Infrastructures

Post-fire assessment of reinforced-concrete (RC) members is commonly based on concrete compressive strength measurements, a local property that may not fully represent the residual behaviour of flexure-governed members. This study addresses this limitation through a non-destructive methodology based on TLS, aimed at estimating the apparent relative loss of flexural stiffness from observed differences in residual geometry. The point clouds were filtered, rasterized, and subsequently normalized using regression planes fitted to each member. Orthogonal deviations from these planes were statistically analysed to define a geometric condition indicator (DI) based on the difference in point concentration between homologous members within a central deviation band. By comparing structurally equivalent members, increases in beam deflection and camber losses in prestressed joists were calculated and used to derive an apparent deformation-based stiffness-change index ($K_{\Delta }$). The methodology was applied to a one-way ribbed slab affected by a real fire. DI showed strong monotonic association with $K_{\Delta }$, with Spearman coefficients of −0.82 for the beams and 0.92 for the prestressed joists. Beams exhibiting increased deflection and joists exhibiting reduced camber relative to their homologous counterparts showed mean apparent relative stiffness losses of 32.7% and 36.7%, respectively. The results show that the statistical distribution of ge-ometric deviations provides information on the global response of the member that is not captured by the local compressive strength of the concrete. The proposed indica-tors also enable the identification of geometric anomalies consistent with post-fire structural deformation, providing a non-destructive screening and prioritization tool that complements inspection, material testing, and structural analysis.

Cost overrun remains a major challenge in construction project management, particularly in regions characterized by geographical constraints, logistical complexity, and high project uncertainty. Conventional statistical approaches often struggle to capture nonlinear relationships among factors associated with construction cost overruns. This study proposes a hybrid framework integrating Structural Equation Modeling–Partial Least Squares (SEM-PLS) and Gradient Boosting to predict a latent Cost Overrun construct in construction projects in Southwest Papua Province, Indonesia. SEM-PLS was employed to assess the validity and reliability of the research constructs, comprising 24 predictor indicators across three explanatory constructs and three outcome indicators for the latent Cost Overrun construct. The validated indicator-level latent scores were used as input features for the Gradient Boosting model, while the latent Cost Overrun construct was used as the prediction target. The model achieved a mean absolute error (MAE) of 1.7838, a root mean square error (RMSE) of 2.2079, a mean absolute percentage error (MAPE) of 19.10%, and an R² of 0.6173. Feature importance analysis indicated that Risk Factors contributed most strongly (53.64%), followed by Social Factors (26.03%) and Geographical Factors (20.33%). The proposed framework provides a structured approach for risk-oriented construction cost management in high-risk regions.

This study develops a framework for evaluating the social and economic impacts of low-cost road safety improvement projects. The proposed framework applies the Social Return on Investment (SROI) approach to compare the socio-economic outcomes of road safety engineering interventions within 4 levels—Tier 1: removing severe conflicts, Tier 2: reducing speed, Tier 3: managing conflicts, and Tier 4: increasing awareness. Traditional evaluation methods focus on crash reduction and economic efficiency; however, they provide limited information on social outcomes and lack a systematic approach for comparing local road safety interventions. To address this gap, data from 15 road safety improvement projects at different study locations are used to assess the social and economic impacts for three stakeholder groups: road users, communities, and government agencies. The social outcome is converted into monetary values over a five-year evaluation period. Statistical and sensitivity analyses are applied to compare differences across the hierarchy of road safety interventions and evaluate the robustness of the SROI results. Results show that higher-level interventions generate the greater social returns. The average SROI ratios for Tiers 1 to 4 are 17.0, 15.6, 11.5, and 8.1, respectively. Statistical analysis shows a significant difference between intervention levels (F(3,11) = 16.51, p < 0.001). Crash reduction and community satisfaction were the main positive impacts, while increased travel time, fuel consumption, and emissions reduced SROI. The proposed framework provides practical guidance for evaluating low-cost road safety measures based on socio-economic performance in decision-making.