This paper proposes an improved method for car motion planning aimed at addressing the limitations of traditional path planning and obstacle avoidance algorithms in complex environments. The study utilizes Bi-RRT* and polynomial fitting for path planning, incorporating an environment-adaptive polynomial fitting technique based on obstacle density to enhance path precision in areas with high obstacle density. In the local planning phase, intelligent switching of the car’s obstacle avoidance strategies is implemented, allowing the car to use reverse motion or lateral avoidance in high-density regions to prevent stalling. Furthermore, problem decomposition and approximation methods are applied to large-scale quadratic programming (QP) problems in path tracking, improving the efficiency of the MPC algorithm. Experimental results demonstrate that the proposed method significantly enhances the car’s motion performance and stability in complex environments.

Rice husk ash concrete (RHAC) shows promise as a beneficial supplementary material in concrete. However, determining mechanical properties such as compressive strength (CS) and splitting tensile strength (STS) of RHAC through conventional lab-scale methods is laborious and time-consuming. In this research, seven important variables were selected as inputs to predict CS and STS using machine learning (ML) models, including Gaussian Process Regression (GPR), Random Forest Regression (RFR), and Decision Tree Regression (DTR) with grid search optimization. The result presented revealed that selected machine learning models provide well accuracy for CS and STS estimates. Among these, the DTR model demonstrated superior performance, with CS prediction R², RMSE, MAE, and MAPE values of 0.964, 3.314, 2.225, and 5.068, at the testing stage respectively. For STS at the testing stage, DTR achieved R² of 0.969, RMSE of 0.177, MAE of 0.1322, and MAPE of 3.413. GPR and RFR models also performed well, with R² values of 0.9434 and 0.9530 for CS prediction. The partial dependence plot (PDP) analysis revealed the optimal mix design parameters for achieving the desired strength. These results offer valuable insights for sustainable construction, allowing engineers to efficiently predict and optimize material performance, reducing the reliance on time-consuming lab methods.

The deep mixing method (DMM) has become a leading ground improvement technique in Japan over 60 years. However, recent cases of construction defects suggest not only environmental factors but also human errors, such as poorly constructed improved columns. Engineers may struggle to visualize soil behavior from geotechnical borehole survey data from structural viewpoint. This study focuses on the unconfined compression characteristics, sensitivity, and compressibility of clayey soil samples collected from the Kyushu region of Japan. Statistical data, such as the mean values of state variables including failure strain (ε_f), normalized deformation modulus (E₅₀/s_u), liquidity index (I_L), and compression index ratio (C_c1/C_c2) were calculated. By incorporating soil structure considerations into the comparative analysis of these variables, the study aimed to identify thresholds that indicate the "compatibility" or "incompatibility" of clayey soils. Clayey soils with ε_f < 2.8%, E₅₀/s_u ≥ 110, I_L ≥ 1.11, and C_c1/C_c2 ≥ 1.43 were classified as “compatible” with a bulky structure, while those with ε_f ≥ 2.8%, E₅₀/s_u < 110, I_L < 1.11, and C_c1/C_c2 < 1.43 were “incompatible” with a dense structure. The findings provide a structural framework for engineers to interpret soil data, improving DMM quality, risk management, and sustainable construction.

Faults and fractured zones are common geological hazards in tunnel construction, especially in seismic regions where tunnels are highly vulnerable to fault movements. This study uses Urumqi Metro Line 2 as a case, establishes a new damage assessment system for tunnels crossing multiple slip surface faults. Three-dimensional nonlinear finite element models are used to analyze the effects of fault displacement magnitudes, distances between adjacent slip surfaces, and fault dip angles on tunnel lining damage. The study identifies the damage characteristics and evolution of tunnel linings under multiple fault movements, filling gaps in existing research. Lining damage is quantitatively assessed using two indicators: the damage state of the tunnel lining and the overall lining damage index. The proposed model has been verified through two methods. Results show that as fault displacement increases, lining damage progresses from the invert to the crown and sidewalls, longitudinal damage mainly occurs near the slip surface and fault-rock interface. Once the fault displacement exceeds 0.5 m, the overall lining damage index hardly changes anymore. Fault movements along narrow slip surfaces result in a cumulative effect on the lining; however, when the gap between slip surfaces exceeds 21.0 m, slight damage occurs in the central tunnel region. Tensile damage to the lining is highly sensitive to fault dip angle, with a 20.7% variation as the dip angle changes. Additionally, tunnels in the moving block experience more tensile damage than those in the fixed block. Overall, the numerical results of this study provide a better understanding of the response of tunnels under the movement of multiple slip surface faults.

