This study addresses the challenge of optimizing fire evacuation efficiency in complex buildings by investigating the impact of automating corridor dimension adjustments on reducing evacuation congestion. A Building Information Modeling (BIM)-based approach using Autodesk Revit 2024, Dynamo version 2.17, and Thunderhead Pathfinder 2023 simulations was employed to test this hypothesis. The results show that automated adjustments in hallways have a significant positive impact on evacuation efficiency in the majority of building floor corridor segments. These findings highlight the potential for dynamic design modifications to enhance building safety and sustainability. Future research will focus on refining this approach for diverse building layouts and occupant behaviors.

Traditional spalling repair on concrete pavement roads is labor-intensive. It involves traffic blockages and the manual calculation of repair areas, leading to time-consuming processes with potential discrepancies. This study used a line scan camera to photograph road surface conditions to analyze spalling without causing traffic blockage in an indoor setting. By using deep learning algorithms, specifically a region-based convolutional neural network (R-CNN) in the form of the Mask R-CNN algorithm, the system detects spalling and calculates its area. The program processes data based on the Federal Highway Administration (FHWA) spalling repair standards. Accuracy was assessed using root mean square error (RMSE) and Pearson correlation coefficient (PCC) via comparisons with actual field calculations. The RMSE values were 0.0137 and 0.0167 for the minimum and maximum repair areas, respectively, showing high accuracy. The PCC values were 0.987 and 0.992, indicating a strong correlation between the actual and calculated repair areas, confirming the high calculation accuracy of the method.

Urban expansion into rural and peri-urban areas increases landslide risks, posing significant threats to infrastructure and public safety. However, most studies focus on surface displacement or meteorological inputs, with less emphasis on subsurface sensor data that could detect early instability precursors. To address these gaps, this study presents a proof-of-concept validation, establishing the feasibility of using subsurface sensor data to predict near-surface slope displacements. A laboratory-scale slope model (300 cm × 50 cm × 50 cm) at a 30° inclination was subjected to simulated rainfall (150 mm/h for 180 s), with displacement measured at depths of 5 cm and 25 cm using PDP-2000 extensometers. The Gradient Boosting Regressor (GBR) effectively captured the nonlinear relationship between subsurface and surface displacements, achieving high predictive accuracy (R² = 0.939, MSE = 0.470, MAE = 0.320, RMSE = 0.686). Results demonstrate that, while subsurface sensors do not detect sudden failure events, they effectively capture progressive deformation, offering valuable inputs for multi-sensor EWS in proactive urban planning. Despite demonstrating feasibility, limitations include the controlled laboratory environment and simplified slope conditions. Future work should focus on field-scale validation and multi-sensor fusion to enhance real-world applicability in diverse geological settings.

Concrete infrastructure is vulnerable to damage such as spalling. Spalling leads to chloride penetration, which causes internal corrosion, weakens structural stability and durability, and increases the likelihood of additional cracks or damage, consequently necessitating repair. Existing repair methods do not consider the factors that affect damage in the repaired areas, so additional damage can occur, making monitoring necessary to identify these factors. Nevertheless, existing studies have not adequately addressed the monitoring of internal conditions within concrete, making it difficult to manage such damage effectively. Accordingly, in this study, a monitoring system was developed using building information modeling (BIM) and the Internet of Things (IoT) to better identify internal changes in concrete pavements. Employing embedded sensors to measure temperature, humidity, and stress within the concrete, our system uses Dynamo scripts for real-time data visualization within BIM. Validated against the ASTM D8292 standard, this system captures and analyzes environmental impacts on concrete. This integration facilitates the detection of internal changes, allowing for the real-time visualization of these impacts. This study can help establish repair plans by identifying factors affecting concrete, contributing to preventive maintenance, potentially reducing maintenance costs, and enhancing the sustainability of concrete infrastructures.

Portland cement concrete is widely used in road construction due to its durability and minimal maintenance needs. However, its susceptibility to spall highlights the drawbacks of conventional repair methods, including cost inefficiencies, delays, environmental impacts, and safety risks from road closures. To address these challenges, this study evaluated the environmental benefits of a spall detection and repair method employing artificial-intelligence-based computer vision technology. By utilizing machine vision techniques, this approach detects spall damage without road closures and automates the calculation of repair areas and material requirements through a proprietary estimation program. Environmental impact assessments were conducted using life cycle assessment across three frameworks, TRACI, ReCiPe, and ILCD, to compare this method with conventional practices. The results revealed a 79% reduction in the overall environmental impacts, including significant decreases in global warming due to shorter road closures and reduced material waste. Resource usage improved through optimized processes, and air pollution decreased, with lower emissions of smog and particulates. This study highlights the potential of machine-vision-driven repair material quantity takeoff as a more efficient and sustainable alternative. The results of this study will help institutional engineers and practitioners adopt sustainable strategies for green infrastructure repair and integrate them into various infrastructure maintenance practices to contribute to the development of sustainable urban environments.

Recent advancements in sensor technology have led to an increase in embedding sensors into construction materials for monitoring purposes in the construction industry. However, systematic research on the resulting changes to the material properties is still lacking. A previous study confirmed that the copper–nickel-plated housing of SHT-31 sensors affects the compressive strength of mortar. Moreover, it is necessary to conduct further research to determine if this analogous occurrence takes place in concrete. This study embedded temperature–humidity sensor housings in concrete at 10 mm, 20 mm, and 30 mm, before performing compression tests and a finite element analysis (FEA). The empirical findings indicate that the compressive strengths of concrete at 10 mm, 20 mm, and 30 mm depths were 26.1 MPa, 28.4 MPa, and 29.4 MPa, respectively. In contrast, the control concrete that did not have a sensor housing had a compressive strength of 31.9 MPa. In the case of mortar, a design strength of 28 MPa was achieved at a depth greater than 30 mm, while concrete reached this design strength at 20 mm. Based on these findings, embedding temperature–humidity sensor housings in concrete is recommended at depths greater than 20 mm from the surface. These results serve as important reference data for determining the optimal embedding depth of sensor housings in structures using cement-based materials.