Review on Machine Learning-based Defect Detection of Shield Tunnel Lining

At present, machine learning methods are widely used in various industries for their high adaptability, optimization function, and self-learning reserve function. Besides, the world-famous cities have almost built and formed subway networks that promote economic development. This paper presents the art states of Defect detection of Shield Tunnel lining based on Machine learning (DSTM). In addition, the processing method of image data from the shield tunnel is being explored to adapt to its complex environment. Comparison and analysis are used to show the performance of the algorithms in terms of the effects of data set establishment, algorithm selection, and detection devices. Based on the analysis results, Convolutional Neural Network methods show high recognition accuracy and better adaptability to the complexity of the environment in the shield tunnel compared to traditional machine learning methods. The Support Vector Machine algorithms show high recognition performance only for small data sets. To improve detection models and increase detection accuracy, measures such as optimizing features, fusing algorithms, creating a high-quality data set, increasing the sample size, and using devices with high detection accuracy can be recommended. Finally, we analyze the challenges in the field of coupling DSTM, meanwhile, the possible development direction of DSTM is prospected

Importance and applications of robotic and autonomous systems (RAS) in railway maintenance sector: a review

Maintenance, which is critical for safe, reliable, quality, and cost-effective service, plays a dominant role in the railway industry. Therefore, this paper examines the importance and applications of Robotic and Autonomous Systems (RAS) in railway maintenance. More than 70 research publications, which are either in practice or under investigation describing RAS developments in the railway maintenance, are analysed. It has been found that the majority of RAS developed are for rolling-stock maintenance, followed by railway track maintenance. Further, it has been found that there is growing interest and demand for robotics and autonomous systems in the railway maintenance sector, which is largely due to the increased competition, rapid expansion and ever-increasing expense

Railway freight transport and logistics: Methods for relief, algorithms for verification and proposals for the adjustment of tunnel inner surfaces

In Europe, the attention to efficiency and safety of international railway freight transport has grown in recent years and this has drawn attention to the importance of verifying the clearance between vehicle and lining, mostly when different and variable rolling stock types are expected. This work consists of defining an innovative methodology, with the objective of surveying the tunnel structures, verifying the clearance conditions, and designing a retrofitting work if necessary. The method provides for the use of laser scanner, thermocameras, and ground penetrating radar to survey the geometrical and structural conditions of the tunnel; an algorithm written by the authors permits to verify the clearances. Two different types of works are possible if the inner tunnel surfaces interfere with the profile of the rolling stock passing through: modification of the railroad track or modification of the tunnel intrados by mean milling of its lining. The presented case study demonstrates that the proposed methodology is useful for verifying compatibility between the design vehicle gauge and the existing tunnel intrados, and to investigate the chance to admit rolling stocks from different states. Consequently, the results give the railway management body a chance to perform appropriate measurements in those cases where the minimum clearance requirements are not achieved

Crack detection in concrete tunnels using a gabor filter invariant to rotation

Producción CientíficaIn this article, a system for the detection of cracks in concrete tunnel surfaces, based on image sensors, is presented. Both data acquisition and processing are covered. Linear cameras and proper lighting are used for data acquisition. The required resolution of the camera sensors and the number of cameras is discussed in terms of the crack size and the tunnel type. Data processing is done by applying a new method called Gabor filter invariant to rotation, allowing the detection of cracks in any direction. The parameter values of this filter are set by using a modified genetic algorithm based on the Differential Evolution optimization method. The detection of the pixels belonging to cracks is obtained to a balanced accuracy of 95.27%, thus improving the results of previous approaches.Ministerio de Economía y Competitividad under project Ref. IPT-2012-0980-370000Ministerio de Ciencia e Innovación, research project Ref. DPI2014-56500Junta de Castilla y León Ref. VA036U14

Automatic Road Crack Segmentation Using Thresholding Methods

Maintenance of good condition of roads are very essential to the economy and everyday life of people in a every country. Road cracks are one of the important indicators that show degradations of road surfaces. Inspection of roads that have been done manually took a very long time and tedious. Hence, an automatic road crack segmentation using thresholding methods have been proposed in this study. In this study, ten road crack images have been pre-processed as an initial step. Then, normalization techniques, L1-Sqrt norm have been applied onto images to reduce the variation of intensities that skewed to the right. Then, thresholding methods, Otsu and Sauvola methods have been used to binarize the images.  From the experiment of ten road crack images that have been done, normalization technique, L1-Sqrt norm can help to increase performance of road crack segmentation for Otsu and Sauvola methods. The results also show that Sauvola method outperform Otsu method in detecting road cracks