Solid waste management has been a daunting problem in Zimbabwe over the past few years. The research sought to examine residents’ knowledge, attitudes and perceptions towards illegal household solid waste disposal in ward 8, Chegutu. The study adopted descriptive research design which used qualitative and quantitative data collection methods namely questionnaires, interviews and field observation. A total 55 questionnaires were administered randomly amongst residents who were willing to participate since out of 64 people 9 declined to respond. Interviews were done with purposefully selected key informants which included Environmental Management Agency (EMA) officer, senior health officer from city council and small-scale enterprise owners. Statistical Package for Social Sciences (SPSS) version 21and content analysis was used to analyse quantitative and qualitative data respectively. Results demonstrated existence of 9 major illegal dumpsites in ward 8, Chegutu. Findings revealed types of solid waste namely biodegradable (47.27%), inorganic waste (32.73%), glass and tin (14.55%) and rags, clothes (5.45%). Disposal practices established were open burning (72.73%), resource recovery (14.55), animal feeding (9.09%) and composting (3.64). Results showed that majority of households (50.91%) lacked understanding of waste management. Residents were seen to be oblivious to the state of illegal dumpsites near them as 46% reported that they were not concerned at all and had negative attitudes and perceptions that city council is solely responsible for solid waste management. Research verdicts indicated that solid waste management (SWM) remains a challenge in ward 8, Chegutu as all efforts from city council and EMA have seemingly failed. The study recommends that city council should improve stakeholder participation and use of approaches which support circular economy namely recycle and reuse.

Accurate road lane detection is critical for intelligent transportation, but existing camera- and LiDAR-based methods face challenges: LiDAR is ex- pensive, and cameras are sensitive to lighting and weather conditions. This study proposes a method using millimeter-wave radar data, which is cost- effective and robust under various conditions. This work applys an optical flow algorithm to compute point correspondences in radar point clouds, gen- erate lane line bitmaps, and fit polygonal lane regions. The approach effec- tively handles nonlinear lanes and noisy radar data. Experiments with data from multiple radar manufacturers at different intersections and traffic sce- narios demonstrate strong robustness and reliability. The results show that the method is practical for real-time traffic management, providing a reliable alternative to traditional sensors.

Flash floods are critical events for emergency management, yet their modeling remains highly challenging, even in smart cities approaches. Physically based hydrological models are often unsuitable at small spatiotemporal scales due to their computational complexity and dependence on detailed local parameters, which are rarely available during flash floods. With the growing availability of hydrological data, machine learning (ML) has emerged as a promising alternative. This work performs a Systematic Literature Review (SLR) to improve our understanding of the research landscape on ML applications for flash flood forecasting, a significant subset of flash flood modeling. From more than 1,200 papers published until January 2024 in Web of Science, SCOPUS/Elsevier, Springer/Nature, and Wiley, 50 were selected following PRISMA guidelines. The inclusion and exclusion criteria removed reviews, retractions, papers focused on post-flood damage assessment (not forecasting), and those with time resolutions of 6 hours or more, retaining only studies with fine-scale temporal data (<6 hours). For each paper, we extracted information on forecasting horizon, study area size, input data, ML techniques, and outcomes (regression or classification). Results show a sharp rise in ML-based flash flood research, with China leading (38%). Nearly all studies rely on rainfall, discharge, and water level data - often in combination. Long short-term memory (LSTM) networks dominate (60%). Unfortunately, only 10% of the selected studies provide access to their datasets. This lack of transparency poses a major barrier to reproducibility, inhibits fair comparative evaluation of models, and ultimately slows methodological progress in flash flood forecasting. Furthermore, our review highlights that no method consistently outperforms others. This variability in performance is likely influenced by factors such as regional hydrological characteristics (e.g., differences between arid and tropical basins), variations in input data quality, and the length of the forecast horizon (e.g., 1- vs. 6-hour prediction). Lastly, we recommend advancing this field through integration with early warning systems, creation of benchmarks, open data practices, and stronger multidisciplinary collaboration.

