• Smart Structures and Systems
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• v.24 no.2
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• pp.243-256
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• 2019

Determination of the most meaningful structural modes and gaining insight into how these modes evolve are important issues for long-term structural health monitoring of the long-span bridges. To address this issue, modal parameters identified throughout the life of the bridge need to be compared and linked with each other, which is the process of mode tracking. The modal frequencies for a long-span bridge are typically closely-spaced, sensitive to the environment (e.g., temperature, wind, traffic, etc.), which makes the automated tracking of modal parameters a difficult process, often requiring human intervention. Machine learning methods are well-suited for uncovering complex underlying relationships between processes and thus have the potential to realize accurate and automated modal tracking. In this study, Gaussian mixture model (GMM), a popular unsupervised machine learning method, is employed to automatically determine and update baseline modal properties from the identified unlabeled modal parameters. On this foundation, a new mode tracking method is proposed for automated mode tracking for long-span bridges. Firstly, a numerical example for a three-degree-of-freedom system is employed to validate the feasibility of using GMM to automatically determine the baseline modal properties. Subsequently, the field monitoring data of a long-span bridge are utilized to illustrate the practical usage of GMM for automated determination of the baseline list. Finally, the continuously monitoring bridge acceleration data during strong typhoon events are employed to validate the reliability of proposed method in tracking the changing modal parameters. Results show that the proposed method can automatically track the modal parameters in disastrous scenarios and provide valuable references for condition assessment of the bridge structure.

• Geomechanics and Engineering
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• v.23 no.6
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• pp.513-521
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• 2020

In this paper, due to the need for cutting cement-soil group pile composite foundation under the 7-story masonry structure of Zhenghe District and the shield tunnel of Zhengzhou Metro Line 5, a field test was conducted to directly cut cement-soil single pile composite foundation with diameter Ф=500 mm. Research results showed that the load transfer mechanism of composite foundation was not changed before and after shield tunnel cut the pile, and pile body and the soil between piles was still responsible for overburden load. The construction disturbance of shield cutting pile is a complicated mechanical process. The load carried by the original pile body was affected by the disturbance effect of pile cutting construction. Also, the fraction of the load carried by the original pile body was transferred to the soil between the piles and therefore, the bearing capacity of composite foundation was not decreased. Only the fractions of the load carried by pile and the soil between piles were distributed. On-site monitoring results showed that the settlement of pressure-bearing plates produced during shield cutting stage accounted for about 7% of total settlement. After the completion of pile cutting, the settlements of bearing plates generated by shield machine during residual pile composite foundation stage and shield machine tail were far away from residual pile composite foundation stage which accounted for about 15% and 74% of total settlement, respectively. In order to reduce the impact of shield cutting pile construction on the settlement of upper composite foundation, it was recommended to take measures such as optimization of shield construction parameters, radial grouting reinforcement and "clay shock" grouting within the disturbance range of shield cutting pile construction. Before pile cutting, the pile-soil stress ratio n of composite foundation was 2.437. After the shield cut pile is completed, the soil around the lining structure is gradually consolidated and reshaped, and residual pile composite foundation reaches a new state of force balance. This was because the condensation of grouting layer could increase the resistance of remaining pile end and friction resistance of the side of the pile.

• Structural Engineering and Mechanics
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• v.77 no.1
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• pp.47-56
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• 2021

This paper deals with damage detection using a Gapped Smoothing Method (GSM) combined with deep learning. Convolutional Neural Network (CNN) is a model of deep learning. CNN has an input layer, an output layer, and a number of hidden layers that consist of convolutional layers. The input layer is a tensor with shape (number of images) × (image width) × (image height) × (image depth). An activation function is applied each time to this tensor passing through a hidden layer and the last layer is the fully connected layer. After the fully connected layer, the output layer, which is the final layer, is predicted by CNN. In this paper, a complete machine learning system is introduced. The training data was taken from a Finite Element (FE) model. The input images are the contour plots of curvature gapped smooth damage index. A free-free beam is used as a case study. In the first step, the FE model of the beam was used to generate data. The collected data were then divided into two parts, i.e. 70% for training and 30% for validation. In the second step, the proposed CNN was trained using training data and then validated using available data. Furthermore, a vibration experiment on steel damaged beam in free-free support condition was carried out in the laboratory to test the method. A total number of 15 accelerometers were set up to measure the mode shapes and calculate the curvature gapped smooth of the damaged beam. Two scenarios were introduced with different severities of the damage. The results showed that the trained CNN was successful in detecting the location as well as the severity of the damage in the experimental damaged beam.

