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.

Condition assessment of concrete bridge decks is critical for ensuring structural integrity and public safety, particularly as infrastructure ages. Traditionally, such assessments are made using destructive techniques, such as coring through concrete to collect samples for analysis. However, these invasive methods create weak points within the structure and risk damaging essential components, such as cutting through rebars. This paper explores the use of three non-destructive testing (NDT) methods—electrical resistivity (ER), impact echo (IE), and infrared thermography (IRT)—to evaluate the structural health of concrete bridge decks and overlays. These techniques are tested individually and in combination through a mock-up experiment to detect defects such as delamination and corrosion. The findings demonstrate that while each NDT method has specific strengths—surface ER with a 46.67% detection rate, IE with 40%, and IRT with 53.33%—the combined detection rate increased to 60%. This combined approach provides a more comprehensive assessment and is expected to help establish better maintenance strategies for aging infrastructure. The study highlights the importance of optimizing NDT methods for real-world applications, addressing current limitations such as environmental sensitivity and scanning speed, to improve the early detection and prevention of structural failures.

The urban heat island effect raises road surface temperatures, increasing energy demands and accelerating pavement deterioration. This study evaluates a polymer-based pavement system using methyl methacrylate (MMA) resin with aluminum silicate (AS), glass bubbles (GBs), and microencapsulated n-docosane phase-change material (PCM) to identify the most effective solution. Indoor laboratory tests determined AS as the optimal choice, balancing thermal insulation, workability, and mechanical strength. AS-containing mixtures reduced surface temperatures by ~10 °C and exhibited superior compressive strength (28.2 MPa at 6 wt%) compared to GB (23.7 MPa at 4 wt%) and PCM (27.2 MPa at 6 wt%). AS also maintained stable viscosity at ≤10 wt%, unlike GB and PCM, which became unworkable above 5 wt%. The AS-based system achieved high skid resistance (90.2 BPN), abrasion resistance (0.1% wear after 500,000 cycles), and low VOC emissions (69.64 g/L). Adjusting the resin-to-BPO ratio to 1:0.42 enabled a 30 min curing time at 25 °C, ensuring practical application. These findings highlight AS as the most effective filler for large-scale deployment. Future work should assess long-term durability and optimize formulations for broader adoption in heat-mitigating infrastructure.

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.

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.

Road damage threatens driving safety, making timely maintenance essential. However, conventional repairs require on-site personnel, necessitating traffic control and lane closures. These restrictions cause traffic congestion, leading to unnecessary idling and repeated acceleration and deceleration of vehicles, reducing fuel efficiency and increasing energy consumption. To overcome these limitations, this study proposes a method for performing inspections without lane closures, utilizing machine vision and AI-based damage detection technology. Furthermore, to quantitatively verify the effectiveness of the proposed method, an energy consumption analysis is conducted using the traffic simulator simulation of urban mobility (SUMO) and the vehicle energy simulator future automotive systems technology simulator (FASTSim). Results show lane closures reduced average speed by 25% and increased driving time by over 40%, adding 5044.73 L of fuel for gasoline vehicles and 3208.63 L for diesel vehicles, with CO₂ emissions rising by 11.86 and 8.60 t, respectively. In contrast, the proposed method had minimal traffic impact, with less than 0.1% increases in fuel use and emissions. This approach enables simultaneous multi-lane inspection, improving maintenance efficiency and reducing social costs and energy waste caused by traffic control.