Comprehensive Inspection Research Articles

Post-earthquake inspection of high-speed railway viaducts with multi-scale task interaction deep learning strategy

Automated computer vision-based inspections of railway infrastructures, such as component type, damaged status, and location, have been investigated actively by resorting to task-specific deep learning models. However, task-specific models that fulfill these separate inspection tasks encountered bottlenecks in improving inference accuracy, and bring huge computational costs. Multi-task deep learning, which can fulfill these inspection tasks concurrently, has yet to be fully investigated in the context of structural inspection. In this study, a multi-scale task interaction deep learning strategy is presented towards component recognition, damage segmentation, and depth estimation for a comprehensive post-earthquake inspection of high-speed railway viaducts. Three modules for multi-scale task interaction were proposed to modify the multi-task deep neural network, taking full advantage of task commonalities at multiple scales. The proposed method was validated with a large-scale image dataset of high-speed railway viaducts. Component recognition and depth estimation were incorporated to implement multi-task learning since they have higher pattern affinities at multiple scales. Results reported that mean Intersection over Unions of testing samples of component and damage tasks were 91.2% and 72.1%, RMSE of depth estimation was 1.54 m. Compared with single-task cases, training time, inference duration, and FLOPs of the multi-task model were reduced by 23%, 30%, 27% respectively. Results showed improvement in both inference accuracy and training efficiency, substantiating the superiority of the proposed strategy.

Design of Bayesian evaluation test for missile damage effectiveness based on multiple damage level standards

The qualified missile damage effectiveness is an important guarantee for giving full play to combat effectiveness. In order to test whether the missile damage effectiveness meets the standard, it is particularly necessary to carry out damage effectiveness identification test research. In view of the limitation that the existing "single-shot damage probability" damage effectiveness quantitative characterization index is difficult to fully describe the missile damage effectiveness based on a single damage standard, considering the different damage level results formed by a single missile hitting the target, the damage effectiveness is characterized by the probability of occurrence of different damage level results. Missile identification from the perspective of multiple damage level standards is conducive to comprehensive inspection of missile damage effectiveness. In addition, in order to overcome the problem of insufficient information in small sample missile assembly tests, the Bayesian method is combined with the system contribution degree to fuse multi-source prior information in the identification test design, and a test scheme with full information utilization and controllable risks for both parties is designed. The example shows that compared with the quantitative characterization index of a single damage standard, this study can fully describe the missile damage effectiveness, and compared with the improved binomial distribution hypothesis test method, the superiority of this method is verified.

Development of track geometry inspection equipment for high-speed comprehensive inspection train in China

Development of track geometry inspection equipment for high-speed comprehensive inspection train in China

Sensor placement method for water distribution networks based on sampling of non-bandlimited graph signals

Monitoring water distribution networks (WDNs) requires careful consideration of sensor placement, which is essential for obtaining comprehensive information about the network. A natural graphical structure underlies WDN, making graph sampling theory advantageous for selecting monitoring nodes. However, graph sampling theory is only applied only to restrictive band-limited signals, while the pressure data of WDN is a restrictive non-band-limited signal. To address this issue, this paper presents an approximate conversion method for transforming non-band-limited signals into band-limited signals, accompanied by an optimal spectrum threshold formula. This formula is used to perform spectral screening in the graph frequency domain, effectively converting non-band-limited signals into band-limited signals that preserve the major frequency components while ignoring smaller-value frequency components. By sampling band-limited signal, we identify sampling nodes that perfectly recover the signal. These sampling nodes act as monitoring nodes that can perform a comprehensive inspection of the WDN and accurately locate leaks. The accuracy of our method in recovering the signal and locating the leak is demonstrated by comparing it with two existing sensor placement optimization methods.

