Search Results (119)

This study proposes a barrier-oriented application of conventional failure mode and effects analysis (FMEA) and fuzzy weighted geometric mean (FWGM)-based fuzzy-FMEA for laboratory-scale solvent-based electrospinning. The process was decomposed into 14 sequential steps, and one representative failure mode was defined for each step. Severity, occurrence, and detection were rated by a five-member expert panel, and hazard-type-specific weights were assigned to chemical-dominant, electrical-dominant, fire/static-dominant, and combined-dominant hazards. Conventional FMEA identified material review/approval, equipment setup, pre-start inspection, and response to abnormalities as the highest-risk steps (RPN = 60). FWGM-based fuzzy-FMEA re-ranked tied RPN groups and identified response to abnormalities and equipment setup as the joint highest-FRPN failure modes (FRPN = 79.35), followed by pre-start inspection (77.39) and material review/approval (75.89). Barrier-oriented interpretation revealed four dominant mechanisms: upstream information-based hazards, direct high-voltage access, pre-start combined hazards, and intervention under abnormal or residual-energy states. Scenario-based post-control analysis showed that grounded enclosures, interlocks, de-energize-discharge-verify procedures, pre-start checklists, and bonding/grounding measures reduced FRPN by 25.88–43.79% for prioritized failure modes. The proposed framework supports SOP development, equipment improvement, training prioritization, and laboratory risk-assessment documentation for solvent-based nanofiber manufacturing. Full article

Firefighting and urban search operations occur in hazardous, rapidly changing environments where timely situational awareness is critical. In indoor firefighting scenarios, responders often operate in smoke-filled and structurally complex environments with limited visibility and communication. While UAVs have been widely used in wildfire response, their deployment inside buildings remains limited due to constraints in system mass, cost, and operational complexity. This paper presents the design and preliminary validation of an attritable micro-UAV as a proof-of-concept platform for indoor search support and post-fire inspection and assessment. The platform emphasizes portability, durability, and multi-sensor integration, enabling deployment by minimally trained personnel. System requirements were derived in collaboration with the Southfield Fire Department. The finalized design achieved a total mass of 247.34 g at a cost of $2969. Experimental evaluation demonstrated reliable sensing and communication performance at the subsystem level and confirmed structural robustness through drop tests from heights up to 3 m. Endurance testing yielded a maximum flight time of 28 min, slightly below the targeted 30 min requirement. While full task-level validation in operational firefighting scenarios has not been conducted, the proposed platform establishes a foundation for future development, including system-level validation, post-fire structural assessment, and enhanced visualization interfaces for improved situational awareness in emergency response operations. Full article

Building fires involve numerous interacting hazard factors, making it difficult to identify which combinations are most likely to cause an incident and to design targeted interventions. Existing methods address only part of this problem: structural approaches map causal pathways but cannot quantify the probability of specific factor combinations, while probabilistic models compute risk values but offer little guidance on where to intervene. To bridge this gap, we develop the Causal Hierarchy Model (CHM), a data-driven framework that integrates causal structure analysis with probability calculation. Factor influence is derived from empirical co-occurrence data to distinguish driving factors from dependent ones. A hierarchical structure is then constructed using two layering rules, revealing causal transmission gradients and critical hub nodes. Finally, coupling probabilities are computed within the hierarchical constraints and weighted by the influence of hubs. Applying CHM to building fire records from California reveals clear functional differentiation among hazard factors. Coupling strength attenuates asymmetrically across hierarchy levels but amplifies sharply along pathways that pass through high-prominence hubs. By uniting structure and probability, CHM provides a quantitative basis for shifting fire safety management from uniform inspection toward risk-differentiated strategies. Full article

