Forecasting urban water demand requires approaches that account for both environmental variability and human behavior. Traditional models rely primarily on climatic drivers and assume stable consumption patterns, overlooking the rapid behavioral shifts triggered by social events, consumers’ attention, and environmental stress. The objective of this study is to develop and evaluate a behaviorally informed, event-aware framework for short-term urban water demand forecasting that explicitly integrates physical and social drivers. The proposed framework utilizes data from weather records, Meta mobility distributions, Google Trends search activity, and demand records from the North Okanagan Valley in British Columbia, Canada. By coupling data-driven modeling with interpretable Machine Learning (ML), the framework identifies environmental thresholds and behavioral triggers influencing consumption. The results underscore that integrating digital behavior indicators with climate ones improves forecasting performance, increasing R2 from 0.79 to 0.85, while behavioral indicators alone achieve an R2 of 0.70, highlighting their independent explanatory power. These findings emphasize the value of integrating real-time behavioral proxies into operational forecasting models, supporting more adaptive and socially aware water resource management. The framework is demonstrated using a real-world case study and is designed to be transferable to other urban systems where human activity strongly influences short-term demand dynamics.
Accurate identification of stratigraphic interfaces and embedded cavities is critical for geological modeling, engineering design, and infrastructure maintenance. However, integrating geophysical and geological data remains challenging due to discrepancies in spatial resolution, signal-to-noise ratio, and geological representation. This study presents a stratigraphic U-Net model that combines geophysical and geological information to improve subsurface interpretation. An initial U-Net model is trained using a preliminary P-wave velocity model derived from conventional seismic inversion. The predicted interfaces, however, show noticeable discrepancies with borehole observations because of manual velocity picking and limited borehole data. To address these limitations, the stratigraphic U-Net model is retrained using the initial predictions together with both drilled and synthetic boreholes. This iterative strategy significantly improves the identification of stratigraphic interfaces, particularly at greater depths. The stratigraphic U-Net model is further integrated with an opening detection model to identify embedded cavities within geological layers. Model evaluation shows that geological diversity captured by borehole data contributes more to prediction accuracy than simply increasing the borehole number. A field case study demonstrates that the proposed framework accurately identifies stratigraphic interfaces and subsurface cavities, providing a robust workflow for reducing geological uncertainty and improving the spatial coverage and reliability of subsurface characterization.
Abstract This study conducts a series of vibratory compaction tests on soil-rock mixture fillers, focusing on how rock size and content influence settlement. Using models and parameters validated by unconfined compression tests and numerical simulations, a surface vibration compaction model was developed to monitor real-time settlement. Results show that final settlement decreases with increasing rock size and content. PFC3D simulations reveal void ratio variations during compaction, which correlate closely with settlement and overall compaction quality. Notably, the settlement at the compacted state follows the same trend as final settlement when rock size and content vary. The study also finds that simulated final settlement is inversely proportional to excitation frequency and directly proportional to rock content. A parameter sensitivity analysis highlights the roles of soil-rock ratio and excitation frequency, leading to a settlement prediction model. Field validation confirms the model’s high accuracy in estimating compaction quality from settlement data.
Abstract Pavement defects such as cracks and potholes compromise road safety and demand timely maintenance. Traditional manual inspection is slow and exposes workers to safety risks, whereas automated systems offer a promising alternative. This survey provides a comprehensive review of deep learning methods for road condition assessment. We first examine 2D image-based approaches, tracing their evolution from convolutional neural networks (CNNs) to Transformers. Although these methods are widely adopted, they remain sensitive to lighting conditions and cannot directly capture physical properties such as defect depth. To address these limitations, we review 3D sensing and subsurface diagnostic techniques, which provide essential geometric information for severity assessment. The primary focus of this paper is on evaluation: we summarize key public datasets and evaluation metrics and analyze the persistent gap between algorithmic performance and the practical needs of engineering, emphasizing the importance of assessing the actual utility of models in the field. Finally, we discuss several key challenges and promising research avenues, arguing that future work should prioritize model robustness, reliability, and the integration of these systems into real-world maintenance workflows.