The SG‐Climbot: An Adaptable, Efficient, Inspection and Repair Robot for Steam Generator Heat Transfer Tube Sheet

ABSTRACTThe steam generator plays a crucial role as a safety component and protective barrier in nuclear power plants. Regular inspection and repair of heat transfer tubes, which operate in high‐temperature, high‐pressure, corrosive, and abrasive environments for extended periods, are essential. Inspection and repair robots move inside the steam generator to conduct comprehensive inspections and evaluations of the heat transfer tubes. However, existing robots have relatively simple execution devices and fixed movement methods, resulting in poor adaptability to different specifications of tube sheets and limited flexibility in handling maintenance tasks. In response to these limitations, the SG‐Climbot, a quadruped crawling tube sheet inspection and repair robot, has been proposed. This robot is designed to adapt to full‐specification tube sheets and operate efficiently by conducting inspections while moving. By comparing different motion modes, a robot configuration with a quadruped parallel structure was suggested. Kinematic and static analyses were conducted, resulting in the development of both forward and inverse kinematic models, along with the identification of conditions leading to tipping. Using parametric modeling and optimization design, optimized design parameters were obtained. Finite element simulation analysis was performed on the overall structure and main components, leading to an optimized structural model. Finally, based on the design model and computational analysis, a prototype was developed and tested. The experimental results indicate that the robot has achieved the expected functionality and performance targets. Its efficient maintenance operation mode of conducting inspections while moving provides a basis for the autonomous and intelligent development of steam generators.

Implementation of a low-cost comprehensive pavement inspection system

Assessing the condition of roads is crucial to the maintenance and rehabilitation process as a country's progress is closely linked to its transport infrastructure. Therefore, it is essential to have well-maintained roads and to be able to control and monitor them properly. Technological advancements have transformed the way pavement inspections are carried out. This study presents an innovative approach that combines stereo cameras and a GPS module for efficient and accurate data collection. This integration of low-cost technologies provides a detailed three-dimensional view of pavements, complemented by accurate geospatial information. The experimental results showed that the 3D images of pavement damage had a relative volume measurement error of 0.80 %. Unlike traditional systems such as LIDAR and ground-penetrating radar, which involve more expensive technologies, the proposed method offers a cost-effective solution. This methodology not only simplifies the inspection process but also improves the planning and execution of road maintenance and repair activities. Its low cost makes it a viable option for various projects and applications in road infrastructure.

Development of a Small-Sized Urban Cable Conduit Inspection Robot

Cable conduits are crucial for urban power transmission and distribution systems. However, current conduit robots are often large and susceptible to tilting issues, which hampers the effective and intelligent inspection of these conduits. Therefore, there is an urgent need to develop a smaller-sized conduit inspection robot to address these challenges. Based on an in-depth analysis of the characteristics of the cable conduit working environment and the associated functional requirements, this study successfully developed a small-scale urban cable conduit inspection robot prototype. This development was grounded in relevant design theories, simulation analyses, and experimental tests. The test results demonstrate that the robot’s bracing module effectively prevents tilting within the conduit. Additionally, the detection module enables comprehensive 360-degree conduit inspections, and the vacuuming module meets the negative pressure requirements for efficient absorption of dust and foreign matter. The robot has met the expected design goals, effectively enhanced the automation of the cable conduit construction process, and improved the quality control of cable laying.

The application and development of drone vision technology in railway inspection industry

The inspection and preventive maintenance of railway facilities and their surrounding environments are critical tasks for ensuring the safety of railway operations. Inspection is the prerequisite for maintenance, and therefore, comprehensive inspection and maintenance methods can effectively reduce operational risks and ensure safe train operations. This paper summarizes the application of drone vision technology in railway inspections, analyzing the use of drones for the inspection of high-altitude equipment and environments, ground-level equipment and environments, and disaster prevention and control. It also reviews the recent applications of drone vision inspection technology in the railway industry, identifies the constraints, and offers a perspective on future developments.

