Search Results (1,714)

Coupled grey and green infrastructure (CGGI) offers a promising pathway toward sustainable stormwater management in historic urban environments. This study compares CGGI and conventional grey infrastructure (GREI)-only strategies across four degrees of layout centralization (0%, 33.3%, 66.7%, and 100%) in the Quanzhou West Street Historic Reserve, China. Using a multi-objective optimization framework integrating SWMM simulations, life-cycle cost (LCC) modeling, and resilience metrics, we found that the decentralized CGGI layouts reduced the total LCC by up to 29.6% and required 60.7% less green infrastructure (GI) area than centralized schemes. Under nine extreme rainfall scenarios, the GREI-only systems showed slightly higher technical resilience (Tech-R: max 99.6%) than CGGI (Tech-R: max 99.1%). However, the CGGI systems outperformed GREI in operational resilience (Oper-R), reducing overflow volume by up to 22.6% under 50% network failure. These findings demonstrate that decentralized CGGI provides a more resilient and cost-effective drainage solution, well-suited for heritage districts with spatial and cultural constraints. Full article

Slope stability assessment is a critical component of engineering safety. Conventional analytical methods frequently struggle to integrate heterogeneous slope data and model intricate failure mechanisms, thereby constraining their efficacy in practical engineering scenarios. To tackle these issues, this study presents a novel slope stability classification model grounded in the Optuna-TPE-CatBoost framework. By leveraging the Tree-structured Parzen Estimator (TPE) within the Optuna framework, the model adaptively optimizes CatBoost hyperparameters, thus enhancing prediction accuracy and robustness. It incorporates six key features—slope height, slope angle, unit weight, cohesion, internal friction angle, and the pore pressure ratio—to establish a comprehensive and intelligent assessment system. Utilizing a dataset of 272 slope cases, the model was trained with k-fold cross-validation and dynamic class imbalance strategies to ensure its generalizability. The optimized model achieved impressive performance metrics: an area under the receiver operating characteristic curve (AUC) of 0.926, an accuracy of 0.901, a recall of 0.874, and an F1-score of 0.881, outperforming benchmark algorithms such as XGBoost, LightGBM, and the unoptimized CatBoost. Validation via engineering case studies confirms that the model accurately evaluates slope stability across diverse scenarios and effectively captures the complex interactions between key parameters. This model offers a reliable and interpretable solution for slope stability assessment under complex failure mechanisms. Full article

Preventing progressive collapse induced by accidental events poses a critical challenge in the design and construction of resilient structures. While substantial progress has been made in planar structures, the progressive collapse mechanisms of precast concrete spatial structures—particularly regarding the effects of precast slabs—remain inadequately explored. This study develops a refined finite element modeling approach to investigate progressive collapse mechanisms in fully bonded prestressed precast concrete (FB-PPC) spatial frames, both with and without precast slabs. The modeling approach was validated against available test data from related sub-assemblies, and applied to assess the collapse performance. A series of pushdown analyses were conducted on the spatial frames under various column removal scenarios. The load–displacement curves, slab contribution, and failure modes under different conditions were compared and analyzed. A simplified energy-based dynamic assessment was additionally employed to offer a rapid estimation of the dynamic collapse capacity. The results show that when interior or side columns fail, the progressive collapse process can be divided into the beam action stage and the catenary action (CA) stage. During the beam action stage, the compressive membrane action (CMA) of the slabs and the compressive arch action (CAA) of the beams work in coordination. Additionally, the tensile membrane action (TMA) of the slabs strengthens the CA in the beams. When the corner columns fail, the collapse stages comprise the beam action stage followed by the collapse stage. Due to insufficient lateral restraints around the failed column, the development of CA is limited. The membrane action of the slabs cannot be fully mobilized. The contribution of the slabs is significant, as it can substantially enhance the vertical resistance and restrain the lateral displacement of the columns. The energy-based dynamic assessment further reveals that FB-PPC spatial frames exhibit high ductility and residual strength following sudden column removal, with dynamic load–displacement curves showing sustained plateaus or gentle slopes across all scenarios. The inclusion of precast slabs consistently enhances both the peak load capacity and the residual resistance in dynamic collapse curves. Full article