Explainable artificial intelligence is increasingly needed in high-stakes tabular classification, where predictions should be accurate, auditable, and easy to inspect. We propose GRS-ANFIS, a role-separated neuro-fuzzy model that decomposes inference into a Primary module for main decision formation and a Complementary module for targeted correction. During differentiable training, sigmoid gate values are applied only to consequent coefficients, while the antecedent part receives the original input without soft masking. After each stage, the learned gates are binarized into hard routing masks that define discrete antecedent and consequent subsets for module-specific fine-tuning. The Complementary module is restricted to variables not selected by the Primary module, yielding explicit role separation and disjoint variable usage across modules. To support stable learning in high-dimensional settings, all ANFIS-family models use the same HTSK-style firing computation. Experiments on four tabular benchmarks show that GRS-ANFIS achieves competitive predictive performance while maintaining compact, role-separated rule structures; rule-count compactness is clear, whereas the unified Nauck/HFSi interpretability values are dataset- and variant-dependent. Boundary-focused analysis further shows that the Complementary module mainly improves difficult, low-confidence samples through targeted correction. Full article

Building fire safety inspection is a knowledge-intensive engineering task that requires reliable hazard recognition under complex visual conditions, limited labeled data, and strict regulatory accountability. To address these challenges, this paper proposes a coarse-to-fine intelligent inspection framework for building fire hazard recognition and regulation-grounded reporting. The framework first performs binary hazard screening and then refines positive or uncertain cases into specific hazard categories, thereby aligning the inference process with practical inspection workflows. A self-supervised DINOv2 Vision Transformer is adopted as the visual backbone, and a small-sample adaptation strategy is developed by combining staged fine-tuning, a lightweight SE-based classification head, and task-aligned knowledge distillation. In addition, an Agentic RAG compliance layer is introduced to retrieve, verify, and present clause-level regulatory evidence while suppressing hallucinated or unverifiable citations. Experiments on a real-world building fire hazard image dataset show that the proposed framework achieves stable recognition performance, outperforms representative CNN-, supervised Transformer-, and self-supervised Transformer-based baselines, and improves the faithfulness of regulation-grounded reporting. The results suggest that the proposed framework provides a feasible prototype-level pathway toward intelligent and auditable fire safety inspection, while broader multi-site validation and robustness evaluation remain necessary for future deployment. Full article

The integration of salt removal and structural consolidation remains a major challenge in heritage brick conservation. This research proposes a preliminary experimental setup for a dual-function microbial strategy using a single bacterium, Stutzerimonas stutzeri D1, capable of sequential denitrification (biocleaning) and ureolysis-driven microbially induced calcium carbonate precipitation (biocementation). After the pre-check assessment, which compared standalone, simultaneous, and sequential metabolic configurations, sequential denitrification followed by ureolysis (A→B) optimized functional compatibility, achieving 90.1% nitrate removal within 48 h and the highest precipitation rate during the biocementation phase. Application on authentic demolition waste (solid fired-clay brick specimens) demonstrated highly efficient nitrate reduction, alkalization (from pH value of 6.4 to 9.12), surface mineral deposition confirmed by visual inspection, SEM imaging, and XRD analysis. Furthermore, reduced water absorption (by 30%) and improved compressive strength (by 25%) for only 72 h of this dual treatment indicate a promising and holistic approach in the field of construction biotechnology of heritage brick conservation. These pioneer findings demonstrate that metabolic sequencing governs compatibility in dual-function bacterial systems and validate a sustainable, single-strain platform for combined biocleaning and biocementation of historic brick masonry structures. Full article

To address challenges such as the sudden onset of urban fires, data synchronization delays in early warning systems, response lags, and insufficient routine monitoring, this paper proposes a Spatio-Temporal Collaborative Optimization for Joint Control and Scheduling (STCO-JCS) algorithm tailored for unmanned aerial vehicles (UAVs) and wireless sensor networks (WSNs). First, spatial autocorrelation analysis based on fire data classifies areas into ultra-high, high, medium, and low risk zones to assist in determining UAV access priorities. Second, we construct optimal inspection trajectories for the UAV by taking into account the inspection sequence and the city’s topography. By modeling the path deviations caused by wind interference and designing precision control algorithms, we improve the accuracy of the UAV’s flight path, ultimately achieving the goal of reducing UAV inspection time. Finally, by coordinating the spatiotemporal operations of drones and wireless sensor networks, we can achieve early detection and rapid response in high-risk fire zones, thereby reducing drone energy consumption while enhancing the efficiency of the UAV-WSN fire monitoring system. Simulation results demonstrate that under a 20-square-kilometer simulation area, STCO-JCS controls inspection paths within 14–17 km. In the multi-UAV scenario, the proposed method achieves approximately 3.17–9.66% improvement in energy efficiency, while in the single-UAV scenario, improvements of 10.83%, 50.54%, and 9.26% are observed in metrics. This provides effective decision support for the dynamic deployment of firefighting and rescue resources. Full article