A new paradigm for construction safety management in China: Introducing knowledge graph and accident database into the early-stage of BIM

Significant progress has been made in managing construction safety risks based on Building Information Modelling (BIM) technology. BIM is active in all stages of construction, including5 planning, design, pre-construction and construction. However, the causes of risks and accidents are complex and can be related to humans, the environment, data and project portraits. The current construction safety risk management process lacks scientific reference and efficiency in conducting comprehensive inspections for specific projects. The uneven and substandard data quality in BIM models significantly undermines their effectiveness. This article introduces a knowledge graph and accident database through data crawling and structuring, with the use and analysis of an accident causation model, providing key risk elements and management directions for safety managers who require a high level of attention during the early-stage of BIM. The obtained knowledge graph was subjected to data mining analysis to explore the causes of accidents, considering the combined effect of multiple factors, such as “case-date-cause” and “case-address-cause”. The paradigm can be recognized by the BIM, and through the secondary development on the development side, a data interface is constructed to transfer the knowledge graph into the BIM. The approach mentioned in the article can provide standard and structured data for the BIM model, analysing high-risk points of projects, which is valuable for engineering applications.

Instrumentation of the Eiffel Tower for Continuous Monitoring

The Eiffel Tower stands as a historic and iconic monument in the city of Paris. With a total height of 330 meters and constructed with wrought iron, it features a lattice metal structure. Completed in 1889 after two years of construction, the tower was initially designed for a 20-year lifespan. Now over 130 years old and welcoming millions of visitors annually, it has become a compelling example of extending the lifespan of a structure through vigilant surveillance and maintenance. Historically, the Eiffel Tower's surveillance program primarily consisted of repeated comprehensive and detailed inspections. Since 2021, it has been equipped with sensors to continuously monitor its overall behaviour. This instrumentation includes motoring of the spire’s inclination, as well as monitoring displacements, deformations, and vibrations in its four pillars. Temperature and humidity measurements are also acquired across the structure, to help providing a comprehensive analysis. This paper presents the implemented instrumentation, addressing challenges related to its deployment, and proposes a first analysis two years after the beginning of the campaign. Seasonal movements and trends are identified, and a brief dynamic study is conducted. Finally, the paper discusses the approach of instrumentation for exceptional structures to complete ongoing surveillance.

Drive-by modal identification of railway bridges via on-board measurements from a single railroad vehicle

The global railway network spans over one million kilometers of tracks, and this extensive infrastructure is set to expand even further. The objective is to promote rail transportation as an environmentally sustainable solution to address the growing demands of mobility. A significant portion of these tracks spans across bridge structures, which must also accommodate the rising demands for mobility and increased travel speeds, placing additional demands in terms of imposed loads. At the same time, bridges worldwide suffer from aging, leading to the deterioration of their structural integrity. Such combined effects necessitate monitoring the structural health of bridges to ensure the quality and safety of railway networks by detecting possible alterations in their response characteristics. Traditional Structural Health Monitoring (SHM) techniques use stationary sensors affixed to the bridge structure, enabling the direct assessment of the collected data. While such approaches are reliable, they present limitations in the comprehensive inspection of multiple railway bridges within a network. As an alternative, mobile vibration-based sensing, utilizing sensors on passing trains, presents the potential to gather data from multiple railway bridges using only a few sensors installed on board trains. Furthermore, operating the sensor-equipped trains at frequent intervals allows for collecting continuous data, offering valuable insights into the ongoing deterioration of bridges. In light of this, our work proposes a model-based methodology for extracting the modal frequencies of bridges based on acceleration data collected from traversing trains. Our approach hinges on Kalman filtering for estimating the state and input of the train and employs a subspace identification method to ascertain the frequencies of the bridge. The ultimate goal is to develop a drive-by bridge monitoring scheme that accommodates the assessment of the remaining lifespan of bridges as well as timely repairs in the event of damage, ensuring the safety and reliability of rail transportation.