To address the inefficiency of traditional concrete crack detection methods and the heavy reliance of supervised learning on extensive labeled data, in this study, an intelligent assessment method of concrete damage based on pseudo-label semi-supervised learning and fractal geometry theory is proposed to solve two core tasks: one is binary classification of pixel-level cracks, and the other is multi-category assessment of damage state based on crack morphology. Using three-channel RGB images as input, a dual-path collaborative training framework based on U-Net encoder–decoder architecture is constructed, and a binary segmentation mask of the same size is output to achieve the accurate segmentation of cracks at the pixel level. By constructing a dual-path collaborative training framework and employing a dynamic pseudo-label refinement mechanism, the model achieves an F1-score of 0.883 using only 50% labeled data—a mere 1.3% decrease compared to the fully supervised benchmark DeepCrack (F1 = 0.896)—while reducing manual annotation costs by over 60%. Furthermore, a quantitative correlation model between crack fractal characteristics and structural damage severity is established by combining a U-Net segmentation network with the differential box-counting algorithm. The experimental results demonstrate that under a cyclic loading of 147.6–221.4 kN, the fractal dimension monotonically increases from 1.073 (moderate damage) to 1.189 (failure), with 100% accuracy in damage state identification, closely aligning with the degradation trend of macroscopic mechanical properties. In complex crack scenarios, the model attains a recall rate (Re = 0.882), surpassing U-Net by 13.9%, with significantly enhanced edge reconstruction precision. Compared with the mainstream models, this method effectively alleviates the problem of data annotation dependence through a semi-supervised strategy while maintaining high accuracy. It provides an efficient structural health monitoring solution for engineering practice, which is of great value to promote the application of intelligent detection technology in infrastructure operation and maintenance. Full article

Achieving the construction sector’s dual carbon objectives necessitates scaling green energy adoption in new residential buildings. The current literature critically overlooks four unresolved problems: oversimplified penalty mechanisms, ignoring escalating regulatory costs; static subsidies misaligned with market maturity evolution; systematic exclusion of innovation feedback from energy suppliers; and underexplored behavioral evolution of building owners. This study establishes a government–suppliers–owners evolutionary game framework with dynamically calibrated policies, simulated using MATLAB multi-scenario analysis. Novel findings demonstrate: (1) A dual-threshold penalty effect where excessive fines diminish policy returns due to regulatory costs, requiring dynamic calibration distinct from fixed-penalty approaches; (2) Market-maturity-phased subsidies increasing owner adoption probability by 30% through staged progression; (3) Energy suppliers’ cost-reducing innovations as pivotal feedback drivers resolving coordination failures, overlooked in prior tripartite models; (4) Owners’ adoption motivation shifts from short-term economic incentives to environmentally driven decisions under policy guidance. The framework resolves these gaps through integrated dynamic mechanisms, providing policymakers with evidence-based regulatory thresholds, energy suppliers with cost-reduction targets, and academia with replicable modeling tools. Full article

The movement towards low-emission and sustainable building practices has driven increased use of natural, carbon-based materials such as wood. While these materials offer significant environmental advantages, their inherent flammability introduces new challenges for timber building safety. Despite advancements in fire protection standards and building regulations, the risk of fire incidents—whether from technical failure, human error, or intentional acts—remains. The rapid detection of fire onset is crucial for safeguarding human life, animal welfare, and valuable assets. This study investigates the potential of monitoring fire precursor gases emitted inside building structures during pre-ignition and early combustion stages. The research also examines the sensitivity and effectiveness of commercial smoke detectors compared with custom sensor arrays in detecting these emissions. A representative structural sample was constructed and subjected to a controlled fire scenario in a laboratory setting, providing insights into the integration of gas sensing technologies for enhanced fire resilience in sustainable building systems. Full article

