating a typhoon-resistant structural system. The study results of the identification of lightweight construction materials and techniques available on Santiago Island and the investigation of a sustainable design system, which is the A-frame house design or triangular-shaped structure, foster sustainable development and resilience for remote islands. Structural analysis of the lightweight sustainable design was also identified through STAAD to comprehensively check all the structural members. The results of the research propose a hopeful remedy for crafting a cost-effective, environmentally friendly, and durable architectural blueprint tailored specifically for communities residing on small islands within low- to middle-income nations.

In this paper, the performance of feed-forward backpropagation (BP)—artificial neural network (ANN) was evaluated in predicting the construction project cost (CPC). The models include several factors involving the floor area (FA), number of floors (NF), structural material type of the building (MT), height of the building (HB), number of columns (NC), area of the concrete hollow blocks wall (CHB), volume of concrete (VC), weight of steel (WS), and contract duration (CD). The developed neural network model was evaluated based on several accuracy metrics such as correlation coefficient (R), mean squared error (MSE), mean absolute percentage error (MAPE), and Akaike Information Criterion (AIC). The simulation results showed that the governing model has an excellent R value of 0.99885 and a MAPE of 2.5826%. The comparison results between the ANN and multiple linear regression (MLR) suggest that the ANN model provided superior performance with MAPE which is 4.777 times better than that of the MLR model. Moreover, the lowest AIC value was observed in the 9–21-1 network structure suggesting that this is the governing network model for predicting the CPC. The sensitivity analysis (SA) using Garson’s Algorithm (GA) quantitatively determines the relative contribution (RC) of the input parameters (IP) to the construction project cost. The developed model could be utilized as a support instrument for minimizing the cost overruns and losses that may have been incurred in a construction project (CP).

crucial component of growth in infrastructure is estimating construction costs (CC) for pipelaying projects (PP) related to water distribution networks, which guarantees the effective and long-term provision of safe drinking water to communities. In this paper, an artificial neural network (ANN) and multiple linear regression (MLR) model was developed for predicting construction cost for pipelaying projects. The governing model (GM) has a model structure of 9-20-1 (input-hidden-output) with an R = 0.99992. The findings revealed that the ANN-based network was 13.127 times better than the MLR model, based on its MAPE of 3.214 and 42.194%, for ANN and MLR, respectively. The best network also has the lowest Akaike Information Criterion (AIC) among the simulated network structures indicating that it is the best network. The relative importance (RI) of the independent variables including the length, diameter, material type, hydrotesting works, disinfection works, demolition works, restoration works, duration delay, and liquidated damages were calculated utilizing the Garson’s algorithm (GA). It was seen using GA to compute the relative importance of each parameter that the order of influence is seen as restoration works (RW) > length > demolition works (DeW) > material type (MT) > diameter > disinfection works (DiW) > hydrotesting works (HW) > duration delay (D%) > liquidated damages (LD) wherein the restoration works is the most influential parameter. The findings of the study could be used as a reference for better planning and managing pipelaying project activities.