Measurement of the concentration distribution of hydrogen jets using adaptive stream stripe- background oriented schlieren (ASS-BOS)

Hydrogen leakage diagnosis has attracted more and more attention, especially in the high-pressure hydrogen system of fuel cell vehicles, where the chance of deflagration is quite high. Traditional methods for detecting gas leaks rely on stationary sensor measurements and manual inspections. Manual inspections are time and resource-consuming, and it is challenging to achieve real-time, comprehensive inspections in large spaces. In this study, a new Adaptive Stream Stripe-Background Oriented Schlieren (ASS-BOS) method for hydrogen jet visualization is proposed. Three different background patterns, namely random scatter background, checkerboard background, and stripe background, are designed for multiple experiments. The results show that when observing hydrogen jets in the far-field region (low-density gradient), ASS-BOS has higher accuracy and resolution, detects larger background displacements, and shows more turbulence details. In addition, the concentration distribution of the hydrogen jet is reconstructed using ASS-BOS. We compare the concentration of the hydrogen jet obtained from the inversion of the ASS-BOS method with the experimental results measured by the sensor. We find that the average error is 3.45%, indicating that ASS-BOS can accurately reconstruct the concentration distribution of the hydrogen jet.

Comprehensive Inspection Solution for Boiler Water Wall Tube Corrosion

A substantial number of domestic coal-fired boilers, most of which have been in operation for numerous years, are susceptible to corrosion and tube ruptures within their high-temperature environment, often resulting in unplanned shutdowns. Despite the annual planned maintenance shutdowns, which involve routine inspections of water wall and furnace tubes to detect potential corrosion and erosion, the current non-destructive testing methods are generally localized. This approach, particularly with the considerable height variation of the water wall tubes (ranging from approximately 10 to 25 meters), may overlook segments with severe corrosion during localized ultrasonic thickness gauging. Although the experience of inspection teams reduces the chance of defect oversight, the demand for a comprehensive and effective inspection technique remains a priority for boiler operators. This study utilizes the characteristic of guided waves to propagate over a certain distance along pipelines, initiating an exploration into a comprehensive inspection solution for water wall tubes. Finite element simulation results demonstrate the suitability of utilizing a 120 kHz frequency for shear horizontal mode (SH0) testing on the fire side of water wall tubes. This mode effectively screens for localized corrosion within a 3-meter range. In the field of on-site boiler inspection, electromagnetic coils are employed to generate guided waves within water wall tubes. The gathered signals from each electromagnetic guided wave testing (EMGWT) are consolidated into a distribution map, indicating suspicious signal locations. Pulse eddy current testing (PECT) is employed to identify potential issues with guided waves without requiring surface preparation. If PECT reveals anomalies, the affected surface is polished, and phased array ultrasonic testing (PAUT) is conducted to quantitatively measure the remaining wall thickness due to corrosion. The theoretical exploration and on-site inspection outcomes affirm the feasibility of this approach, aimed at enhancing operational safety for boiler water wall tubes and mitigating the risks of tube ruptures and leaks. This comprehensive inspection solution amalgamates three advanced detection technologies, each capitalizing on its unique inspection characteristics to facilitate corrosion screening, corrosion scanning, and wall thickness sizing. Through the integration of these technologies, the overall inspection strategy ensures the utmost level of reliability.

Обеспечение безопасности труда горнорабочих совершенствованием электрических сетей при подземной добыче руд

In recent years, more attention has been paid to the increase in energy consumption by mining enterprises operating mines due to the need to replace the installed electric equipment and implement automated process management systems in mining and transport areas of the mine, which ultimately ensures safe and faultless operation of power supply system at various voltage levels in both individual subdivisions and the entire mine. During the experimental studies and solving problems, safe conditions for maintaining electric installations of the mine and maintenance personnel will be ensured. Weaknesses of the grid insulation and its elements have been detected; stable values for the resistance of motor insulation, switching equipment, laid cables, and the relevant probability distribution densities have been provided. This enables the replacement of individual unstable sectors of the power grid with new cables and equipment, which will significantly improve the safety of mining operations in the conditions of a high-altitude mine. It has been established that the motors used as actuators for scraper winches and fans must be replaced with motors of similar power equipped with organosilicon insulation or filling of frontal parts with enamel paint. The conducted studies enable the development of a comprehensive inspection methodology of the mine power supply system’s insulation elements and its application for further studies at other mines.