Abstract Energy retrofits are essential for reducing building energy consumption and associated greenhouse gas (GHG) emissions. Occupancy patterns and occupant behaviors can significantly influence building energy performance and, consequently, the effectiveness of retrofit interventions. As such, it is critical to account for occupant factors when evaluating retrofit strategies. This study proposes an occupant-centric energy retrofit evaluation and optimization framework for Canadian residential buildings, grounded in a life cycle thinking approach. The research first assessed the impact of occupancy patterns and occupant behaviors on retrofit performance using a physics-based energy modeling approach. Then, energy modeling outputs were subsequently integrated with life cycle assessment and costing to evaluate the emission and cost outcomes of retrofit packages. Finally, multi-objective Pareto optimization was applied to identify optimal retrofit packages tailored to distinct occupant personas, based on trade-offs between life cycle GHG emissions and costs. Results demonstrate that cost-optimal packages achieved over $3,500 in life cycle savings per household, while emission-optimal packages reduced life cycle GHG emissions by over 113 tonnes of CO₂eq per home. The proposed framework offers a comprehensive tool for analyzing occupant-driven variability in retrofit outcomes and supports decision-making for stakeholders aiming to implement cost-effective and low-carbon retrofit programs.
Abstract Hydrogen is a versatile energy carrier that can be produced from a range of feedstocks. Therefore, decentralized hydrogen production from renewable energy sources is gaining popularity worldwide, as it is considered environmentally friendly and less energy-intensive due to the use of cleaner energy sources and proximity to end-users. The present study provides a foundation work for identifying and assessing the applicability of different renewable energy-based decentralized hydrogen production alternatives through a systematic approach. This research commenced with a comprehensive literature review on renewable-based hydrogen production routes. Then, the identified routes were evaluated using life cycle assessment (LCA) and life cycle cost analysis (LCCA). Finally, the fuel production routes were prioritized using suitable multi-criteria decision-making (MCDM) ranking methods. The results revealed that run-of-river technology scenarios showed relatively higher impacts on terrestrial ecosystems. Moreover, over 60% of the greenhouse gas emissions of hydrogen production are associated with the energy consumption during the operational phase of the electrolyzer. Solar energy-based hydrogen production with solid oxide electrolysis cells was identified as the most favourable hydrogen production route. The proposed framework is flexible and can be used to compare renewable-based hydrogen production methods under any regional conditions.
Abstract Seismic damage data for buried sewer pipelines affected by six earthquakes in Japan were analyzed to construct fragility curves considering pipe type, pipe diameter, liquefaction occurrence, and ground conditions. To verify the applicability of these fragility curves to regions outside Japan, they were applied to anticipated interplate mega-earthquake scenarios in Lima, Peru, a region of high seismic risk, to assess the seismic vulnerability of the sewer pipeline network. Under the specific Mw 8.55 earthquake scenario with rupture of the Ba-fault, the estimated length of damaged pipelines reached 94 km in CARABAYLLO and SAN MARTIN DE PORRES, and 86 km in VENTANILLA. Seven districts exhibited high damage ratios, with particularly severe damage exceeding 0.200 km/km in MI PERU, SANTA ROSA, and PUENTE PIEDRA. Considering ten interplate mega-earthquake scenarios, high-risk coastal drainage areas in CALLAO, CHORRILLOS, VILLA EL SALVADOR, and LURIN were identified and would not be detected by a single deterministic scenario. Sensitivity analyses showed that damage estimates are influenced by mesh resolution, representative seismic intensity values, proportionality factors, and microtopographic classifications. These findings highlight the importance of spatial resolution and detailed ground condition modeling when fragility curves are applied for seismic damage assessment of buried sewer pipelines.
Abstract Post-event residential building damage assessment supports reconnaissance, screening, and recovery planning following major hazard events. Recent hurricanes have generated large volumes of satellite, aerial, and street-level imagery, creating challenges not of data scarcity but of integrating heterogeneous visual and contextual information into consistent and reviewable damage classifications. This study presents a multimodal deep-learning framework, the Multimodal Swin Transformer (MMST), that combines street-view imagery with structured building and hazard attributes to classify post-hurricane residential building damage. The model is evaluated using a curated dataset derived from extensive Structural Extreme Events Reconnaissance (StEER) field reconnaissance following Hurricane Ian (2022), which incorporates human interpretation, quality control, and selective sampling across impacted communities. Results show that integrating visual features with contextual information such as building age, building value, and wind speed improves classification performance relative to image-only baselines, achieving an accuracy of 92.67%. Attention-based visualizations further enable post-hoc inspection of image regions the model weighted most heavily, supporting qualitative review of model behavior rather than physical interpretation. The proposed MMST serves as a decision-support tool to augment reconnaissance workflows and enhance the continuity of critical community infrastructure in future hurricanes.
