Search Results (250)

Smart safety helmets equipped with vision systems are emerging as powerful tools for industrial infrastructure inspection. This paper presents a comprehensive state-of-the-art review of such VSLAM-enabled (Visual Simultaneous Localization and Mapping) helmets. We surveyed the evolution from basic helmet cameras to intelligent, sensor-fused inspection platforms, highlighting how modern helmets leverage real-time visual SLAM algorithms to map environments and assist inspectors. A systematic literature search was conducted targeting high-impact journals, patents, and industry reports. We classify helmet-integrated camera systems into monocular, stereo, and omnidirectional types and compare their capabilities for infrastructure inspection. We examine core VSLAM algorithms (feature-based, direct, hybrid, and deep-learning-enhanced) and discuss their adaptation to wearable platforms. Multi-sensor fusion approaches integrating inertial, LiDAR, and GNSS data are reviewed, along with edge/cloud processing architectures enabling real-time performance. This paper compiles numerous industrial use cases, from bridges and tunnels to plants and power facilities, demonstrating significant improvements in inspection efficiency, data quality, and worker safety. Key challenges are analyzed, including technical hurdles (battery life, processing limits, and harsh environments), human factors (ergonomics, training, and cognitive load), and regulatory issues (safety certification and data privacy). We also identify emerging trends, such as semantic SLAM, AI-driven defect recognition, hardware miniaturization, and collaborative multi-helmet systems. This review finds that VSLAM-equipped smart helmets offer a transformative approach to infrastructure inspection, enabling real-time mapping, augmented awareness, and safer workflows. We conclude by highlighting current research gaps, notably in standardizing systems and integrating with asset management, and provide recommendations for industry adoption and future research directions. Full article

A particularly important activity carried out by railway infrastructure managers to maintain railway devices in full working order is the diagnostic process. It increases the level of railway safety. The diagnostic process involves collecting information about the equipment through inspections, tests, functional trials, parameter measurements, and analysis of the working environment, followed by comparing the obtained information with the required parameters or permissible conditions. This activity also enables the formulation of a technical diagnosis regarding the current ability of the devices to perform its intended functions, taking into account the impact of its technical condition on railway traffic safety. This is especially important in the case of railway traffic control devices, as these devices are largely responsible for ensuring railway traffic safety. The collection of data on the condition of railway traffic control devices in the form of Big Data sets and diagnostic inference is an effective factor in making operational decisions for such devices. It enables the acquisition of complete information about the actual course of the exploitation process and allows for obtaining reliable information necessary to manage this process, particularly in the areas of diagnostics forecasting of devices conditions, renewal, and organization of maintenance and repair facilities. To support this, a service data acquisition and analysis system for railway traffic control devices (SADEK) was developed. This system can serve as a software platform for maintenance needs in the railway sector. Full article

Agriculture and animal feeding operations are responsible for 87% of ammonia emissions in the UK. Controlling NH₃ concentrations below 20 ppm is crucial to preserve workers’ and livestock’s well-being. Therefore, ammonia control systems are required for maintaining adequate air quality in livestock facilities. This study assessed the ammonia reduction efficiency of a novel air pollution abatement (APA) system used in a pig farm building. The monitoring duration was 11 weeks. The results were compared with the baseline from a previous pig cycle during the same time of year in 2023. A ventilation-controlled room was monitored during a two-phase campaign, and the actual ammonia concentrations were measured at different locations within the site and at the inlet/outlet of the APA system. A 98% ammonia reduction was achieved at the APA outlet through NH₃ absorption in tap water. Ion chromatography analyses of farm water samples revealed NH₃ concentrations of up to 530 ppm within 83 days of APA operation. Further scanning electron microscopy and energy-dispersive X-ray inspections revealed the presence of salts and organic/inorganic matter in the solid residues. This research can contribute to meeting current ammonia regulations (NECRs), also by reusing the process water as a potential nitrogen fertiliser in agriculture. Full article

