Vol.37/No.4 (146) (2022)
Special Issue: Application of Artificial Intelligence in Structural Engineering
Guest Editor: Tzu-Kang Lin
| Title | Framework of Advanced Building Inspection withRoute Planning, Defect Detection, and Damage Rating |
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| Author | Shun-Hsiang Hsu, Ho-Tin Hung, Yun-Man Hsu, Chia-Ming Chang, Tzung-Wu Chen, Chun-Chung Chen |
| Keywords | visual inspection; damage detection; damage quantification; deep learning |
| Abstract | Visual inspection is commonly adopted for building operation, maintenance, and safety. The durability and defects of components or materials in buildings can be quickly assessed through visual inspection. However, implementations of visual inspection are substantially time-consuming, labor-intensive, and error-prone because useful auxiliary tools that can instantly highlight defects or damage locations from images are not available. Therefore, an advanced building inspection framework is developed and implemented with route planning, realtime and detailed damage recognition, and damage rating in this study. The inspection route sketching is first exploited to provide an efficient plan with significantly reduced disruption. Then, Scaled-YOLOv4 and SOLOv2 models are considered in this study to detect defects even in a large-scale field quickly and acquire pixel-level damage recognition for more precise quantification, respectively. Finally, damage levels of components are rated following the importance and numbers per unit area of the detected defects. This entire framework is also implemented and verified by the hallway of an elementary school to detect and quantify surface damage of concrete components. As seen in the results, the conventional building inspection is significantly improved by the aid of the proposed framework in terms of damage localization, damage quantification, and inspection efficiency. |
| Title | Linear Static Analysis with Graph Neural Networks |
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| Author | Yuan-Tung Chou, Kuang-Yao Li, Po-Chih Kuo, Wei-Tze Chang, Yin-Nan Huang , Chuin-Shan Chen |
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| Abstract | Structural design is an iterative process for optimum selection, which relies on structural analysis results and experience from structural engineers. Since iterative structural analysis is a necessary for getting a good design, accelerating structural analysis is an important task. In this work, we adopt deep learning approaches as a surrogate model for linear static analysis, which provides fast and accurate structural response prediction. Based on the similarity between the structure’s topology and graph data structure, we represent structures as graphs and leverage graph neural networks (GNNs) to learn the relationship between given external forces and corresponding structural responses. The GNN model is trained with random-generated structures, including random story number, span number, beam-column length, and value of external forces. The results show that the GNN model has good performance on displacement and force predictions and excellent generalizability on unseen, taller structures. In addition, it is shown that based on the analysis of feature importance, the GNN model can extract important physical attributes from the input features. |
| Title | DATA ANOMALIES DETECTION AND CLASSIFICATION USING MACHINE LEARNING AND STATISTIC INFORMATION |
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| Author | Tian-Xun Lin, Shieh-Kung Huang, Jau-Yu Chou |
| Keywords | data anomaly, machine learning, pattern recognition network, GoogLeNet |
| Abstract | Structural health monitoring (SHM) and structural integrity management (SIM) are emerging recently. To continuously track the condition and constantly detect early deterioration of the infrastructure, huge amounts of data are produced and abnormal measurement is inevitable. The corrupted data can produce a lot of problems and, generally, they are examined and classified by humans. In this study, the detection and classification are replaced by the techniques of machine learning (ML) and improved by using statistic information. The neural networks based on 1-dimensional and 2-dimensional data are studied via a field dataset collected from a long-span cable-stayed bridge. Therefore, a shallow network, called pattern recognition network, is selected to use 1-dimensional data as an input and a deep network, GoogLeNet, is selected to use 2-dimensional data. The results show that both models can detect and classify the data anomalies and the usage depends on the assigned application and the trade-off between computation and performance. |
| Title | Application of Convolution Neural Network and Neural Network Entropy Algorithm for Structural Health Monitoring |
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| Author | Tzu-Kang Lin, Yi-Ting Lin, Kai-Wei Kuo |
| Keywords | Structural Health Monitoring, NNetEn, Convolution Neural Network |