Abstract Conventional drinking water monitoring relies on laboratory-based chemical and microbiological analyses that are often time intensive. This study evaluates a rapid optical alternative by integrating excitation–emission matrix (EEM) fluorescence spectroscopy, parallel factor analysis (PARAFAC), and machine learning to predict intact cell count (ICC) within a chlorinated drinking water distribution system. A full scale network in the Okanagan Valley, Canada was monitored, and fluorescence-derived dissolved organic matter (DOM) components were used as predictors in tree-based classification models. Fluorescence alone showed moderate skill in predicting ICC, but incorporating contextual factors (chlorine residual and turbidity) markedly improved performance. The ICC models could categorically distinguish microbial levels within the distribution system, though absolute accuracy declined at sites not seen during training, indicating a need for site-specific calibration. Notably, this work is the first to assess fluorescence-based metrics for determining ICC in drinking water distribution systems. The findings demonstrate that a unified fluorescence-PARAFAC approach can provide timely alternative measures for water quality, as it shows promise for microbial risk indication when augmented with operational data. Overall, the work highlights the potential of fluorescence-informed machine learning to bridge conventional chemical and microbiological monitoring, offering a pathway toward more responsive water quality management.
Abstract Protective energy-absorbing components must reconcile lightweight design, high dissipation capacity, and reliable performance under diverse loading conditions. This study presents a novel origami unit, whose manufacturability was verified via kinematic analysis and whose baseline quasi-static axial compression performance exceeds that of two benchmark origami geometries by up to 52% in total energy absorption (E abs ) and 62% in specific energy absorption (SEA). A hybrid optimization framework integrating XGBoost machine learning surrogate modeling and NSGA-II multi-objective algorithms efficiently identified Pareto-optimal geometric parameters, achieving more than 97% prediction accuracy for key performance metrics. Embedding aluminum-foam-filled into the optimized origami cores produced a sandwich panel whose energy absorption (E abs ), specific energy absorption (SEA), and crush force efficiency (CFE) surged by approximately 619%, 101%, and 349%, respectively, compared to the unfilled structure. Finite-element simulations accurately capture deformation stages and confirm that foam filling yields more uniform hinge formation and markedly enhanced stability. The findings provide a novel design concept and methodology for advanced protective sandwich structures in both civilian and military applications.
Abstract This study investigates the suspension of sediments accumulated in water distribution tanks during seismic sloshing. Shaking‑table tests were conducted using a small rectangular tank to examine how internal water flow and sloshing amplitude influence sediment uplift. Two experiments were performed under identical vibration conditions: one using tracers to visualize flow patterns, and another using actual sediments to evaluate suspension and resulting turbidity. When sinusoidal input at the tank’s primary sloshing frequency was applied, tracers near the bottom wall rose to the surface once the water level reached the ceiling. Sediment tests similarly showed increased suspension and turbidity. For a water depth of 600 mm, turbidity remained below the water quality standard of 5 mg/L as long as the water level rise did not exceed 126 mm. Turbidity peaked not during shaking but after oscillation ceased. The study also examined the effect of internal columns, finding that sediments beneath column bases were rapidly disturbed, producing turbidity more than twice that observed without columns. Because the seismic motions used correspond to observed ground motions, the results suggest that uncleaned distribution tanks may pose a risk of water supply interruption during earthquakes.