An investigation employing multiple diagnostic techniques was conducted to evaluate the post-fire condition and residual structural safety of a fire-damaged precast concrete industrial building. The evaluation included a detailed visual inspection, mechanical testing of extracted concrete cores, and mineralogical and microstructural analysis through thermo-chemical methods, namely X-ray Diffraction, Scanning Electron Microscopy, and Energy-Dispersive X-ray Spectroscopy, alongside tensile strength tests of reinforcement bars sampled from the affected structure. The building was divided into five sections according to the severity and extent of observed fire damage. Results indicated that the highest in situ temperatures were attained in the most heavily damaged section, whereas the remaining sections experienced progressively lower temperatures, remained below approximately 600 °C. Despite the severe fire exposure in localized areas, all assessed structural elements maintained adequate residual integrity. The reinforcing steel exhibited satisfactory residual mechanical properties, exhibiting yield strengths ranging from 550 to 600 MPa. The integration of visual, mechanical, and microstructural assessments provides a reliable framework for estimating fire temperatures and supporting structural rehabilitation decisions. Full article

Transmission lines traversing forested areas pose significant fire risks, necessitating timely and efficient inspection mechanisms. Traditional manual patrols and cloud-based UAV inspections suffer from high latency, bandwidth dependence, and delayed response times. To address these challenges, this study proposes an integrated, real-time UAV-edge computing system for the early identification of fire risks and structural hazards along transmission corridors. The system integrates a DJI M300 RTK UAV with a Manifold 2-G edge computing unit (based on NVIDIA Jetson TX2), deploying a lightweight, TensorRT-optimized YOLOv8 model. By leveraging FP16 precision quantization and operator fusion, the system achieves a real-time inference speed of 32 FPS on the embedded platform. Furthermore, a custom Payload SDK integration ensures automated image acquisition and closed-loop data transmission via a dual-mode (4G/5G + Wi-Fi) communication link. Field experiments demonstrate that the system significantly reduces data transmission latency while maintaining high detection accuracy (mAP > 94%), providing a robust and replicable solution for intelligent power grid maintenance in resource-constrained environments. Full article

The renewed interest in hydrogen peroxide-based space propulsion systems has highlighted the persistent issue of catalyst degradation during long-term operation. Although several studies have investigated the underlying causes of this phenomenon, effective regeneration techniques capable of restoring catalytic activity have not yet been clearly demonstrated. This study investigates the mechanisms responsible for performance degradation and proposes a viable regeneration strategy for palladium-based catalysts. Experimental analyses were conducted on a batch of commercial Al₂O₃/Pd pellets subjected to multiple firing cycles in a 10 N-class hybrid mini-thruster. Monitoring of the propulsive performance revealed a progressive decline in catalytic activity, ultimately preventing ignition of the hybrid rocket engine. To characterize the degradation mechanisms, the pellets were examined through visual inspection, static hydrogen peroxide decomposition tests, and Temperature Programmed Reduction (TPR) analysis. The results indicated significant surface oxidation of palladium, leading to reduced decomposition efficiency. A chemical regeneration procedure based on sodium borohydride (NaBH₄) treatment was subsequently developed to restore catalytic performance. The regenerated pellets were tested under the same experimental conditions that had previously led to ignition failure. Their propulsive performance was then compared with both the degraded pellets and a new batch of equivalent catalysts. The results demonstrate that the regeneration process successfully restored the catalytic activity to levels comparable with the original state, enabling stable and efficient hybrid combustion. These findings confirm the role of surface oxidation in catalyst degradation and demonstrate that targeted chemical treatment can significantly extend catalyst lifetime. The proposed regeneration strategy offers a practical method to reduce costs of ground-based experimental campaigns and support the future deployment of hydrogen peroxide-based propulsion systems in space applications by providing insights into the mechanisms that can degrade the performance of palladium catalysts. Full article