Association between regulator inspection and ratings on primary care prescribing: an observational study in England 2014 to 2019

BackgroundHealthcare regulators in many countries undertake inspections of healthcare providers and publish inspection outcomes with the intention of improving quality of care. Comprehensive inspections of general practices in England by the Care Quality Commission began for the first time in 2014. It is assumed that inspection and rating will raise standards and improve care, but the presence and extent of any improvements is unknown. We aim to determine if practice inspection ratings are associated with past performance on prescribing indicators and if prescribing behaviour changes following inspection.MethodsLongitudinal study using a dataset of 6771 general practices in England. Practice inspection date and score was linked with monthly practice-level data on prescribing indicators relating to antibiotics, hypnotics and non-steroidal anti-inflammatory drugs. The sample covers practices receiving their first inspection between September 2014 and December 2018. Regression analysis and the differential timing of inspections is used to identify the impact on prescribing.ResultsBetter-rated practices had better prescribing in the period before inspections began. In the six months following inspections, no overall change in prescribing was observed. However, the differences between the best and worse rated practices were reduced but not fully. The same is also true when taking a longer-term view. There is little evidence that practices responded in anticipation of inspection or reacted differently once the ratings were made public.ConclusionWhile some of the observed historic variation in prescribing behaviour has been lessened by the process of inspection and ratings, we find this change is small and appears to come from both improvements among lower-rated practices and deteriorations among higher-rated practices. While inspection and rating no doubt had other impacts, these prescribing indicators were largely unchanged.

UAV vision-based crack quantification and visualization of bridges: system design and engineering application

Accurately measuring visible cracks in bridges is crucial for their structural health diagnosis, damage detection, performance evaluation, and maintenance planning. The primary means of visual crack detection still relies heavily on manual visual inspection, an inefficient process that can pose significant safety risks. This article develops a unmanned aerial vehicle (UAV) vision-based surface crack measurement methodology and visualization scheme for the bridges that can detect and measure cracks automatically with improved efficiency. The surface crack measurement methodology is achieved by designing a three-stage crack sensing system including the You Only Look Once-based crack recognition, U-shaped network-based crack segmentation, and deep-vision-based crack width calculation. This workflow is integrated into a comprehensive UAV inspection system, which is intended for operation at the field. The surface crack visualization scheme is accomplished by taking advantage of time-series image fusion, GPS information migration, and three-dimensional (3D) point cloud technique to reconstruct the 3D geometrical model of the tested bridge, which is convenient for unveiling the crack information in the bridge. The proposed methodology was successfully validated by a case study on an arch bridge. The achievement of this article promotes the UAV vision-based bridge’s surface crack inspection technology to a new status that no preparation for pasting calibration marker is needed, and crack identification, segmentation, and width calculation are realized promptly during the UAV flying on-site, as well as damage evaluation for bridges is visually fulfilled based on the reconstructed digital-graphical 3D model. The working environments and influencing factors to the developed system are sufficiently discussed. Certain limitations in the current application are pointed out for future improvements.