Abstract Increasing public transport adoption is important for reducing dependence on private vehicles, thereby helping to lower transport-related emissions and support urban climate goals. Despite substantial public investment, public transport ridership in Malaysia remains low, suggesting that infrastructure improvements alone may be insufficient to shift travel behaviour. This underscores the need to understand the behavioral and contextual factors underlying public transport adoption. This study applies the Technology Acceptance Model (TAM) to examine how perceived usefulness and perceived ease of use shape behavioral intention to use public transport, while considering trust in government as a contextual factor in public service delivery. A cross-sectional survey of 122 Malaysian university students measured perceived usefulness, perceived ease of use, trust in government, and behavioral intention. An exploratory path analysis indicated that trust in government significantly predicted both perceived ease of use and perceived usefulness, while perceived usefulness emerged as the most immediate predictor of behavioral intention. The findings suggest that increasing public transport adoption requires more than infrastructure investment alone; it also depends on building institutional trust and improving usability in ways that enhance the perceived usefulness of public transport.
Cross-camera vehicle trajectory reconstruction is essential for roadside perception systems supporting traffic analysis, safety evaluation, and infrastructure monitoring. In practical deployments, roadside cameras often exhibit partial or minimal overlaps, providing limited spatiotemporal continuity and making cross-camera association challenging. This paper proposes a global trajectory reconstruction framework designed for such roadside surveillance networks. Single-camera trajectories are first extracted and projected into a unified road-plane coordinate system through calibration, enabling metric-level comparison across views. Appearance features and motion cues are then jointly exploited in a multi-stage association strategy that integrates spatiotemporal feasibility and visual similarity. Matched trajectories are stitched and refined using quality-aware alignment and short-gap compensation to generate continuous and physically plausible cross-camera trajectories. Real-world experiments with roadside cameras, RTK-equipped vehicles, and UAV-based trajectory data demonstrate that the proposed method achieves high association accuracy and reconstruction precision in both single- and multi-vehicle scenarios while maintaining low computational overhead.
Abstract The rapid advancement of new technologies and the widespread availability of data have enabled new business paradigms in urban logistics and transportation. Although optimization theories and algorithms are well-established, a persistent gap remains between these theoretical foundations and the practical skills required to formulate models and implement effective, data-driven solutions. This positioning paper aims to bridge this gap by introducing optimization modeling frameworks and solution methodologies tailored to urban logistics and transportation applications. The paper targets researchers, students, and practitioners in non-business disciplines such as civil engineering, industrial engineering, economics, and computer science. We present several widely used data-driven optimization paradigms, including deterministic optimization with sensitivity analysis, two-stage stochastic programming, integrated simulation-optimization, and Markov decision process, and discuss how mathematical programming, statistical methods, simulation, and machine learning can support decision making. We then provide a systematic classification of urban transportation applications based on key decision characteristics, such as whether decisions are static or dynamic, deterministic or stochastic, and whether they involve interactions among multiple decision makers. Building on this classification, we propose a practical roadmap to guide the selection and implementation of appropriate optimization approaches. Illustrative applications, demonstrate the relevance and versatility of the proposed frameworks.
Abstract This study proposes a novel adaptive traffic signal control method leveraging a Deep Q-Network (DQN) and Proximal Policy Optimization (PPO) to optimize signal timing by integrating variable cell length and multi-channel state representation. A road partition formula consisting of the sum of logarithmic and linear functions was proposed. The state variables are a vector composed of three channels: the number of vehicles, the average speed, and space occupancy. The set of available signal phases constitutes the action space, and the selected phase is executed with a fixed green time. The reward function is formulated using the absolute values of key traffic state metrics—waiting time, speed, and fuel consumption. Each metric is normalized by a typical maximum value and assigned a weight that reflects its priority and optimization direction. The simulation results, using Sumo-TensorFlow-Python, demonstrate a cross-range transferability evaluation and show that the proposed variable cell length and multi-channel state representation method excels compared to fixed cell length in optimization performance.
