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).

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.

Transportation is critical, especially in rural areas as it provides the mobility to people to access different activities satisfying their daily needs. The purpose of this research is to create an artificial neural network (ANN) trip generation model (TGM). 500 households (HH) were surveyed to obtain the independent variables used in the modeling process including the HH size (HHS), number of children in the HH below 7 years old (NCHHBS), number of HH member from 7 to 59 years old (NHHMSF), number of HH member above 59 years old (NHHAF), number of working member of the HH (NWMHH), number of school children in the HH (NSCHH), number of helpers in the HH (NHHH), number of motorize vehicles in the HH (NMVHH), HH income (HHI), highest educational attainment (HEA), head of HH age (HHHA), and number of driver’s license holder in the HH (NDLHHH). Using the Levenberg–Marquardt algorithm (LMA) as the training algorithm (TA) and hyperbolic tangent sigmoid (HTS) function as the activation function (AF), the governing TGM was observed in the 12–25-1 network structure with the highest R value = 0.98476 and least Mean Absolute Percentage Error (MAPE) of 9.04%. Moreover, the governing network structure achieved the minimized Akaike Information Criterion (AIC) value at 25 hidden neurons (HN) indicating that the network has already been generalized and was the best model among those observed in this study. The outcomes of the research showed the efficacy of artificial neural networks in developing trip generation prediction models (PM).

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.

The research was derived from extensive literature reading and addressed the gap in strengthening existing buildings. The study aims to create a model that would correlate the concrete's compressive strength to nondestructive tests (NDTs), establish the strength of in-situ structural members of an existing building using the model, and propose retrofitting intervention strategies as mitigation measures against ground motions. The study presents the artificial neural network (ANN) as the governing model for strength predictions over multi-linear and quadratic regressions. Sensitivity analysis gives prevalent insights into which factor influences the forecast among the input variables. This prediction model has been initiated to evaluate the in-situ strength of the case study building for the analysis following nonlinear static procedures. Two retrofitting interventions were then developed to compare with the performance of the existing three-story building. Predominantly, a performance-based design employing pushover analysis was done where the idealized curves were generated, projecting the base shear and displacements concerning the behavior of the building (ductile or inelastic behavior). This research evaluates the passing criteria of the building based on the performance objectives provided by American Society of Civil Engineers (ASCE) 41–17. The structural member checks in terms of member chord rotations, member shear forces, joint shear stress, and inter-story drifts in connection with the base shear and target displacements evaluation proposed the best retrofitting intervention. The research showed that Case II (retrofitting by shear walls) intervention provided the lowest base shear and passed the considered member checks than RC jacketed with FRP wrapping interventions.

Addressing the worldwide crisis of inadequate physical education (PE) programs requires immediate attention. Despite the advocacy of international organizations like UNESCO and WHO, there still needs to be a significant gap in understanding the effectiveness of PE initiatives globally. Cultural, socio-economic, and policy differences further complicate evaluating and improving these programs. More comprehensive research is needed to promote academic achievement, well-being, and overall health. This is where Global Innovations in Physical Education and Health comes in, a groundbreaking solution poised to revolutionize PE on a global scale. This innovative book serves as a beacon of hope by exploring diverse teaching strategies and creative methods worldwide. Bridging critical research gaps empowers policymakers, educators, researchers, administrators, and health professionals with actionable insights to enhance the quality and inclusivity of PE programs. With its comprehensive coverage of topics such as adaptive PE, nutritional education, and global health initiatives, this book provides a roadmap for transforming PE into a catalyst for holistic health and lifelong well-being.

PE often finds itself overlooked in the broader discourse on educational innovation, despite its crucial role in fostering lifelong health and well-being. Unlike subjects that easily attract attention due to their academic prestige or technological allure, PE is sometimes seen as secondary, relegated to the sidelines of educational reform. Yet, in an age where global health challenges such as obesity, mental health disorders, and sedentary lifestyles are increasingly prevalent (World Health Organization, 2020), the significance of PE cannot be understated. The Global Innovations in Physical Education and Health book seeks to challenge this perception by highlighting the transformative potential of innovative approaches in PE and health education (Garcia, Lopez Cabrera, et al., 2023). It brings together 69 experts from nine countries—Indonesia, Philippines, China, Ireland, United Arab Emirates, Portugal, India, USA, and Canada—to explore how these innovations are reshaping the way we teach, experience, and benefit from PE, making it a field deserving of as much attention and innovation as any other educational domain. This book serves as a crucial resource in guiding the development of more effective, inclusive, and culturally aware health and PE programs. It sets the foundation for actionable teaching and learning models in various educational and cultural contexts. The coverage includes insights into how different countries approach PE, the integration of health education within these programs, and their impact on student health and academic achievement.

Power system protection is essential for maintaining the reliability and stability of electrical grids, ensuring continuous service, and preventing catastrophic failures. As power systems evolve to incorporate renewable energy and increasingly complex configurations, the role of Artificial Intelligence (AI) in enhancing protection mechanisms has become indispensable. This paper reviews the integration of AI in power system protection, highlighting its potential to improve fault detection, adaptive protection strategies, predictive maintenance, and real-time monitoring. AI techniques, including machine learning, deep learning, and expert systems, offer significant advancements in overcoming the limitations of traditional protection schemes. Furthermore, the integration of AI contributes to the development of resilient and sustainable infrastructure, supports innovation in intelligent urban systems, and enhances the reliability of modern power grids. Despite its promising potential, challenges such as data scarcity, model scalability, and real-time processing need to be addressed for effective implementation. This review synthesizes the current literature on AI applications in power system protection, comparing them with conventional methods, and provides information on future research directions and practical applications to improve energy reliability, sustainable urban development, and industrial innovation.