• Geomechanics and Engineering
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• v.24 no.4
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• pp.323-335
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• 2021

This paper aims to estimate the range of the excavation damaged zone (EDZ) formation caused by the tunnel boring machine (TBM) advancement through dynamic three-dimensional large deformation finite element analysis. Large deformation analysis based on Coupled Eulerian-Lagrangian (CEL) analysis is used to accurately simulate the behavior during TBM excavation. The analysis model is verified based on numerous test results reported in the literature. The range of the formed EDZ will be suggested as a boundary under various conditions - different tunnel diameter, tunnel depth, and rock type. Moreover, evaluation of the integrity of the tunnel structure during excavation has been carried out. Based on the numerical results, the apparent boundary of the EDZ is shown to within the range of 0.7D (D: tunnel diameter) around the excavation surface. Through series of numerical computation, it is clear that for the rock of with higher rock mass rating (RMR) grade (close to 1st grade), the EDZ around the tunnel tends to increase. The size of the EDZ is found to be direct proportional to the tunnel diameter, whereas the depth of the tunnel is inversely proportional to the magnitude of the EDZ. However, the relationship between the formation of the EDZ and the stability of the tunnel was not found to be consistent. In case where the TBM excavation is carried out in hard rock or rock under high confinement (excavation under greater depth), large range of the EDZ may be formed, but less strain occurs along the excavation surface during excavation and is found to be more stable.

• Geomechanics and Engineering
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• v.30 no.1
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• pp.11-26
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• 2022

One of the most important issues in tunneling, is the squeezing phenomenon. Squeezing can occur during excavation or after the construction of tunnels, which in both cases could lead to significant damages. Therefore, it is important to predict the squeezing and consider it in the early design stage of tunnel construction. Different empirical, semi-empirical and theoretical-analytical methods have been presented to determine the squeezing. Therefore, it is necessary to examine the ability of each of these methods and identify the best method among them. In this study, squeezing in a part of the Alborz service tunnel in Iran was estimated through a number of empirical, semi- empirical and theoretical-analytical methods. Among these methods, the most robust model was used to obtain a database including 300 data for training and 33 data for testing in order to develop a machine learning (ML) method. To this end, three ML models of Gaussian process regression (GPR), artificial neural network (ANN) and support vector regression (SVR) were trained and tested to propose a robust model to predict the squeezing phenomenon. A comparative analysis between the conventional and the ML methods utilized in this study showed that, the GPR model is the most robust model in the prediction of squeezing phenomenon. The sensitivity analysis of the input parameters using the mutual information test (MIT) method showed that, the most sensitive parameter on the squeezing phenomenon is the tangential strain (ε_θ^α) parameter with a sensitivity score of 2.18. Finally, the GPR model was recommended to predict the squeezing phenomenon in tunneling projects. This work's significance is that it can provide a good estimation of the squeezing phenomenon in tunneling projects, based on which geotechnical engineers can take the necessary actions to deal with it in the pre-construction designs.

• Geomechanics and Engineering
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• v.29 no.3
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• pp.321-329
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• 2022

Tunnel site where high water pressure is applied, such as subsea tunnel, generally selects the shield TBM (Tunnel Boring Machine) to maintain the tunnel excavation face. The shield TBM has cutters installed, and the cutters wear out during the process of excavation, so it should be checked and replaced regularly. This is called CHI (Cutterhead Intervention). The conventional CHI under high water pressure is very disadvantageous in terms of safety and economics because humans perform work in response to high water pressure and huge water inflow in the chamber. To overcome this disadvantage, this study proposes a new method to dramatically reduce water pressure and water ingress by injecting an appropriate grout solution into the front of the tunnel face through the shield TBM chamber, called New Face Grouting Method (NFGM). The tunnel model tests were performed to determine the characteristics, injection volume, and curing time of grout solution to be applied to the NFGM. Model test apparatus was composed of a pressure soil tank, a model shield TBM, a grout tank, and an air compressor to measure the amount of water inflow into the chamber. The model tests were conducted by changing the injection amount of the grout solution, the curing time after the grout injection, and the water/cement ratio of grout solution. From an economic point of view, the results showed that the injection volume of 1.0 L, curing time of 6 hours, and water/cement ratio of the grout solution between 1.5 and 2.0 are the most economical. It can be concluded that this study has presented a method to economically perform the CHI under the high water pressure.

• Smart Structures and Systems
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• v.29 no.6
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• pp.757-766
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• 2022

To train deep learning algorithms, a sufficient number of data are required. However, in most engineering systems, the acquisition of fault data is difficult or sometimes not feasible, while normal data are secured. The dearth of data is one of the major challenges to developing deep learning models, and fault diagnosis in particular cannot be made in the absence of fault data. With this context, this paper proposes an anomaly detection methodology for rotating machines using only normal data with self-labeling. Since only normal data are used for anomaly detection, a self-labeling method is used to generate a new labeled dataset. The overall procedure includes the following three steps: (1) transformation of normal data to self-labeled data based on a pretext task, (2) training the convolutional neural networks (CNN), and (3) anomaly detection using defined anomaly score based on the softmax output of the trained CNN. The softmax value of the abnormal sample shows different behavior from the normal softmax values. To verify the proposed method, four case studies were conducted, on the Case Western Reserve University (CWRU) bearing dataset, IEEE PHM 2012 data challenge dataset, PHMAP 2021 data challenge dataset, and laboratory bearing testbed; and the results were compared to those of existing machine learning and deep learning methods. The results showed that the proposed algorithm could detect faults in the bearing testbed and compressor with over 99.7% accuracy. In particular, it was possible to detect not only bearing faults but also structural faults such as unbalance and belt looseness with very high accuracy. Compared with the existing GAN, the autoencoder-based anomaly detection algorithm, the proposed method showed high anomaly detection performance.