Challenges of bridge maintenance inspection

Bridges are amongst the largest, most expensive and complex structures, which makes them crucial and valuable transportation asset for modern infrastructure. Bridge inspection is a crucial component of monitoring and maintaining these complex structures. It provides a safety assessment and condition documentation on a regular basis, noting maintenance actions needed to counteract defects like cracks, corrosion and spalling. This paper presents the challenges with existing bridge maintenance inspection as well as an overview on proposed methods to overcome these challenges by automating inspection using computer vision methods. As a conclusion, existing methods for automated bridge inspection are able to detect one class of damage type based on images. A multiclass approach that also considers the 3D geometry, as inspectors do, is missing

Performance Comparison of Hybrid CNN-SVM and CNN-XGBoost models in Concrete Crack Detection

Detection of cracks mainly has been a sort of essential step in visual inspection involved in construction engineering as it is the commonly used building material and cracks in them is an early sign of de-basement. It is hard to find cracks by a visual check for the massive structures. So, the development of crack detecting systems generally has been a critical issue. The utilization of contextual image processing in crack detection is constrained, as image data usually taken under real-world situations vary widely and also includes the complex modelling of cracks and the extraction of handcrafted features. Therefore the intent of this study is to address the above problem using two-hybrid machine learning models and classify the concrete digital images into having cracks or non-cracks. The Convolutional Neural Network is used in this study to extract features from concrete pictures and use the extracted features as inputs for other machine learning models, namely Support Vector Machines (SVMs) and Extreme Gradient Boosting (XGBoost). The proposed method is evaluated on a collection of 40000 real concrete images, and the experimental results show that application of XGBoost classifier to CNN extracted image features include an advantage over SVM approach in accuracy and achieve a relatively better performance than a few existing methods

A Survey on Audio-Video based Defect Detection through Deep Learning in Railway Maintenance

Within Artificial Intelligence, Deep Learning (DL) represents a paradigm that has been showing unprecedented performance in image and audio processing by supporting or even replacing humans in defect and anomaly detection. The Railway sector is expected to benefit from DL applications, especially in predictive maintenance applications, where smart audio and video sensors can be leveraged yet kept distinct from safety-critical functions. Such separation is crucial, as it allows for improving system dependability with no impact on its safety certification. This is further supported by the development of DL in other transportation domains, such as automotive and avionics, opening for knowledge transfer opportunities and highlighting the potential of such a paradigm in railways. In order to summarize the recent state-of-the-art while inquiring about future opportunities, this paper reviews DL approaches for the analysis of data generated by acoustic and visual sensors in railway maintenance applications that have been published until August 31st, 2021. In this paper, the current state of the research is investigated and evaluated using a structured and systematic method, in order to highlight promising approaches and successful applications, as well as to identify available datasets, current limitations, open issues, challenges, and recommendations about future research directions

Deep Learning Based Concrete Distress Detection System for Civil Infrastructure

In most civil concrete structures, the inspection of structural health is essential. A periodical inspection process ensures that the infrastructure will meet the functional requirements properly or not. To avoid hazardous situations in civil infrastructure, proper maintenance of concrete structures is necessary. The manual visual examination process might provide erroneous results while exploring critical parts of concrete surfaces. As a result, an accurate, safe, and dependable automated process is required for detecting concrete distress. Spalling is a critical distress that can damage concrete surfaces in civil infrastructure. Severe and harmful spalling needs to be taken care of to avoid life-threatening incidents by identifying the location of the distress. Aside from determining the location of the spalling, the severity level of the spalling must also be determined. These severity levels help determine how adverse the situation is and prioritize the process of fixing the spalling. Due to the impact of concrete distress, detecting surface defects like spallings caught the attention of researchers. In this thesis, we have presented approaches to detecting the location of spalling according to its severity level. The proposed methods use deep learning-based approaches and multi-class semantic segmentation. Our approaches have explored two major criteria to detect the spalling and categorize its severity level. Furthermore, we have conducted qualitative and quantitative analyses to demonstrate the performance achieved by the proposed methodologies