Steel lazy wave risers (SLWRs) are critical in offshore hydrocarbon transport for linking subsea wells to floating production facilities in deep-water environments. The incorporation of buoyancy modules reduces curvature-induced stress concentrations in the touchdown zone (TDZ); however, extended operational exposure under cyclic environmental and operational loads results in repeated seabed contact. This repeated interaction modifies the seabed soil over time, gradually forming a trench and altering the riser configuration, which significantly impacts stress patterns and contributes to fatigue degradation. Accurately reconstructing the riser’s evolving profile in the TDZ is essential for reliable fatigue life estimation and structural integrity evaluation. This study proposes a simulation-based framework for the autonomous tracking of SLWRs using a fin-actuated autonomous underwater vehicle (AUV) equipped with a monocular camera and multibeam echosounder. By fusing visual and acoustic data, the system continuously estimates the AUV’s relative position concerning the riser. A dedicated image processing pipeline, comprising bilateral filtering, edge detection, Hough transform, and K-means clustering, facilitates the extraction of the riser’s centerline and measures its displacement from nearby objects and seabed variations. The framework was developed and validated in the underwater unmanned vehicle (UUV) Simulator, a high-fidelity underwater robotics and pipeline inspection environment. Simulated scenarios included the riser’s dynamic lateral and vertical oscillations, in which the system demonstrated robust performance in capturing complex three-dimensional trajectories. The resulting riser profiles can be integrated into numerical models incorporating riser–soil interaction and non-linear hysteretic behavior, ultimately enhancing fatigue prediction accuracy and informing long-term infrastructure maintenance strategies. Full article

Aging port infrastructure demands frequent and reliable inspections, yet the existing automated systems often require expensive industrial drones, posing significant adoption barriers for local governments with limited resources. To address this challenge, this study develops a low-cost, automated crack detection system for port quay walls utilizing orthophotos generated from a small general-purpose drone. The system employs the YOLOR (You Only Learn One Representation) object detection algorithm, enhanced by two novel image processing techniques—overlapping tiling and pseudo-altitude slicing—to overcome the resolution limitations of low-cost cameras. While official guidelines for port facilities designate 3 mm as an inspection threshold, our system is specifically designed to achieve a higher-resolution detection capability for cracks as narrow as 1 mm. This approach ensures reliable detection with a sufficient safety margin and enables the proactive monitoring of crack progression for preventive maintenance. The effectiveness of the proposed image processing techniques was validated, with an F₁ score-based analysis revealing key trade-offs between maximizing detection recall and achieving a balanced performance depending on the chosen simulated altitude. Furthermore, evaluation using real-world inspection data demonstrated that the proposed system achieves a detection performance comparable to that of a well-established commercial system, confirming its practical applicability. Crucially, by mapping the detected cracks to real-world coordinates on georeferenced orthophotos, the system provides a foundation for advanced, data-driven asset management, allowing for the quantitative tracking of deterioration over time. These results confirm that the proposed workflow is a practical and sustainable solution for infrastructure monitoring. Full article

It is a well-known fact that proper nutrition is essential for human beings to live healthy lives. For thousands of years, it has been considered that dates are one of the best nutrient providers. To have better-quality dates and to enhance the shelf life of dates, it is vital to preserve dates in optimal conditions that contribute to food security. Hence, it is crucial to know the shelf life of different types of dates. In current practice, shelf life assessment is typically based on manual visual inspection, which is subjective, error-prone, and requires considerable expertise, making it difficult to scale across large storage facilities. Traditional cold storage systems, whilst being capable of monitoring temperature and humidity, lack the intelligence to detect spoilage or predict shelf life in real-time. In this study, we present a novel IoT-based shelf life estimation system that integrates multichannel gas sensors and a lightweight machine learning model deployed on an edge device. Unlike prior approaches, our system captures the real-time emissions of spoilage-related gases (methane, nitrogen dioxide, and carbon monoxide) along with environmental data to classify the freshness of date fruits. The model achieved a classification accuracy of 91.9% and an AUC of 0.98 and was successfully deployed on an Arduino Nano 33 BLE Sense board. This solution offers a low-cost, scalable, and objective method for real-time shelf life prediction. This significantly improves reliability and reduces postharvest losses in the date supply chain. Full article

