Search Results (10)

Corrosion is a critical issue in civil structures, significantly affecting their durability and functionality. Detecting corrosion at an early stage is essential to prevent structural failures and ensure safety. This study proposes an expert system based on a novel methodology for corrosion detection using vibration signal analysis. The approach employs graphical empirical mode decomposition (GEMD) to decompose vibration signals into their intrinsic mode functions, extracting relevant structural features. These features are then transformed into grayscale images and classified using a Convolutional Neural Network (CNN) to automatically differentiate between a healthy structure and one affected by corrosion. To enhance the computational efficiency of the method without compromising accuracy, different CNN architectures and image sizes are tested to propose a low-complexity model. The proposed approach is validated using a 3D nine-bay truss-type bridge model encountered in the Vibrations Laboratory at the Autonomous University of Querétaro, Mexico. The evaluation considers three different corrosion levels: (1) incipient, (2) moderate, and (3) severe, along with a healthy condition. The combination of GEMD and CNN provides a highly accurate corrosion detection framework that achieves 100% classification accuracy while remaining effective regardless of the damage location and severity, making it a reliable tool for early-stage corrosion assessment that enables timely maintenance and enhances structural health monitoring to improve the long life and safety of civil structures. Full article

The maritime transport of liquefied gases poses significant safety and environmental hazards such as fire, explosion, toxic gas emissions, and air pollution. The main objective of this study was to systematically identify, analyze, and prioritise the potential risks associated with the operation of liquefied gas tankers using a hybrid methodological framework. This framework integrates Fuzzy Delphi, Fuzzy DEMATEL, and Fault Tree Analysis (FTA) techniques to provide a comprehensive risk assessment. Initially, 20 key risk factors were identified through expert consensus using the Fuzzy Delphi method. The causal relationships between these factors were then assessed using Fuzzy DEMATEL to understand their interdependencies. Based on these results, accident probabilities were further analyzed using FTA modelling. The results show that fires, explosions, and large gas leaks are the most serious threats. Equipment failures—often caused by corrosion and operational errors by crew members—are also significant contributors. In contrast, cyber-related risks were found to be of lower criticality. The study highlights the need for improved crew training, rigorous inspection mechanisms, and the implementation of robust preventive risk controls. It also suggests that the prioritisation of these risks may need to be reevaluated as autonomous ship technologies become more widespread. By mapping the interrelated structure of operational hazards, this research contributes to a more integrated and strategic approach to risk management in the LNG/LPG shipping industry. Full article

Inspecting and maintaining power lines is essential for ensuring the safety, reliability, and efficiency of electrical infrastructure. This process involves regular assessment to identify hazards such as damaged wires, corrosion, or vegetation encroachment, followed by timely maintenance to prevent accidents and power outages. By conducting routine inspections and maintenance, utilities can comply with regulations, enhance operational efficiency, and extend the lifespan of power lines and equipment. Unmanned Aerial Vehicles (UAVs) can play a relevant role in this process by increasing efficiency through rapid coverage of large areas and access to difficult-to-reach locations, enhanced safety by minimizing risks to personnel in hazardous environments, and cost-effectiveness compared to traditional methods. UAVs equipped with sensors such as visual and thermographic cameras enable the accurate collection of high-resolution data, facilitating early detection of defects and other potential issues. To ensure the safety of the autonomous inspection process, UAVs must be capable of performing onboard processing, particularly for detection of power lines and obstacles. In this paper, we address the development of a deep learning approach with YOLOv8 for power line detection based on visual and thermographic images. The developed solution was validated with a UAV during a power line inspection mission, obtaining mAP@0.5 results of over 90.5% on visible images and over 96.9% on thermographic images. Full article