• Composite Materials and Engineering
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• v.3 no.3
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• pp.201-220
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• 2021

In this study, potential of three machine learning techniques i.e., M5P, Support vector machines and Gaussian processes were evaluated to find the best algorithm for the prediction of flexural strength of concrete mix with steel fibre. The study comprises the comparison of results obtained from above-said techniques for given dataset. The dataset consists of 124 observations from past research studies and this dataset is randomly divided into two subsets namely training and testing datasets with (70-30)% proportion by weight. Cement, fine aggregates, coarse aggregates, water, super plasticizer/ high-range water reducer, steel fibre, fibre length and curing days were taken as input parameters whereas flexural strength of the concrete mix was taken as the output parameter. Performance of the techniques was checked by statistic evaluation parameters. Results show that the Gaussian process technique works better than other techniques with its minimum error bandwidth. Statistical analysis shows that the Gaussian process predicts better results with higher coefficient of correlation value (0.9138) and minimum mean absolute error (1.2954) and Root mean square error value (1.9672). Sensitivity analysis proves that steel fibre is the significant parameter among other parameters to predict the flexural strength of concrete mix. According to the shape of the fibre, the mixed type performs better for this data than the hooked shape of the steel fibre, which has a higher CC of 0.9649, which shows that the shape of fibers do effect the flexural strength of the concrete. However, the intricacy of the mixed fibres needs further investigations. For future mixes, the most favorable range for the increase in flexural strength of concrete mix found to be (1-3)%.

• Advances in Computational Design
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• v.7 no.2
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• pp.113-128
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• 2022

Water consumption is strongly affected by numerous factors, such as population, climatic, geographic, and socio-economic factors. Therefore, the implementation of a reliable predictive model of water consumption pattern is challenging task. This study investigates the performance of predictive models based on multi-layer perceptron (MLP), multiple linear regression (MLR), and support vector regression (SVR). To understand the significant factors affecting water consumption, the stepwise regression (SW) procedure is used in MLR to obtain suitable variables. Then, this study also implements three predictive models based on these significant variables (e.g., SWMLR, SWMLP, and SWSVR). Annual data of water consumption in Thailand during 2006 - 2015 were compiled and categorized by provinces and distributors. By comparing the predictive performance of models with all variables, the results demonstrate that the MLP models outperformed the MLR and SVR models. As compared to the models with selected variables, the predictive capability of SWMLP was superior to SWMLR and SWSVR. Therefore, the SWMLP still provided satisfactory results with the minimum number of explanatory variables which in turn reduced the computation time and other resources required while performing the predictive task. It can be concluded that the MLP exhibited the best result and can be utilized as a reliable water demand predictive model for both of all variables and selected variables cases. These findings support important implications and serve as a feasible water consumption predictive model and can be used for water resources management to produce sufficient tap water to meet the demand in each province of Thailand.

• Smart Structures and Systems
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• v.29 no.1
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• pp.251-266
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• 2022

Structural Health Monitoring (SHM) of critical infrastructure comprises a major pillar of maintenance management, shielding public safety and economic sustainability. Although SHM is usually associated with data-driven metrics and thresholds, expert judgement is essential, especially in cases where erroneous predictions can bear casualties or substantial economic loss. Considering that visual inspections are time consuming and potentially subjective, artificial-intelligence tools may be leveraged in order to minimize the inspection effort and provide objective outcomes. In this context, timely detection of sensor malfunctioning is crucial in preventing inaccurate assessment and false alarms. The present work introduces a sensor-fault detection and interpretation framework, based on the well-established support-vector machine scheme for anomaly detection, combined with a coalitional game-theory approach. The proposed framework is implemented in two datasets, provided along the 1st International Project Competition for Structural Health Monitoring (IPC-SHM 2020), comprising acceleration and cable-load measurements from two real cable-stayed bridges. The results demonstrate good predictive performance and highlight the potential for seamless adaption of the algorithm to intrinsically different data domains. For the first time, the term "decision trajectories", originating from the field of cognitive sciences, is introduced and applied in the context of SHM. This provides an intuitive and comprehensive illustration of the impact of individual features, along with an elaboration on feature dependencies that drive individual model predictions. Overall, the proposed framework provides an easy-to-train, application-agnostic and interpretable anomaly detector, which can be integrated into the preprocessing part of various SHM and condition-monitoring applications, offering a first screening of the sensor health prior to further analysis.