This study examines how differences in municipal administrative capacity influence adaptive governance in disaster response, particularly focusing on relationships with NPOs. This research analyzes contrasting approaches to adaptive governance through a comparative case study of two adjacent municipalities in Saga Prefecture, Japan: Omachi Town (population: 6,245) and Takeo City (population: 48,637). Both municipalities experienced floods caused by the same rainfall events in 2019 and 2021 and had limited prior experience with NPO collaboration. The study finds that the small municipality (town) demonstrated stronger adaptive governance by deeply embedding external NPOs into their administration to compensate for limited capacity, while the medium-sized municipality initially showed reluctance toward NPOs but later developed equal partnerships with NPOs. There are two reasons why the small town appeared to be adaptive. First, the small town lacked the administrative capacity to respond to disasters, so they had no choice but to adapt. Second, an official who was dispatched from the prefecture to the town as a liaison—this official later worked as the deputy mayor of this town—instructed the town staff that they needed to develop the capacity to accept assistance. While more robust adaptive governance is generally considered preferable for disaster response and recovery, there are inherent risks: over-reliance on NPOs and the tendency for nationwide NPOs to shift their resources when disasters strike elsewhere. Although this study is limited to the cases of only two municipalities, it provides contrasting valuable insights for the practice of adaptive governance.

A resistivity survey has been conducted in Mukim Padang Peliang, Pendang, Kedah, Malaysia, to assess the granite rock reserve for quarry exploitation. The study area is located along the S-type western granite belt. An electrical resistivity imaging (ERI) with the induced polarization (IP) method together with 2-dimensional (2D) and 3-dimensional (3D) voxel resistivity model analysis was generated in order to attain the anomaly reading related to the granite body in the area. Several software used in data processing are Res2Dinv, SketchUp, and Voxler. Terrameter model LS2, cables, electrodes, cables, connectors, and battery were used as the measurement tools. Five Survey lines were conducted throughout the study area for 400 m each with 5 m inner and 10 m outer spacing using the Wenner-Schlumberger protocol. A comparison of the resistivity value and the induced polarization (IP) led to a better interpretation of the data. 3D modelling of the resistivity has resulted in better presentation, thus relating the data from each survey line into one whole picture. It could be depicted that the orientation of the granite body is distributed in the NW – SE direction. The granite reserve is estimated to be 282.7 million metric tons. The result shows that induced polarization (IP) value is needed to complement the obtained resistivity data, and the 3D model is crucial for finding the interrelationship between all survey lines.

Rapid urbanization presents significant challenges for sustainable housing, often exposing the inadequacies of traditional planning methods. This study addresses this critical gap by proposing Model-Based Engineering (MBE) as a transformative solution. Employing a mixed-methods framework that integrates computational modeling (BIM, GIS), data analytics, and case studies such as NEOM, the research demonstrates that MBE can significantly enhance urban sustainability. Key findings indicate a 15% reduction in carbon emissions, a 20% decrease in energy consumption, and improved urban resilience under extreme conditions. These results surpass traditional approaches and highlight the integration of renewable energy sources and adaptive infrastructure. The study provides actionable strategies for policymakers and urban planners, presenting a replicable model that aligns with Saudi Arabia's Vision 2030 and supports sustainable urban development. Future research directions will focus on integrating AI for predictive modeling and exploring MBE’s applicability in diverse urban contexts worldwide. By harmonizing technology with sustainability, MBE emerges as a pivotal tool for creating resilient urban environments that ensure long-term environmental and economic benefits.

The increasing focus on sustainability in architecture necessitates a detailed understanding of materials and selection processes, particularly in residential projects. Sustainable residential architecture integrates environmental stewardship, economic feasibility, and social responsibility to create durable, eco-friendly living spaces. This research provides an in-depth exploration of principles and methodologies for selecting sustainable materials in residential architectural projects. The research surveyed 647 participants, including architects, builders, and environmental specialists, capturing diverse perspectives. A mixed-method approach using SPSS 28.0 was employed: Exploratory Factor Analysis (EFA) identified underlying dimensions of material selection, regression models quantified factor impacts, and additional tools including ANOVA, correlation tests, ranking, and sensitivity analysis prioritized variables. Results showed that thermal performance was highly significant in ANOVA (F = 15.72, p = 0.0003), while material availability strongly correlated with cost-efficiency (r = 0.85). Sensitivity analysis indicated material availability as the most influential factor (factor score = 0.85), and ranking tests highlighted low carbon footprint as the top priority (27.0%). In EFA the Environmental (Ecological Impact, loading = 0.87), Economic-Performance (Material Availability, loading = 0.86), and Social-Cultural (Aesthetic Value, loading = 0.83).Regression analysis identified durability as the strongest predictor of material sustainability (β = 0.60, p < 0.001, R² = 0.68). These findings emphasize the central role of informed material selection in balancing performance, cost, and environmental impact. By presenting a structured framework, it equips professionals with practical guidelines for sustainable design, aligning with the principles of sustainable development.

