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

Integrated Condition Assessment of Subway Networks Using Computer Vision and Nondestructive Evaluation Techniques

Subway networks play a key role in the smart mobility of millions of commuters in major metropolises. The facilities of these networks constantly deteriorate, which may compromise the integrity and durability of concrete structures. The ASCE 2017 Report Card revealed that the condition of public transit infrastructure in the U.S. is rated D-; hence a rehabilitation backlog of $90 billion is estimated to improve transit status to good conditions. Moreover, the Canadian Urban Transit Association (CUTA) reported 56.6 billion CAD in infrastructure needs for the period 2014-2018. The inspection and assessment of metro structures are predominantly conducted on the basis of Visual Inspection (VI) techniques, which are known to be time-consuming, costly, and qualitative in nature. The ultimate goal of this research is to develop an integrated condition assessment model for subway networks based on image processing, Artificial Intelligence (AI), and Non-Destructive Evaluation (NDE) techniques. Multiple image processing algorithms are created to enhance the crucial clues associated with RGB images and detect surface distresses. A complementary scheme is structured to channel the resulted information to Artificial Neural Networks (ANNs) and Regression Analysis (RA) techniques. The ANN model comprises sequential processors that automatically detect and quantify moisture marks (MM) defects. The RA model predicts spalling/scaling depth and simulates the de-facto scene by developing a hybrid algorithm and interactive 3D presentation. In addition, a comparative analysis is performed to select the most appropriate NDE technique for subway inspection. This technique is applied to probe the structure and measure the subsurface defects. Also, a novel model for the detection of air voids and water voids is proposed. The Fuzzy Inference System (FIS), Adaptive Neuro-Fuzzy Inference System (ANFIS), and Monte Carlo Simulation (MCS) are streamlined through successive operations to create the integrated condition assessment model. To exemplify and validate the proposed methodology, a myriad of images and profiles are collected from Montréal Metro systems. The results ascertain the efficacy of the developed detection algorithms. The attained recall, precision, and accuracy for MM detection algorithm are 93.2%, 96.1%, and 91.5% respectively. Whereas for spalling detection algorithm, are 91.7%, 94.8%, and 89.3% respectively. The mean and standard deviation of error percentage in MM region extraction are 12.2% and 7.9% respectively. While for spalling region extraction, they account for 11% and 7.1% respectively. Subsequent to selecting the Ground Penetrating Radar (GPR) for subway inspection, attenuation maps are generated by both the amplitude analysis and image-based analysis. Thus, the deteriorated zones and corrosiveness indices for subway elements are automatically computed. The ANN and RA models are validated versus statistical tests and key performance metrics that indicated the average validity of 96% and 93% respectively. The air/water voids model is validated through coring samples, camera images, infrared thermography and 3D laser scanning techniques. The validation outcomes reflected a strong correlation between the different results. A sensitivity analysis is conducted showing the influence of the studied subway elements on the overall subway condition. The element condition index using neuro-fuzzy technique indicated different conditions in Montréal subway systems, ranging from sound concrete to very poor, represented by 74.8 and 35.1 respectively. The fuzzy consolidator extrapolated the subway condition index of 61.6, which reveals a fair condition for Montréal Metro network. This research developed an automated tool, expected to improve the quality of decision making, as it can assist transportation agencies in identifying critical deficiencies, and by focusing constrained funding on most deserving assets

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

Shaking table model tests of reinforced concrete tunnels under multiple earthquake shakings

The cumulative effect of multiple relatively low or moderate seismic events on tunnels is not well-understood within an earthquake prone region. To investigate the effect of multiple earthquakes on the integrity of tunnel structures, 1 g shaking table tests were performed. This research also explored the impact of tunnel presence on the soil response, namely analyzing soil-structure interaction effects. Within the tests, a free-field model and a soil-tunnel model were employed synchronously. The shaking table study was designed and conducted following a new set of scaling laws able to faithfully simulate cracking of tunnel lining, and white noise tests were applied after each seismic shaking for dynamic identification. Except for the measurement of acceleration and bending strain, a new cracking monitoring system equipped with wireless mini-cameras was proposed to detect the evolution of tunnel damages during the tests, while Light Detection and Ranging (LiDAR) technology was utilized to examine the ground deformations in the two model configurations. Based on the point cloud data, it was observed that sand densification effects were obvious in the two models and the influence of tunnel presence on the soil response was restricted in a limited region. The trend in the evolution of an image-based damage index kept similar to that in the progression of surface settlements, implying that the seismically-induced ground failure might play an important role in the seismic response of shallow tunnels. Also, the frequency shifting behaviour of lining did not follow the intuitive pattern, where a reduction in natural frequencies is expected when structural damage occurs. Moreover, the variation of acceleration amplification factors of the tunnel was almost consistent with that of the soil, and the trend of strain agreed with that of surface settlements. The findings from this study provide an insight to better understand the resilience and life-long performance of earthquakes exposed underground structures

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

Proceedings of the 8th International Conference on Civil Engineering

This open access book is a collection of accepted papers from the 8th International Conference on Civil Engineering (ICCE2021). Researchers and engineers have discussed and presented around three major topics, i.e., construction and structural mechanics, building materials, and transportation and traffic. The content provide new ideas and practical experiences for both scientists and professionals

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

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