Urban road networks in megacities face increasing vulnerability to disruptions that can severely impair mobility and accessibility. Assessing the resilience of such systems requires identifying critical links whose congestion/failures disproportionately degrade network performance. However, conventional link-criticality analysis (LCA) approaches often require exhaustive traffic reassignment, making them computationally impractical for large-scale applications. This study introduces a scalable, data-driven framework for LCA that integrates deep learning (DL) and global sensitivity analysis (GSA) to efficiently quantify link-level vulnerability in metropolitan road networks. The framework formulates LCA as a mapping from network topology, geometric attributes, and traffic-demand features to a composite criticality index that captures both operational degradation and accessibility loss. A DL model is trained on simulation-based disruption scenarios to approximate the impact of network performance, thereby replacing computationally intensive traffic assignment procedures. Subsequently, GSA is applied to interpret the influence and interactions of the underlying factors that drive criticality predictions. The proposed approach is demonstrated on the Greater Cairo road network using a detailed, multi-class model calibrated with observed traffic data. Results show that the DL surrogate accurately reproduces network efficiency and travel-time loss metrics while achieving orders-of-magnitude reductions in computation time compared with traditional LCA. GSA results highlight a limited subset of topologically central and highly loaded corridors as primary drivers of system vulnerability, revealing non-linear interactions among capacity, redundancy, and demand patterns. These findings underscore the potential of combining DL with GSA for interpretable, scalable, and policy-relevant LCA supporting resilient transport planning, investment prioritization, and emergency response in complex urban systems.

Improving the operational effectiveness of water structures requires effective hydraulic design. Significant changes in water levels, velocity distribution, heading up, and energy loss occur at the entrance zone of these structures because of the interaction between the canal inside slope and upstream wing walls geometry. However, prior research has not adequately assessed the integrated hydraulic effects of these characteristics. This study experimentally examines the interplay between wing wall type and canal inside slope on the hydraulic performance of water structures. A total of 360 laboratory experiments were conducted using four upstream wing wall configurations (box, broken, curved, and splayed) and three canal inside slopes (Z = H:V = 1:1, 3:2, and 2:1), representing common conditions in Egyptian irrigation canals. The findings show that hydraulic behavior is strongly influenced by both parameters. When compared to the box type, the splayed configuration exhibited the lowest energy losses and afflux, with reductions of up to 84.12% and 30.01%, respectively. Furthermore, steeper slopes were linked to higher afflux and energy losses, while the 1:1 canal inside slope continuously exhibited maximum hydraulic efficiency. New empirical relationships were developed to enable the prediction and optimization of irrigation structure performance. The outcomes support sustainable water management by increasing operational dependability, lowering maintenance requirements, and increasing hydraulic efficiency. For both new construction and rehabilitation projects, the study suggests giving priority to splayed wing walls in conjunction with a 1:1 canal inside slope, considering the soil conditions.

Powder Bed Fusion (PBF) is a growing and expanding technology for Additive Manufacturing (AM) of metallic materials. PBF has numerous advantages, including near-net-shaped parts, low tooling costs, and the ability to tailor microstructures, which make it an ideal candidate for manufacturing extremely complex designs that can be used in a variety of high-tech industries. Despite these enormous benefits, residual stress, and distortion, arising from a substantial cooling rate, temperature gradient in the melt pool and the layer-by-layer nature of the process, represent a challenge for the implementation of PBF technology in metal AM more broadly. Residual stress has a significant impact on the dimensional accuracy of printed parts and leads to severe defects such as cracks, delamination, and distortion. Conducting trial-and-error real AM build iterations to predict the developed residual stress and finding a strategy to mitigate its adverse effect is a time consuming, costly, and challenging process. Computational-based models offer an effective means to accurately predict residual stress, while reducing costs and saving time. This article discusses the fundamental principles underlying the numerical models used to predict residual stress. It investigates and presents their principles in a systematic manner, facilitating an in-depth understanding of these various numerical models. This deep investigation not only establishes a solid foundation for the improvement of existing models but also highlights the potential for the development of novel, more efficient numerical models for predicting residual stress in PBF. The review examines the experimental techniques used to validate these numerical approaches and clarifies how they can be used to validate residual stress prediction models. Furthermore, the potential application and integration of machine learning in conjunction with numerical models to predict residual stress and suggest effective mitigation strategies are also discussed. The review demonstrates how simulation can be used as an effective tool to efficiently identify suitable residual stress mitigation strategies in the PBF AM process.