The nuclear industry is exploring applications of Artificial Intelligence (AI), including autonomous control and management of reactors and components. A condition assessment framework that utilizes AI and sensor data is an important part of such an autonomous control system. A nuclear power plant has various structures, systems, and components (SSCs) such as piping-equipment that carries coolant to the reactor. Piping systems can degrade over time because of flow-accelerated corrosion and erosion. Any cracks and leakages can cause loss of coolant accident (LOCA). The current industry standards for conducting maintenance of vital SSCs can be time and cost-intensive. AI can play a greater role in the condition assessment and can be extended to recognize concrete degradation (chloride-induced damage and alkali–silica reaction) before cracks develop. This paper reviews developments in condition assessment and AI applications of structural and mechanical systems. The applicability of existing techniques to nuclear systems is somewhat limited because its response requires characterization of high and low-frequency vibration modes, whereas previous studies focus on systems where a single vibration mode can define the degraded state. Data assimilation and storage is another challenging aspect of autonomous control. Advances in AI and data mining world can help to address these challenges. Full article

Annotated datasets play a significant role in developing advanced Artificial Intelligence (AI) models that can detect bridge structure defects autonomously. Most defect datasets contain visual images of surface defects; however, subsurface defect data such as delamination which are critical for effective bridge deck evaluations are typically rare or limited to laboratory specimens. Three Non-Destructive Evaluation (NDE) methods (Infrared Thermography (IRT), Impact Echo (IE), and Ground Penetrating Radar (GPR)) were used for concrete delamination detection and reinforcement corrosion detection. The authors have developed a unique NDE dataset, Structural Defect Network 2021 (SDNET2021), which consists of IRT, IE, and GPR data collected from five in-service reinforced concrete bridge decks. A delamination survey map locating the areas, extent and classes of delamination served as the ground truth for annotating IRT, IE and GPR field tests’ data in this study. The IRT were processed to create an ortho-mosaic maps for each deck and were aligned with the ground truth maps using image registration, affine transformation, image binarization, morphological operations, connected components and region props techniques to execute a semi-automatic pixel–wise annotation. Conventional methods such as Fast Fourier transform (FFT)/peak frequency and B-Scan were used for preliminary analysis for the IE and GPR signal data respectively. The quality of NDE data was verified using conventional Image Quality Assessment (IQA) techniques. SDNET2021 dataset consists of 557 delaminated and 1379 sound IE signals, 214,943 delaminated and 448,159 sound GPR signals, and about 1,718,083 delaminated and 2,862,597 sound IRT pixels. SDNET2021 addresses one of the major gaps in benchmarking, developing, training, and testing advanced deep learning models for concrete bridge evaluation by providing a publicly available annotated and validated NDE dataset. Full article

In line with the recent industrial trends of hyperconnectivity, 5G technology deployment, the Internet of Things (IoT) and Industry 4.0, the ultimate goal of corrosion prevention is the invention of smart coatings that are able to assess their own condition, predict the onset of corrosion and alert users just before it happens. It is of particular interest to tackle corrosion that occurs in non-accessible areas where human inspectors or handheld devices are useless. To accomplish this, a variety of technologies that are embedded or could potentially be embedded into the coatings are being developed to monitor coating condition, which are based, for instance, on the evolution of electrochemical or mechanical properties over time. For these technologies to be fully embedded into the coatings and work remotely, solutions are needed for connectivity and power supply. A paradigm shift from routine prescheduled maintenance to condition-based preventive maintenance could then become a reality. In this work, the technologies that enable the in-service monitoring of organic anticorrosion coatings were compiled. Soon, some of them could be integrated into the sensing elements of autonomous, connected neural-like networks that are capable of remotely assessing the condition of the anticorrosion protection of future infrastructures. Full article

