Low-rise (1–4 storey) building projects often operate under time and re-source constraints that can weaken the consistency of safety implementation. This study proposes an interpretable, data-driven framework that converts multi-indicator safety assessments into a defensible priority ranking for site interventions. A content-validity–tested questionnaire (24 indicators) was evaluated by eight construction experts and administered to Philippine site engineers and safety officers, yielding 260 valid responses. The dataset satisfied Principal Component Analysis (PCA) prerequisites (Kaiser–Meyer–Olkin (KMO) = 0.7791; Bartlett’s χ2 = 4617.4677, df = 325, p < 0.05). Seven principal components were retained (eigenvalue >1), ex-plaining 79.006% of total variance and forming orthogonal component-level safety dimensions. These component scores were used as inputs to an Artificial Neural Network (ANN) model trained with Levenberg–Marquardt; across 30 random initializations, the most stable architecture was Hidden Neurons (HN) = 1 (validation: Mean Absolute Percentage Error (MAPE) = 6.297% ± 0.487; R = 0.82). For benchmarking, linear and ridge regression achieved validation MAPE of 6.5039% (R = 0.81338) and 6.3908% (R = 0.81185), respectively. To enable interpretability, Garson’s algorithm computed relative importance across seeds, showing a dominant general safety factor (PC1: 64.887% ± 6.962) followed by domain-specific drivers (PC2: 10.648% ± 2.1105; PC5: 8.0125% ± 3.6733; PC6: 7.3157% ± 3.2195). The resulting PCA–ANN–Garson workflow supports evidence-based prioritization of safety improvements in low-rise construction sites by combining predictive accuracy with transparent ranking. This work supports Sustainable Development Goal (SDG) 3 (Good Health and Well-being) and SDG 8 (Decent Work and Economic Growth) through improved prevention of site injuries and stronger working-condition safeguards.

Natural fibers are considered as alternative reinforcements in composites due to their accessibility, affordability, renewability and potential positive effects on some properties. Sources of these fibers include bast, leaf, seed and grass. In this paper, untreated tiger grass fiber, which is typically used as material in soft brooms, has been reinforced in epoxy resin with varying loading of 0 %, 5 %, 10 %, 15 % and 20 % by mass of matrix. For the composite manufacturing, the samples were prepared with the use of silicone molds and were subjected to tensile and water absorption tests. Based from the results, the tiger grass fiber reinforcement has provided significant improvements on tensile strength. The sample with 20 % fiber content achieved the maximum strength of 42 MPa which correspond to about 91 % enhancement as compared to the plain sample. This could be associated with the stress transfer between the unidirectional fibers and the epoxy matrix. As for water absorption, all composites only attained minimal mean values that ranges from 0.035 % to 0.063 %. This could be linked to the water-resistant characteristic of the matrix that protected the reinforcing fibers from being exposed directly to water.

Post-disaster reconstruction projects (PDRP) are integral to ensure that a community will recover and return to normal after a major disaster. The project team success (PTS) in PDRPs is essential to ensure that post-construction efforts will be effective and attain its objective of recovery in the community. An Artificial Neural Network (ANN) model was established considering several factors including post-disaster reconstruction project including project manager's leadership style (PMLS), multi-disciplinary project competence (MDPC), project manager's experience and competence (PMEC), high degree of trust within the project management team (HDTPMT), implementing an effective decision (IAED), effective project control (EPC), competent project manager (CPM), project risk and liability management (PRLM), motivated and well-integrated team (MWIT), and team composition (TC). The governing ANN model has a topology of 10-3-1 network structure and showed good performance with correlation plot (R) of 0.99850, MSE and MAPE of 0.00135 and 0.40559, respectively. The relative importance (RI) of the input parameters (IP) was also determined utilizing the connection weights (CWs) via Garson's algorithm (GA). The findings showed that the MWIT factor is the most influential factor (MIF) to project team success in PDRPs. The results in this study could be utilized to focus on improving areas to guarantee the success of PDRPs.

Steel diagrid high-rise structures require repeated finite-element analyses to accurately predict the multi-directional displacements, which is a time-consuming approach for parametric exploration and early-stage design. This paper presents an artificial neural network (ANN) – particle swarm optimization (PSO) informed model for predicting multi-directional displacements of high-rise steel diagrid frames considering different parameters including the number of storeys (NS), diagrid angle (DA), cross-sectional area (CSA), total weight (TW), and mass of the diagrid exterior (MDE). The model was developed from a dataset of 360 simulations from SAP 2000 ranging from 20-80 storeys and 33.69°-90° angles was used to create a Levenberg-Marquardt (LM) ANN with hyperbolic tangent sigmoid (HTS) activation function and 11 hidden neurons. The PSO was integrated into the model to enhance the training by optimizing the weights and biases (WB) of the network. The ANN-PSO achieved excellent model performance results with R values ranging from 0.9931 to 0.9989 and mean squared error (MSE) ranging from 0.000380 to 0.017200. The sensitivity analysis (SA) utilizing Garson's algorithm (GA) revealed that the number of storeys and diagrid angles are primary influencing the X and Y-displacements while the total weight and cross-sectional area were the leading influential factors to the Z-displacement. The proposed ANN-PSO offers an accurate, interpretable and computationally efficient approach for performance-based preliminary design of steel diagrid high-rise structures.

Chloride-induced corrosion (CIC) is a primary reason of deterioration in reinforced concrete (RC), particularly in marine structures which causes cracking, degradation, and decreased service life. Advances in the 4th Industrial Revolution have enabled utilization of machine learning techniques in different fields of civil engineering. This study develops an Artificial Neural Network (ANN) enhanced by Particle Swarm Optimization (PSO) to predict rebar corrosion in polypropylene fiber reinforced concrete (PFRC). Accelerated corrosion tests were performed using the impressed current method on samples with varying polypropylene fiber content, concrete cover (CC), and bar diameter (BD). Experimental results showed that the 3-7-1 network structure (NS) (3 input neurons (IN), 7 hidden neurons (HN), 1 output neuron (ON)) achieved the highest accuracy with correlation coefficient (R) of 0.98969, mean squared error (MSE) of 0.18846, and mean absolute percentage error (MAPE) of 7.832 %. Employing the generated connection weights (CW) from the governing model (GM), through Olden's connection weights approach, observed that the concrete cover had the most significant influence on corrosion (-43.231%), followed by bar diameter (33.717%) and fiber content (-23.052%). It highlights that increasing concrete cover and fiber content significantly reduces corrosion in PFRC, which may be used by civil engineering professionals as it offers insights for enhancing the durability of reinforced concrete structures. This approach supports SDG 9 (Sustainable Development Goal 9: Industry, Innovation, and Infrastructure) by promoting resilient, innovative construction methods and contributes to SDG 11 (Sustainable Development Goal 11: Sustainable Cities and Communities) by enhancing the longevity and sustainability of urban infrastructure.