Bridges are critical components of transportation networks, and fire accidents can significantly impair their structural integrity, leading to safety risks and major economic losses. This study presents a comprehensive inspection, materials testing, repair, and field load testing program for a full-scale concrete box girder bridge (Delta Bridge, Alexandria, Egypt) following a fire exposure on two spans. A total of 28 concrete core samples were extracted and tested, revealing average compressive strengths of 48.50 MPa (slab), 53.90 MPa (web), and 45.88 MPa (columns), representing moderate reductions of approximately 8.5%, 7.9%, and 10.8%, respectively, relative to the original in situ concrete strength recorded during construction, and 29.2%, 43.7%, and 30.0% increases over the minimum acceptance limits specified by Egyptian code of practice (ECP 203). Tensile strength tests on reinforcement bars indicated an average yield strength reduction coefficient of 0.87, corresponding to an estimated peak exposure temperature of 600 °C, yet still satisfying Egyptian code requirements (≥500 MPa). Field static load tests using 40-ton tri-axle trucks demonstrated maximum midspan deflections of 6.7 mm in fire-exposed spans and full recovery (>94%) upon unloading, confirming that the residual stiffness and load-carrying capacity were within acceptable limits. Based on these results, a targeted repair program was executed, including concrete cover replacement with shotcrete; steel derusting; surface coating; and bearing replacement, followed by a verification load test that confirmed the effectiveness of the rehabilitation. This case study demonstrates a robust framework for post-fire condition assessment, residual capacity evaluation, and repair validation of concrete box girder bridges. The methodology and findings provide valuable guidance for engineers and transportation authorities in mitigating fire-induced risks and ensuring the safe reopening of critical bridge infrastructure. Full article

With the advancement of unmanned aerial vehicle (UAV) technology, its use in ecological monitoring and safety management of national parks has expanded significantly. However, object detection in complex scenes remains challenging due to environmental complexity, background interference, and occlusion. To address these issues, this paper proposes two improved YOLOv8-based models, YOLOv8-StarNet-CGA and SCS-YOLOv8, for detecting pine wilt disease-infected trees, under-construction farmhouses, and forest fires. In YOLOv8-StarNet-CGA, the StarNet module and Content-Guided Attention (CGA) are integrated into the backbone to enhance global feature extraction and focus on critical regions through dynamic weight adjustment. In SCS-YOLOv8, the original CIoU loss is also replaced with SIoU loss to optimize shape and orientation consistency, improving robustness. Experiments on UAV datasets covering diverse national park scenes demonstrate the effectiveness of the models. Results show that the improved models substantially outperform the original YOLOv8 in Precision, Recall, and mAP50. For pine wilt disease caused by the pine wood nematode Bursaphelenchus xylophilus, YOLOv8-StarNet-CGA achieves 8.6% higher Precision and 11.7% higher mAP50, facilitating early diagnosis and intervention of the disease. In under-construction farmhouse scenarios, Precision rises by 11% and mAP50 by 10.1%, lowering annual inspection labor by nearly 30% and improving oversight. For forest fires, SCS-YOLOv8 is more effective, with Precision improved by 7.2% and mAP50 by 6.3%. The improved detection model enables earlier identification of fire spots, thereby providing additional response time for emergency intervention, helping to mitigate fire spread and reduce the loss of forest resources. Both models also reduce GFLOPs and computational complexity, striking a balance between efficiency and accuracy, and showing strong potential for UAV deployment. Full article