Centrifugal compressors are commonly employed for pressure compensation in long-distance natural gas pipelines. Accurate prediction of compressor performance is essential for safety and economic considerations. However, due to the invariance of process requirements over time, operational data is usually insufficient to establish a comprehensive model that covers varying operating conditions. This paper presents a prediction method based on the long short-term memory (LSTM) network and transfer learning technique, integrating similarity laws to expand the feature domain and address the issue of limited data. In the case study, the constructed TransLSTM model, a fusion of transfer learning and LSTM, is compared with pure LSTM, Gaussian Process (GP), Gradient Boosting Decision Tree (GBDT), Random Forest (RF), and Support Vector Machine (SVM) models. Results indicate that TransLSTM achieves the highest prediction accuracy, with deviations from actual values of only 2.9
Transverse cracking is a major distress mechanism in Continuously Reinforced Concrete Pavement (CRCP), affecting ride smoothness, service life, and maintenance strategies. This research introduces a hybrid predictive framework that couples Particle Swarm Optimization (PSO) with Gradient Boosting Machine (GBM) to enhance the accuracy of transverse crack prediction in CRCP. The analysis utilized 395 records from 33 pavement sections obtained from the Long-Term Pavement Performance (LTPP) program, encompassing structural, environmental, traffic, and performance-related parameters. PSO was applied to fine-tune critical GBM hyperparameters, namely the number of iterations, learning rate, and tree depth. The optimized PSO–GBM model demonstrated excellent performance, yielding an average RMSE of 1.62 and an R2 of 0.99 under 5-fold cross-validation, surpassing benchmark models such as conventional GBM, Random Forest, Support Vector Regression (SVR), Artificial Neural Networks (ANN), and Linear Regression. Sensitivity analysis revealed that L3 thickness, L4 thickness, and Annual Average Daily Traffic (AADT) were the most significant contributors, consistent with engineering knowledge of crack development. Validation through residual distribution and equality line plots confirmed the robustness and stability of the proposed approach across varying severity levels.
Vehicular Ad Hoc Networks (VANETs) are critical for transportation safety and efficiency but face serious security challenges due to limitations of traditional cryptography and emerging cyber threats. This paper proposes a novel reconciliation-free Physical Layer Key Generation (PKG) framework based on dynamic Received Signal Strength (RSS) measurements and Triplet Networks (TN). Unlike conventional PKG schemes, the proposed approach eliminates reconciliation overhead while maintaining high entropy and strong resistance to eavesdropping. Realistic RSS is generated by integrating SUMO-based vehicular mobility with NS-3 network simulations, capturing spatiotemporal channel dynamics. The TN architecture extracts reciprocal channel features through hierarchical convolutional layers and optimized embedding spaces for secure key extraction. Experimental results show near-zero bit error rates between legitimate vehicles, high key entropy (0.69 bits/bit), and complete failure of eavesdroppers to reconstruct keys. Among 3,200 generated keys, 74.1
Despite the continued success of machine learning (ML), its indiscriminate use may not always yield the expected benefits, can waste resources, introduce unforeseen complexities, and even lead to failure. From this lens, this paper systematically addresses the following elemental questions: When should we integrate ML, and which problem-solving workflows benefit the most? Thus, this paper critically examines existing decision making frameworks and guidelines to identify their strengths and limitations in defining ML applicability. Then, through a comprehensive literature review and analysis, we outline explicit suitability criteria to assess when ML should be favored over traditional engineering methods. Based on this analysis, we propose a conceptual structured decision making framework that incorporates practical steps and checklists to guide engineers in method selection, model development, validation, and deployment. Additionally, this paper highlights critical challenges and potential future research questions.
The increasing frequency and intensity of extreme weather events, coupled with rapid urbanization, are placing unprecedented pressure on urban environments. The Three-Point Sponge Policy Approach (3PSPa) integrates the Three-Point Approach (3PA) with Sponge City principles to enhance urban flood resilience through design-driven, adaptive, and multifunctional solutions. This paper applies the 3PSPa framework to Zhengzhou, China, which experienced devastating flooding in 2021, to assess its current flood resilience measures and explore pathways for improvement. Two contrasting urban districts in Zhengzhou were analysed: B1, a newly developed, low-density district offering high potential for implementing large-scale blue-green-grey infrastructure (BGGI); and B2, a dense, older district where interventions are limited, typically emerging incrementally through targeted retrofitting associated with urban renewal activities. By applying the 3PSPa’s five-step design process, this study identifies resilience gaps and proposes tailored interventions, balancing short-term, localized strategies with long-term, catchment-wide transformations. It emphasizes the importance of shifting from a conveyance-based water management approach to a diversified strategy that integrates infrastructure with natural hydrological processes.