This study investigated the impact of different reverse logistics strategies on the economic and environmental performance of a system within the rubber flooring sector. A simulation tool was developed to replicate the behavior of a real production system, focusing on the transition from linear to circular processes. By considering multiple factors influencing system performance, this research offers an overview of the sustainability of various RL strategies and provides realistic estimates for different scenarios. Three key factors were used to evaluate each strategy’s response: transportation distance, flooring thickness, and returned flooring quality. The findings suggest that an environmental advantage generally favors on-site inspections at the customer’s location to assess the returned product’s condition, regardless of distance. However, centralizing inspections at the manufacturer’s facility is more economically advantageous when distances are short, particularly when the company prioritizes recycling over other circular economy practices. Based on these results, practical implications and guidelines are proposed to help companies balance cost-effectiveness with sustainability, optimizing their operations within a circular economy framework. Full article

Pavement markings, as a crucial component of traffic guidance and safety facilities, are subject to degradation and abrasion after a period of service. To ensure traffic safety, retroreflectivity and diffuse illumination should be above the minimum thresholds and required to undergo inspection periodically. Therefore, an onboard light detection and ranging (LiDAR) and camera deployment optimization method is proposed for pavement marking distress detection to adapt to complex traffic conditions, such as shadows and changing light. First, LiDAR and camera sensors’ detection capability was assessed based on the sensors’ built-in features. Then, the LiDAR–camera deployment problem was mathematically formulated for pavement marking distress fusion detection. Finally, an improved red fox optimization (RFO) algorithm was developed to solve the deployment optimization problem by incorporating a multi-dimensional trap mechanism and an improved prey position update strategy. The experimental results illustrate that the proposed method achieves 5217 LiDAR points, which fall on a 0.58 m pavement marking per data frame for distress fusion detection, with a relative error of less than 7% between the mathematical calculation and the field test measurements. This empirical accuracy underscores the proposed method’s robustness in real-world scenarios, effectively mitigating the challenges posed by environmental interference. Full article

This study proposes a practical method for the early detection of failure signs in a rotating biological contactor (RBC) system that has been in long-term operation at a municipal solid waste landfill. Seventeen years of inspection logs, recorded between 2006 and 2023, were digitized and analyzed with a focus on abnormal noise, electric current values, operational status, and failure history. The analysis revealed that frequent occurrences of abnormal noise and sudden fluctuations in current tend to precede equipment failures. Based on these findings, we developed a scoring model for the predictive maintenance of RBCs. Traditionally, determining the score required professional knowledge such as performing a sensitivity analysis. However, by utilizing AI (ChatGPT o4), we were able to obtain recommended values for these parameters. This means that operators can now build and adjust a scoring model for predictive maintenance of RBCs according to their specific on-site conditions. On the other hand, sudden increases in current and abnormal noises were previously considered strong indicators of failure prediction. These parameters will depend on factors such as the sensitivity of electrical current meters and surrounding noise. Therefore, depending on the specific environmental conditions at the site, the scoring model developed in this study may have limited predictive accuracy. Full article

The petrochemical industry faces significant safety challenges, necessitating stringent protocols and advanced monitoring systems. Traditional methods rely on manual inspections and fixed sensors, often reacting to hazards only after they occur. Multimodal AI, integrating visual, sensor, and textual data, offers a transformative solution for real-time, proactive safety management. This paper evaluates AI models—Gemini 1.5 Pro, OPENAI GPT-4, and Copilot—in detecting workplace hazards, ensuring compliance with Process Safety Management (PSM) and DuPont safety frameworks. The study highlights the models’ potential in improving safety outcomes, reducing human error, and supporting continuous, data-driven risk management in petrochemical plants. This paper is the first of its kind to use the latest multimodal tech to identify the safety hazard; a similar model could be deployed in other manufacturing industries, especially the oil and gas (both upstream and downstream) industry, fertilizer industries, and production facilities. Full article