The reliability of unmanned aerial vehicle (UAV) energy storage battery systems is critical for ensuring their safe operation and efficient mission execution, and has the potential to significantly advance applications in logistics, monitoring, and emergency response. This paper reviews theoretical and technical advancements in UAV battery reliability, covering definitions and metrics, modeling approaches, state estimation, fault diagnosis, and battery management system (BMS) technologies. Based on international standards, reliability encompasses performance stability, environmental adaptability, and safety redundancy, encompassing metrics such as the capacity retention rate, mean time between failures (MTBF), and thermal runaway warning time. Modeling methods for reliability include mathematical, data-driven, and hybrid models, which are evaluated for accuracy and efficiency under dynamic conditions. State estimation focuses on five key battery parameters and compares neural network, regression, and optimization algorithms in complex flight scenarios. Fault diagnosis involves feature extraction, time-series modeling, and probabilistic inference, with multimodal fusion strategies being proposed for faults like overcharge and thermal runaway. BMS technologies include state monitoring, protection, and optimization, and balancing strategies and the potential of intelligent algorithms are being explored. Challenges in this field include non-unified standards, limited model generalization, and complexity in diagnosing concurrent faults. Future research should prioritize multi-physics-coupled modeling, AI-driven predictive techniques, and cybersecurity to enhance the reliability and intelligence of battery systems in order to support the sustainable development of unmanned systems. Full article

Operations management personnel emphasize the maintenance of workforce empowerment on the shop floor. This is made possible by implementing effective operations and human resource management practices. However, organizations are adept at controlling the workforce empowerment domain within operational scenarios. In the current industry revolution scenario, industry personnel often face failure due to a laggard mindset in the face of industry revolutions. There are higher possibilities of failure because of standardized operations controlling the shop floor. Organizations utilize well-established human resource concepts, including McClelland’s acquired needs theory, Herzberg’s two-factor theory, and Maslow’s hierarchy of needs, in order to enhance the workforce’s performance on the shop floor. Current SME individuals require fast-paced approaches for tracking the performance and idleness of a workforce in order to control them more efficiently in both flexible and transformational stages. The present study focuses on investigating the parameters and factors that contribute to workforce empowerment in an industrial revolution scenario. The present research is used to develop a framework utilizing operations and human resource management approaches in order to identify and address the issues responsible for deteriorating workforce contributions. The framework includes HRM and operations management practices, including Herzberg’s two-factor theory, Maslow’s theory, and lean and smart approaches. The developed framework contains four phases for achieving desired outcomes on the shop floor. The developed framework is validated by implementing it in a real-life electric vehicle manufacturing organization, where the human resources and operations team were exhausted and looking to resolve employee-related issues instantly and establish a sustainable work environment. The current industry is transforming from Industry 3.0 to Industry 4.0, and seeks future-ready innovations in operations, control, and monitoring of shop floor setups. The operations management and human resource management practices teams reviewed the results over the next three months after the implementation of the developed framework. The results revealed an improvement in workforce empowerment within the existing work environment, as evidenced by reductions in the number of absentees, resignations, transfer requests, and medical issues, by 30.35%, 94.44%, 95.65%, and 93.33%, respectively. A few studies have been conducted on workforce empowerment by controlling shop floor scenarios through modifications in operations and human resource management strategies. The results of this study can be used to fulfil manufacturers’ needs within confined constraints and provide guidelines for efficiently controlling workforce performance on the shop floor. Constraints refer to barriers that have been decided, including production time, working time, asset availability, resource availability, and organizational policy. The study proposes a decision-making plan for enhancing shop floor performance by providing suitable guidelines and an action plan, taking into account both workforce and operational performance. Full article