Effective management of landslide events requires robust mapping and visualization tools to ensure prompt responses and a thorough understanding of the situation. This study undertakes a comparative analysis of web-based tools specifically tailored for landslide reporting, mapping, and viewing, to evaluate their functionalities, usability, and suitability across diverse stakeholder groups and operational contexts. This article offers valuable considerations for users seeking specific functionalities and geographical coverage, contributing to ongoing efforts to leverage the capabilities of web-based technologies in the realm of landslide management in addressing this critical geological hazard. The research encompasses a comprehensive literature review and hands-on evaluations of selected platforms designed to meet the needs of landslide management. Key dimensions under scrutiny include data input mechanisms, spatial analysis features, visualization capabilities, user interfaces, accessibility, and customization options. Four publicly available web-based tools underwent examination in this study: the NASA Landslide Viewer, the Global Landslide Detector, the Western North Carolina (WNC) Landslide Hazard Data Viewer, and the Landslide Susceptibility Map Viewer. Each platform enables users to visualize landslide occurrences, access pertinent datasets, and scrutinize specific details about each landslide event. Notably, the Landslide Viewer, to monitor global landslides. In contrast, the WNC Landslide Hazard Data Viewer focuses specifically on North Carolina, United States, while the Landslide Susceptibility Map Viewer prioritizes comprehensive mapping within Ireland. Of all the platforms, only the Landslide Susceptibility Map Viewer actively encourages users to contribute by submitting landslide reports, thereby providing real-time observations that enhance the dataset.

Sustainable biomineralization technologies rely on the efficient hydrolysis of urea, typically catalyzed by urease-producing microorganisms or purified enzymes. However, conventional approaches such as microbially induced calcium carbonate precipitation (MICP) and enzyme induced calcium carbonate precipitation (EICP) face limitations related to biosafety risks and high costs, respectively. In this study, soybean, an abundant and low-cost agricultural resource, was utilized to extract urease through a soaking and centrifugation process. A geometric modeling approach using tri-axial ellipsoid theory was introduced to explain how soybean grain size affects sieved powder yield. The effects of soybean grain size, powder concentration, temperature, pH, and storage conditions on the urease activity were systematically evaluated. Results showed that smaller soybean particle sizes resulted in lower extraction efficiency, whereas medium-grain soybeans provided the most cost-effective source due to their higher sieved powder yield and lower market price. Urease activity was positively correlated with both powder concentration and temperature within the tested range and reached its maximum at approximately pH 8. Additionally, storage at 4 °C significantly preserved the enzyme's initial activity over 72 h compared to room temperature conditions. These findings establish a practical foundation for the cost-effective production of plant-derived urease, promoting broader application of biomineralization techniques in sustainable geotechnical engineering.

In recent years, the incidence of sediment disasters caused by climate change, such as torrential rains exceeding expectations due to global warming, has been increasing year by year, and the associated human and economic losses have been expanding. In this paper, nonwoven filters were installed on the slope of an on-site observation area in the sediment disaster warning area north of National Institute of Technology, Tokuyama College in Shunan City, Yamaguchi Prefecture. The rainfall intensity and moisture content by volume were then measured in both the nonwoven filter installed and bare ground sections. As a consequence, the relationship between precipitation levels and the occurrence of slope failure was elucidated. Moreover, the rainfall seepage model proposed by Tsubogo et al. demonstrated a reduced discrepancy between the numerical solution and the observed value in comparison to the previous model. When a nonwoven filter was installed, the results of the numerical solutions exhibited a high degree of concordance with the observed value. In the bare ground section, the outcome of the before collapse numerical solution was in alignment with the observed value. However, following the collapse, this concordance exhibited a tendency to diminish.

Urban transportation systems significantly contribute to environmental degradation in rapidly urbanizing cities of South and Southeast Asia. This study evaluates the environmental effects of transportation modes in Dhaka, Karachi, Bhopal, and Jakarta utilizing critical indicators: CO₂ emissions (kg/passenger-km), PM₂.₅ concentrations (µg/m³), and noise pollution (dB). Data were aggregated from standardized environmental sources, and Pearson correlation analysis using R software was employed to examine interdependencies among measures. The results indicate that motorbikes and private vehicles impose the greatest environmental loads, emitting up to 0.25 kg CO₂ per passenger-km and 172.2 µg/m³ of PM₂.₅, together generating noise levels of 90 dB, whilst MRT systems and bicycles demonstrate negligible consequences. Significant associations (r > 0.75) were identified between PM₂.₅ and noise levels, signifying cumulative environmental stress. The research promotes a shift towards sustainable transportation methods, including mass rapid transit (MRT) and non-motorized options. The Avoid-Shift-Improve (ASI) framework is advised to direct policy initiatives. These findings offer evidence-based guidance for communities seeking to reduce the environmental impact of urban transportation and improve long-term sustainability.