The accurate detection and mapping of subsurface utilities are critical for ensuring safety and efficiency in excavation and construction projects. Among various technologies, Ground-Penetrating Radar (GPR) has been widely used for locating underground infrastructure due to its non-destructive nature and ability to detect both metallic and non-metallic materials. However, many GPR systems struggle to meet the practical depth requirements in real-world conditions, especially when identifying non-metallic facilities such as PVC and PE pipes. In South Korea, the legal regulations require underground utility locators to meet specific accuracy standards, including a minimum detectable depth of 3 m. These regulations also mandate periodic performance testing of surveying equipment at authorized inspection centers. Despite this, most GPR systems tested at the official performance evaluation site at Sungkyunkwan University demonstrated limited effectiveness, with an average detection range of only 1.5 m. This study evaluates the performance of a handheld All Materials Locator (AML) device developed by SubSurface Instruments, Inc., (Janesville, WI, USA) which uses ultra-high radio frequencies to detect subsurface density variations. Experimental results from both the certified test facility and field conditions indicate that the AML meets South Korea’s legal requirements for minimum depth and accuracy, by successfully identifying a wide range of subsurface utilities including non-metallic materials. The findings suggest that the AML is a viable alternative to conventional GPR systems for utility detection in regulated environments. Full article

Continuous monitoring is key to the safety of such critical infrastructure as Tailings storage facilities. Due to the high risk of liquification of the dams, it is crucial to move the water as far as possible from the dam crest. In order to control the distance from the water to the dam, regular manual inspections need to be carried out. In this article, we propose a method for automatic detection of the water-beach line based on photographs from an unmanned aerial vehicle (UAV). An algorithm based on MobileNet v2 convolutional neural network architecture was developed for the classification of images collected by the UAV. Based on the results of this classification, the border between the water and the beach is defined. Several approaches to the model training were tested. Accuracy for the validation set reaches up to 97% for particular image fragments. Full article

The Building Information Modeling (BIM) concept has been recently implemented in railway infrastructure, assisting mainly in the project elaboration, and further, the facility management aspect. The present study addresses the inspection activity of the railway geometry, in a BIM context, using a rigorous modeling process of the railway track components, and the development of a Dynamo script for the evaluation of the degree of geometric irregularity detected during inspection works. The monitoring phase of the rail tracks involves a planned railway inspection schedule, normally supported by human analyses of data collected in a railway geometric inspection. The created script allows for evaluating the inspection data and categorizes the data by alert levels that are associated with a color code, visualized over the railway components of the BIM model. The Dynamo script uses new BIM parameters considering the maintenance activity, allowing for analyzing inspection data and visualizing the colored alerts. This capacity alerts the maintenance engineer about the urgency of planning a retrofitting action, according to the severity level of the detected geometric anomaly. An illustrative real railway track segment is considered supporting the modeling process, the inspection data collection and the efficiency analyses of the script application. This research intends to contribute to increment knowledge of BIM adoption in railway infrastructures, emphasizing the potential of using Dynamo programming on BIM model database management. Full article

This study investigates the failure of a 750A dual-insulated pipeline, where cracks developed along the weld joints during heat supply resumption at the district heating facility. A comprehensive analysis was conducted through visual inspection, mechanical testing, microstructural characterization, finite element analysis (FEA), and electrochemical corrosion testing. The results indicate that cracks were generated in the heat-affected zone (HAZ), primarily caused by galvanic corrosion and thermal expansion-induced stress accumulation. Open circuit potential (OCP) measurements in a 3 M NaCl solution confirmed that the HAZ was anodic, leading to the most vulnerable position to corrosion. Furthermore, localized electrochemical tests were conducted for respective microstructural regions within the HAZ. The results reveal that coarse-grained HAZ exhibited the lowest corrosion potential, giving rise to preferential corrosion, promoting pit formation, and serving as initiation sites for stress concentration and crack propagation. FEA simulations demonstrate that pre-existing microvoids in the HAZ act as stress concentration sites, undergoing a localized stress exceeding 475 MPa. These findings emphasize the importance of controlling microstructural stability and mechanical integrity in welded pipelines, particularly in corrosive environments subjected to thermal stresses. Full article