Vision-based end-to-end navigation systems have shown impressive capabilities, especially when combined with Imitation Learning (IL) and advanced Deep Learning architectures, such as Transformers. One such example is CIL++, a Transformer-based architecture that learns to map navigation states to vehicle controls based on expert demonstrations only. Nevertheless, reliance on experts’ datasets limits generalization and can lead to failures in unknown circumstances. Deep Reinforcement Learning (DRL) can address this issue by fine-tuning the pretrained policy, using a reward function that aims to improve its weaknesses through interaction with the environment. However, fine-tuning with DRL can lead to the Catastrophic Forgetting (CF) problem, where a policy forgets the expert behaviors learned from the demonstrations as it learns to optimize the new reward function. In this paper, we present CILRLv3, a DRL-based training method that is immune to CF, enabling pretrained navigation agents to improve their driving skills across new scenarios. Full article

Early evacuation during bushfires remains the safest strategy; however, in many realistic scenarios, timely evacuation is challenging, making safe sheltering a last-resort option to reduce risk compared to late evacuation attempts. However, most Australian homes in bushfire-prone areas are neither designed nor retrofitted to provide adequate protection against extreme bushfires, raising safety concerns. This study addresses this gap by investigating the concept of retrofitting a part of the residential buildings as attached safe rooms for sheltering and protection of valuables, providing a potential last-resort solution for bushfire-prone communities. Numerical simulations were conducted using the Fire Dynamics Simulator to assess heat transfer and internal temperature conditions in a representative residential building under bushfire exposure conditions. The study investigated the impact of the placement of the safe room relative to the fire front direction, failure of vulnerable building components, and the effectiveness of steel shutters in response to internal temperatures. The results showed that the strategic placement of safe rooms inside the building, along with adequate protective measures for windows, can substantially reduce internal temperatures. The findings emphasised the importance of maintaining the integrity of openings and the external building envelope, demonstrating the potential of retrofitted attached safe rooms as a last-resort solution for existing residential buildings in bushfire-prone areas where the entire building was not constructed to withstand bushfire conditions. Full article

Unexpected machine failures in industrial environments lead to high maintenance costs, unplanned downtime, and safety risks. This study proposes a proactive predictive model using a hybrid of eXtreme Gradient Boosting (XGBoost) and Neural Networks (NN) to forecast machine failures. A synthetic dataset capturing recent breakdown history and time since last failure was used to simulate industrial scenarios. To address class imbalance, SMOTE and class weighting were applied, alongside a focal loss function to emphasize difficult-to-classify failures. The XGBoost model was tuned via GridSearchCV, while the NN model utilized ReLU-activated hidden layers with dropout. Evaluation using stratified 5-fold cross-validation showed that the NN achieved an F1-score of 0.7199 and a recall of 0.9545 for the minority class. XGBoost attained a higher PR AUC of 0.7126 and a more balanced precision–recall trade-off. Sample predictions demonstrated strong recall (100%) for failures, but also a high false positive rate, with most prediction probabilities clustered between 0.50–0.55. Additional benchmarking against Logistic Regression, Random Forest, and SVM further confirmed the superiority of the proposed hybrid model. Model interpretability was enhanced using SHAP and LIME, confirming that recent breakdowns and time since last failure were key predictors. While the model effectively detects failures, further improvements in feature engineering and threshold tuning are recommended to reduce false alarms and boost decision confidence. Full article

Flooding due to dam failures is a critical issue with significant impacts on human safety, infrastructure, and the environment. This study assessed the potential flood hazard that could be generated from breaching of the Alacranes dam in Villa Clara, Cuba. Thirteen reservoir breaching scenarios were simulated under several criteria for modeling the flood wave through the 2D Saint Venant equations using the Hydrologic Engineering Center’s River Analysis System (HEC-RAS). A sensitivity analysis was performed on Manning’s roughness coefficient, demonstrating a low variability of the model outputs for these events. The results show that, for all modeled scenarios, the terrain topography of the coastal plain expands the flood wave, reaching a maximum width of up to 105,057 km. The most critical scenario included a 350 m breach in just 0.67 h. Flood, velocity, and hazard maps were generated, identifying populated areas potentially affected by the flooding events. The reported depths, velocities, and maximum flows could pose extreme danger to infrastructure and populated areas downstream. These types of studies are crucial for both risk assessment and emergency planning in the event of a potential dam breach. Full article