Inundation has become a recurrent issue in urban areas, highlighting the need for effective mitigation strategies. Various interventions have been implemented, underscoring the importance of systematically evaluating their long-term performance. This study presents a case analysis of three rainwater retention basins in Sorocaba, São Paulo, Brazil, applying a multidimensional model to assess interventions made several years ago. Field works included water and sediment sampling, along with in situ measurements of key environmental parameters. A composite index was developed to evaluate each basin’s environmental quality and find improvement opportunities. Two basins were classified as having good environmental conditions, while one (RT basin), showed average performance due to the discharge of untreated sewage from upstream sources, negatively affecting water and sediment quality. Despite these issues, all basins demonstrated potential for ecological and social improvement. The results indicate a favorable situation for local authorities implement effective, targeted actions. For broader impact, future interventions should be strategically planned, with careful attention to site selection and infrastructure sizing, as these factors are essential for long-term sustainability and community benefit.

This study compares the bearing capacity results of piles obtained through static and dynamic load tests, using different safety factors. The Static Load Test (SLT) based on GOST standards indicated a bearing capacity of 1400 kN. After applying a safety factor of 1.2, the ultimate capacity was calculated as 1167 kN, considered as the baseline (100%) for comparison. The Cone Penetration Test (CPT) at a depth of 7 m yielded a lower bearing capacity of 930 kN, which, after applying a safety factor of 1.25, resulted in an ultimate capacity of 744 kN (63.8% of the SLT value). The Dynamic Load Test (PDA) according to ASTM standards showed the highest bearing capacity, with an average of 1799 kN. Applying a safety factor of 1.4, the ultimate capacity was determined to be 1285 kN, which is 10% higher than the SLT value (110%). This analysis underscores significant variations in pile bearing capacity depending on the testing method, with dynamic tests generally yielding higher ultimate capacities after adjusting for safety factors.

Jointed rock masses generally experience high-temperature conditions across various geological infrastructures such as nuclear waste repositories, petroleum drilling sites, hydrothermal exploration areas, etc. This study examines the heat-treated rock samples with pre-existing cracks subjected to uniaxial static loading under both ungrouted and grouted conditions. Rock samples, prepared from model rock with pre-existing cracks oriented at 30° joint orientation with horizontal, have been subjected to heat treatment ranging from 30 to 400 °C for 24 h. The rose plots show that un-grouted samples have a more uniform fracture distribution at lower temperatures, but distinct orientations emerge at higher temperatures, especially at 0°, 90°, and 180°. Cement-grouted samples consistently display higher fracture densities and more defined orientations at equivalent temperatures. The grout influences the alignment and increases fracture density due to additional stress and micro-cracks. The mechanical behaviour of the samples has been evaluated through uniaxial compression loading and fracture propagations tracked using digital image correlation (DIC) analysis using MATLAB. Results showed that samples show increased nonlinearity and micro-crack formation as temperature rises. Post-peak behaviour shifts from brittle to strain-softening with higher temperatures, indicating increased ductility. Grouting enhances peak strength below 100 °C, but strength decreases for both grouted and un-grouted samples at higher temperatures due to matrix and grout degradation. However, fracture initiation primarily occurred at the pre-existing crack tips in all samples except for those grouted at lower temperatures. Fracturing mechanisms shifted from tensile cracks to shear crack dominance with increasing temperature.

Tidal irrigation systems in peat soil areas of South Kalimantan, Indonesia, face challenges such as water stagnation and acidity accumulation, especially in regions influenced by acid sulfate soil containing pyrite (FeS₂), which can negatively affect agricultural productivity. To address this issue, the study applied long-term smart water level monitoring to analyze sulfate and pH distribution and water flow patterns using field data and hydrodynamic modelling. Water samples were collected from multiple points along the primary, secondary, and tertiary irrigation channels to measure sulfate and pH. In parallel, water level observations were conducted using a simple water level gauge built from low-cost components and deployed from May to December 2024. The recorded tidal fluctuations served as boundary conditions for hydrodynamic modeling using the Hydrologic Engineering Center’s River Analysis System (HEC-RAS). The results showed that water velocity decreased with distance from the river mouth, leading to longer hydraulic retention times (13–66 h) in downstream channels. This stagnation contributed to high sulfate accumulation (> 100 mg/L) and low pH (< 4), particularly in the tertiary channels. These findings underscore the importance of integrating smart field monitoring and flow modeling to mitigate acidification risks and support more sustainable irrigation strategies in peatland environments.