Passenger transport companies have often been affected by fires in their vehicles, causing considerable damage. As a result, it is important to study the causes and effects of these fires, as well as to define the maintenance policies and strategies to be implemented to minimize the probability of this type of accident occurring. The support for this paper was based on the study of an accident that occurred in Portugal involving a passenger bus that suffered a fire in the engine compartment, which spread to the passenger compartment and caused the destruction of the vehicle, with no personal injuries. This study used infrared image analysis technology, oil ignition temperature analysis, maintenance history, accident history and operator interviews to determine the possible cause of the ignition. It was found that the cause was due to oil leaks from the engine compartment cooling system. The present communication will share a set of explanatory elements of the circumstances in which the accident occurred. In addition to identifying the causes of the accident, the study warns of the importance of more effective and efficient maintenance, particularly when using Condition Based Maintenance (CBM), including periodic visual inspections of the various mechanical and electrical components that make up the vehicles. The conclusions presented in the study also show that these events are not unrelated to the poor or even non-existent maintenance policy for the entire fleet, including the applicable standards. Full article

Automated anomaly detection in transportation infrastructure is essential for enhancing safety and reducing the operational costs associated with manual inspection protocols. This study presents an improved neuromorphic vision system, which extends the prior SIFT-SNN (scale-invariant feature transform–spiking neural network) proof-of-concept by incorporating temporal feature aggregation for context-aware and sequence-stable detection. Analysis of classical stitching-based pipelines exposed sensitivity to motion and lighting variations, motivating the proposed temporally smoothed neuromorphic design. SIFT keypoints are encoded into latency-based spike trains and classified using a leaky integrate-and-fire (LIF) spiking neural network implemented in PyTorch. Evaluated across three hardware configurations—an NVIDIA RTX 4060 GPU, an Intel i7 CPU, and a simulated Jetson Nano—the system achieved 92.3% accuracy and a macro F1 score of 91.0% under five-fold cross-validation. Inference latencies were measured at 9.5 ms, 26.1 ms, and ~48.3 ms per frame, respectively. Memory footprints were under 290 MB, and power consumption was estimated to be between 5 and 65 W. The classifier distinguishes between safe, partially dislodged, and fully dislodged barrier pins, which are critical failure modes for the Auckland Harbour Bridge’s Movable Concrete Barrier (MCB) system. Temporal smoothing further improves recall for ambiguous cases. By achieving a compact model size (2.9 MB), low-latency inference, and minimal power demands, the proposed framework offers a deployable, interpretable, and energy-efficient alternative to conventional CNN-based inspection tools. Future work will focus on exploring the generalisability and transferability of the work presented, additional input sources, and human–computer interaction paradigms for various deployment infrastructures and advancements. Full article

Ensuring production safety and enabling rapid emergency response in complex industrial environments remains a critical challenge. Traditional inspection robots are often limited by perception delays when confronted with sudden dynamic threats. This paper presents a vision-driven dynamic digital twin system designed to enhance real-time monitoring and emergency management capabilities. The framework constructs high-fidelity 3D models using SolidWorks 2024, Scaniverse 5.0.0, and 3ds Max 2024, and integrates them into a unified digital twin environment via the Unity 3D engine. Its core contribution is a vision-driven dynamic mapping mechanism: robots operating on the Robot Operating System (ROS) and equipped with ZED stereo cameras and embedded YOLOv5m models perform real-time detection, such as personnel and fire sources. Recognized targets trigger the dynamic instantiation of corresponding virtual models from a pre-built library, enabling automated, real-time reconstruction within the digital twin. An integrated service platform further supports early warning, status monitoring, and maintenance functions. Experimental validation confirms that the system satisfies key performance metrics, including data collection completeness exceeding 99.99%, incident detection accuracy of 80%, and state synchronization latency below 90 milliseconds. The system improves the dynamic updating efficiency of digital twins and demonstrates strong potential for proactive safety assurance and efficient emergency response in dynamic industrial settings. Full article