Background/Objectives: Sunk cost bias—continuing failing endeavours due to prior investments—is among the most studied decision-making biases. Despite decades of vignette-based research, these measures lack systematic psychometric validation. We examined whether widely-used sunk cost scenarios reliably measure the same psychological construct. Methods: Across two experiments (N = 395), we tested established sunk cost vignettes, including classic scenarios from Arkes and Blumer (1985). English-speaking participants from Prolific Academic completed vignettes alongside cognitive reflection and social desirability measures. We assessed internal consistency and intercorrelations between scenarios. Results: Internal consistency was consistently poor (ω = 0.14–0.57) with weak intercorrelations between scenarios. Even highly similar vignettes correlated only moderately. External validity was problematic, showing inconsistent relationships with cognitive reflection and social desirability across vignettes. Conclusions: These measurement failures have critical implications for neuroimaging research, where unreliable behavioural measures may be mistaken for genuine neural differences. The field needs systematic categorization of scenarios to identify which vignettes engage specific psychological processes and neural circuits, enabling more targeted theoretical development. Full article

Unmanned aerial vehicles (UAVs), commonly known as drones, offer unprecedented flexibility for complex missions such as area surveillance, search and rescue, and cooperative inspection. This paper presents a unified evaluation framework for the comparison of centralized and distributed task allocation algorithms specifically tailored to multi-UAV operations. We first contextualize the classical assignment problem (AP) under UAV mission constraints, including the flight time, propulsion energy capacity, and communication range, and evaluate optimal one-to-one solvers including the Hungarian algorithm, the Bertsekas $ϵ$-auction algorithm, and a minimum cost maximum flow formulation. To reflect the dynamic, uncertain environments that UAV fleets encounter, we extend our analysis to distributed multi-UAV task allocation (MUTA) methods. In particular, we examine the consensus-based bundle algorithm (CBBA) and a distributed auction 2-opt refinement strategy, both of which iteratively negotiate task bundles across UAVs to accommodate real-time task arrivals and intermittent connectivity. Finally, we outline how reinforcement learning (RL) can be incorporated to learn adaptive policies that balance energy efficiency and mission success under varying wind conditions and obstacle fields. Through simulations incorporating UAV-specific cost models and communication topologies, we assess each algorithm’s mission completion time, total energy expenditure, communication overhead, and resilience to UAV failures. Our results highlight the trade-off between strict optimality, which is suitable for small fleets in static scenarios, and scalable, robust coordination, necessary for large, dynamic multi-UAV deployments. Full article

This study investigates the flow dynamics and local scour around a Reef Cube^® artificial reef deployed in Torbay, UK, using computational fluid dynamics. The flow is modelled using Reynolds-Averaged Navier–Stokes (RANS) equations with a k-ω SST turbulence model. A novel hydro-morphodynamic model employing the generalized internal boundary method in HELYX (OpenFOAM-based) is used to simulate scour development. Model performance was validated against experimental data for flow fields, bed shear stress, and local scour. Flow simulations across various scenarios demonstrated that parameters such as the orientation angle and arrangement of Reef Cubes significantly influence flow patterns, bed shear stress, and habitat suitability. The hydro-morphodynamic model was used to simulate scouring around a reef cube in the Torbay marine environment. Results indicate that typical tidal flow velocity flow in the region is barely sufficient to initiate sediment motion, whereas extreme flow events, represented by doubling the mean flow velocity, significantly accelerate scour development, producing holes up to ten times deeper. These findings underscore the importance of considering extreme flow conditions in scour analyses due to their potential impact on the stability and failure risk of AR projects. Full article