Chloride diffusion and penetration, and consequently chloride-induced corrosion of reinforcement, are among the most common mechanisms of deterioration of concrete structures, and, as such, the most widely and deeply investigated as well. The benefits of using Ultra-High Performance (Fiber-Reinforced) Concrete—UHP(FR)C to extend the service life of concrete structures in “chloride attack” scenarios have been addressed, mainly focusing on higher “intrinsic” durability of the aforementioned category of materials due to their compact microstructure. Scant, if nil, information exists on the chloride diffusion and penetration resistance of UHPC in the cracked state, which would be of the utmost importance, also considering the peculiar (tensile) behavior of the material and its high inborn autogenous healing capacity. On the other hand, studies aimed at quantifying the delay in chloride penetration promoted by self-healing, both autogenous and autonomous, of cracked (ordinary) concrete have started being promoted, further highlighting the need to investigate the multidirectional features of the phenomenon, in the direction both parallel and orthogonal to cracks. In this paper, a tailored experimental methodology is presented and validated to measure, with reference to its multidirectional features, the chloride penetration in cracked UHPC and the effects on it of self-healing, both autogenous and stimulated via crystalline admixtures. The methodology is based on micro-core drilling in different positions and at different depths of UHPC disks cracked in splitting and submitted to different exposure/healing times in a 33 g/L NaCl aqueous solution. Its validation is completed through comparison with visual image analysis of crack sealing on the same specimens as well as with the assessment of crack sealing and of mechanical and permeability healing-induced recovery performed, as previously validated by the authors, on companion specimens. Full article

The utilisation of robots in hazardous nuclear environments has potential to reduce risk to humans. However, historical use has been largely limited to specific missions rather than broader industry-wide adoption. Testing and verification of robotics in realistic scenarios is key to gaining stakeholder confidence but hindered by limited access to facilities that contain radioactive materials. Simulations offer an alternative to testing with actual radioactive sources, provided they can readily describe the behaviour of robotic systems and ionising radiation within the same environment. This work presents a quick and easy way to generate simulated but realistic deployment scenarios and environments which include ionising radiation, developed to work within the popular robot operating system compatible Gazebo physics simulator. Generated environments can be evolved over time, randomly or user-defined, to simulate the effects of degradation, corrosion or to alter features of certain objects. Interaction of gamma radiation sources within the environment, as well as the response of simulated detectors attached to mobile robots, is verified against the MCNP6 Monte Carlo radiation transport code. The benefits these tools provide are highlighted by inclusion of three real-world nuclear sector environments, providing the robotics community with opportunities to assess the capabilities of robotic systems and autonomous functionalities. Full article

In this work, technological feasibility of autonomous corrosion assessment of reinforced concrete structures is studied. Corrosion of reinforcement bars (rebar), induced by carbonation or chloride penetration, is one of the leading causes for deterioration of concrete structures throughout the globe. Continuous nondestructive in-service monitoring of carbonation through pH and chloride ion (Cl⁻) concentration in concrete is indispensable for early detection of corrosion and making appropriate decisions, which ultimately make the lifecycle management of RC structures optimal from resources and safety perspectives. Critical state-of-the-art review of pH and Cl⁻ sensors revealed that the majority of the sensors have high sensitivity, reliability, and stability in concrete environment, though the experiments were carried out for relatively short periods. Among the reviewed works, only three attempted to monitor Cl⁻ wirelessly, albeit over a very short range. As part of the feasibility study, this work recommends the use of internet of things (IoT) and machine learning for autonomous corrosion condition assessment of RC structures. Full article

China is facing severe acid deposition. Acid deposition can cause economic loss, corrosion, and damage to materials, and the reduction of material life span. In this study, the administrative areas (including municipalities, prefecture-level cities, regions, autonomous prefectures, and leagues—hereinafter referred to the cities) at and above the prefecture level were selected as research areas. Monitoring results of acid precipitation and ambient air sulfur dioxide (SO₂) from the China National Environmental Monitoring Network were used, research findings available domestically and abroad were summarized, and a set of material exposure inventory per capita was established, based on urban and rural areas in Eastern, Central, and Western China regions. Losses of construction materials caused by acid deposition in the cities were assessed by using the said materials’ acid rain exposure response functions available. The results showed that, material loss caused by acid deposition in China was 32.165 billion yuan (RMB, similarly hereinafter) in 2013, accounting for 0.057% of GDP (Gross Domestic Product) and 3.4% of the total investment for environmental pollution governance this year. Full article