SiC is susceptible to decomposition into Silicon and Carbon during. D-LPBF when exposed to temperatures above its decomposition threshold. This study investigates the impact of various laser scanning strategies; linear, concentric in-out, zigzag, and hexagonal patterns. The temperature history effects of these scanning strategies were investigated to observe the SiC degradation and its impact on the phase composition. A thermal simulation model was developed to analyse temperature evolution under each scanning pattern. Results showed that concentric in-out, zigzag, and hexagonal strategies involving frequent changes in laser direction generated localised high-temperature peaks. These peaks enhanced layer bonding but also triggered partial decomposition of SiC. However, the linear strategy lacked sufficient thermal peaks, resulting in poor interlayer adhesion and overlapping defects. Quantitative phase …

Evaluating line criticality in public transport networks is computationally challenging due to overlapping routes, frequency-based operations, and the combinatorial growth of multi-line disruption scenarios. Traditional approaches, whether topology-based or reliant on repeated equilibrium assignments, do not scale to realistic networks, where full-scan or multi-failure enumeration can require thousands of computationally intensive assignments. This study presents an innovative, computationally efficient framework that integrates a section-based transit assignment model with Mutual Information–based Global Sensitivity Analysis (MISA) to rank individual line criticality without simulating any failure scenarios. Stochastic O–D demand samples are generated using Monte Carlo methods, and equilibrium flows are computed once per scenario using a variational inequality formulation on an augmented network representation. The resulting dataset is analyzed using information-theoretic sensitivity indices, capturing nonlinear and interaction effects while reducing the computational burden by several orders of magnitude compared to brute-force multi-line failure analysis. Application to a benchmark network demonstrates that the proposed method reliably identifies lines whose operational roles and network embeddings make them most influential for system-wide performance. The framework offers a practical, scalable solution for infrastructure planners seeking data-driven tools for resilient public transport design and prioritization under uncertainty.

This study presents an integrated modeling approach to evaluate pedestrian satisfaction in new urban cities characterized by rapid growth and limited multimodal connectivity. A structured questionnaire, distributed to stratified participants across residential, administrative, and service zones, captured user perceptions of 13 key urban design features, including safety, accessibility, visual coherence, and economic vibrancy. Descriptive statistics and visual analytics revealed that accessibility, protection from crime and traffic, and urban aesthetics were strong correlates of satisfaction. To model these relationships quantitatively,the study employed both ordinal and multinomial logistic regression, with the latter achieving 92.45% classification accuracy. K-means clustering and principal component analysis further uncovered latent user typologies, highlighting the heterogeneity of pedestrian priorities. Local and global sensitivity analyses, including mutual information metrics, identified easy access, protection from traffic, and crime prevention as the most influential features. Response surface modeling illustrated nonlinear interactions among key variables, emphasizing the multidimensional and synergistic nature of satisfaction outcomes. The findings showed that pedestrian experience is shaped not by isolated design features, but by their interactive effects across spatial, psychological, and infrastructural domains. The study offers actionable insights for human-centered urban design, while the presented analytical framework is scalable and supports evidence-based interventions in emerging urban contexts.

Land surveying is considered one of the main activities in executing construction projects. Therefore, it is exposed to various risks, while the most important of which are environmental risks. For that, this study addresses and identifies the expected environmental risk factors associated with Land Surveying Execution (LSE) in construction projects. Twenty three environmental risk factors influencing LSE are identified under three main categories including atmospheric and climatic conditions category, topographical features and physical site barriers category, and logistical, operational, regulatory, and data integrity category. Identification and qualification analyses are conducted to define the priorities of risk factors affecting the cost of LSE which represents the main objective of such projects. Further, the risk breakdown structure is developed. Data concerning the characteristics of the identified risks and the probability of occurrence and impact of risk factors on LSE cost is collected and analyzed. The results showed that a risk factor related to “High traffic or dense urban areas causing operational delays” is the most likely to be occurred in these projects. Further, a risk factor which is related to “Marker movement due to ground subsidence or landslide risk”, is the most factor with impact on LSE cost. The combined effect of the probability and impact is calculated in the form of severity for all risk factors, and the highest severity value is for “High traffic or dense urban areas causing operational delays”. The third category is found to be the riskiest one because it has the largest number of crucial risk factors and has the uppermost cumulative and average cost severity values.

Green building (GB) projects in the Middle East encounter several causes of waste during design and construction phases, impacting their economic, environmental, and social goals. This study aims to identify these causes of waste and assess their impact on project objectives. Forty-five causes are identified and classified into five categories: G01) Green Materials, (G02) Green Building Design, (G03) Sustainable Site, (G04) Green Building Technologies, and (G05) Green Building Stakeholders. Field surveys, including semi-structured interviews, brainstorming sessions, and a questionnaire, are conducted to evaluate each cause's impact on GB goals. The findings reveal that the most significant cause of waste, with the highest influence on GB objectives, is “Poor assessment of site conditions before design, such as topography, hydrology, climate, vegetation, and soil.” Additionally, the cause of waste, which refers to the Lack of experience of green building designers, contractors, subcontractors, and suppliers in executing green buildings, is also a critical factor, as it highly affects the project objectives. This study helps project managers identify the key causes of waste in green building projects during the design and construction phases and develop strategies to minimize their effects. In addition, designers and decision-makers can use these insights to ensure they meet the requirements of various green building rating systems such as LEED (Leadership in Energy and Environmental Design).