Enhancing Tower Crane Safety: A UAV-Based Intelligent Inspection Approach

Tower cranes play a crucial role in the construction industry, facilitating the vertical and horizontal movement of materials and aiding in building construction, especially for high-rise structures. However, tower crane accidents can lead to severe consequences, highlighting the importance of effective safety management and inspection. This paper presents a novel approach to tower crane safety inspections using Unmanned Aerial Vehicles (UAVs) equipped with high-definition cameras and an intelligent inspection APP system. The system utilizes real-time kinematic (RTK) positioning and digital image processing to perform efficient and comprehensive inspections, reducing the reliance on manual labor and associated risks. A case study demonstrated the method’s practicality and effectiveness, with the UAV inspections capable of identifying 76.3% of major hazards, 64.8% of significant hazards, and 76.2% of general hazards within a 30-minute timeframe. Preliminary identification rates were also promising. Despite the initial requirement for manual drone piloting and the current manual review of images, the approach shows significant potential for enhancing safety in the construction industry. Future work will focus on integrating AI for hazard recognition and automating the inspection process further. The proposed method marks a step forward in tower crane safety management, offering a more efficient and accurate alternative to traditional inspection methods.

Design and Testing of an Autonomous Navigation Unmanned Surface Vehicle for Buoy Inspection

In response to the inefficiencies and high costs associated with manual buoy inspection, this paper presents the design and testing of an Autonomous Navigation Unmanned Surface Vehicle (USV) tailored for this purpose. The research is structured into three main components: Firstly, the hardware framework and communication system of the USV are detailed, incorporating the Robot Operating System (ROS) and additional nodes to meet practical requirements. Furthermore, a buoy tracking system utilizing the Kernelized Correlation Filter (KCF) algorithm is introduced. Secondly, buoy image training is conducted using the YOLOv7 object detection algorithm, establishing a robust model for accurate buoy state recognition. Finally, an improved Line-of-Sight (LOS) method for USV path tracking, assuming the presence of an attraction potential field around the inspected buoy, is proposed to enable a comprehensive 360-degree inspection. Experimental testing includes validation of buoy image target tracking and detection, assessment of USV autonomous navigation and obstacle avoidance capabilities, and evaluation of the enhanced LOS path tracking algorithm. The results demonstrate the USV’s efficacy in conducting practical buoy inspection missions. This research contributes insights and advancements to the fields of maritime patrol and routine buoy inspections.

A novel sensor based on the composite mechanism of magnetic flux leakage and magnetic field disturbance for comprehensive inspection of defects with varying angles and widths

Ferromagnetic components are susceptible to various defects after prolonged service. The defects generated in these components are diverse and exhibit irregular orientation, which may impede comprehensive defect examination using a single magnetic testing method. This paper proposes a novel magnetic measurement sensor that integrates the principles of magnetic flux leakage (MFL) and magnetic field disturbance (MFD). The combined theory is analyzed and validated through the utilization of finite element method (FEM) simulations as well as experimental investigations. By comparing simulation results with experimental data, the composite nature of the signal acquired beneath a magnetic bridge is convincingly demonstrated. In comparison to conventional MFL and MFD methods, this innovative sensor exhibits superior defect detection performance for varying angles and widths, while also addressing some inherent limitations associated with MFL and MFD principles.

IMPLEMENTATION OF THE PRISM SURVEY FORM TO CREATE SEISMIC RISK MAPS

This study details a seismic risk assessment of stone and adobe masonry structures in the Agios Dometios Municipality, Nicosia, Cyprus. The study integrates field inspections using the PRISM survey form, designed exclusively for masonry buildings, with advanced analytical procedures. The initial phase involves comprehensive field inspections to evaluate the structural condition and vulnerabilities of these historic masonry buildings. The data gathered from these inspections provide essential insights into potential seismic risks. This data, alongside fragility curves generated from 3D finite element (FE) models using nonlinear time-history analysis, informs the creation of seismic risk maps for Agios Dometios. These maps offer a comprehensive depiction of the area's seismic vulnerability, particularly concerning the masonry structures, serving as invaluable resources for local authorities and conservationists in strategizing preservation and reinforcement measures, and in formulating mitigation plans. This study's approach, blending field inspections with advanced computational analysis, provides a robust framework for assessing the seismic risks of masonry structures. It holds significant implications for preserving architectural heritage in seismically active regions, offering a model that can be replicated in similar urban contexts.