| Abstract | This study combines Neural Network Entropy (NNetEn) and Convolutional Neural Network (CNN) to develop a practical structural health monitoring system. In order to verify the feasibility of the system, the failure experiment of a seven-story steel frame has been carried out with a numerical model of the same structural characteristics as the steel frame. The state space method is used to simulate the sixteen failure modes on the steel frame, where the acceleration signals of each floor at the time of failure are analyzed by neural network entropy. An entropy database is established based on the model to train the neural network model. To avoid the misjudgment and automatic interpretation of human factors, this study uses the visualized heatmap to quantify the change of entropy value, and the convolutional neural network analysis is selected for image processing. By converting the entropy value into image data, not only the number of parameters in the model can be reduced, but its operation speed can be improved. During the training process, the neural network model extracts and learns the damage features in the entropy value. After the training is completed, the model can allocate the damage area of the structure by identifying the damage features of the input data. Finally, through the verification of 16 failure cases simulated on the seven-story steel frame of the National Center for Research on Earthquake Engineering (NCREE), the performance of the proposed SHM system is evaluated by both numerical simulation and experimental verification with confusion matrix. The SHM system proposed in this study combines the emerging entropy analysis method with a neural network. The test results of the final verification have an accuracy rate of 93.13%. |
| Title | Improved Acceleration Tracking Performance of Seismic Simulators using Supervised Deep Learning |
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| Author | Kui-Xing Lai and Pei-Ching Chen |
| Keywords | Seismic shake table; acceleration control, three-variable control; deep learning, long short-term memory neural network |
| Abstract | Seismic shake table testing has been widely used for various structural systems such as steel structures, reinforced-concrete structures, energy-dissipated and base-isolated buildings, and nonstructural components etc. Therefore, accurate replication of shake table acceleration is particularly important to these tests. In this study, supervised deep learning approach is applied as an alternative for seismic shake table control. The Long Short-Term Memory (LSTM) neural network is built for training the controller to improve acceleration performance of the shake table. A large-scale servo-hydraulic uniaxial shake table is adopted. A steel specimen is designed and fabricated for performing a large number of shake table tests. Then, the shake table testing data are used to train a feedforward controller using LSTM which is implemented close to an existing Three-Variable Control (TVC) loop. The validating experimental results prove that the acceleration tracking performance is improved compared with conventional TVC. The control-structure interaction is also suppressed. The experimental results demonstrate the proposed control scheme reduces the acceleration tracking error effectively compared with conventional TVC control. The research results also show great potential for deep learning application to seismic shake table control in the future. Keywords: Constitutive model, anisotropy, shear-slip and re-contact, mesh-sensitivity, non-proportional loading, concrete, finite element |
| Title | Synthetic Power Distribution Network Construction Based on Deep Learning Algorithm |
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| Author | Yue-Hung Lin and Chi-Ying Lin |
| Keywords | synthetic power distribution network, deep learning, object detection, geo-positioning |
| Abstract | Global warming has caused high energy consumption and an increasing scale of disasters, which make people draw more attention to public asset management to reduce energy consumption and predict losses caused by disasters. Based on a deep learning based object detection approach, this study develops a synthetic power distribution network that can serve as an alternative to the real power distribution net-work and be used to analyze its reliability. This research uses the street view images to detect utility poles and conduct geo-positioning to locate utility poles on the map. For object detection, the Mask R-CNN and YOLOv4 are trained with controlled du-ration, and then the accuracy of the two models is compared to determine which method is suitable for this research. Second, the model’s hyperparameters are adjusted and compared to determine the best model setting for the object detection task in this study. Then the selected model is used to perform the object detection task for each street in the research region. Two sorting methods, namely, the latitude and longitude sorting method and the shortest path sorting method, are proposed to sort the poles for pole geopositioning and supplementation. With two sorting methods, pole geopositioning is conducted based on two approaches: The first is the Markov random field (MRF) approach, and the second is the line of bearing (LOB) with density-based spatial clustering of application with noise (DBSCAN). After determining the detected pole location, pole supplementation is conducted to ensure the maximum allowable distance between poles. Third, four sets of results are obtained by merging all streets and removing duplicate poles by means of distance iteration. Finally, four results are compared with the coordinates of real utility poles. The most suitable method for study region is selected to establish the synthetic power distribution network using the minimum spanning tree (MST). In the future, this model can be improved to make it more in line with the real power distribution network, and the synthetic power distribution network can be used for power grid reliability analysis, public asset management, disaster analysis, power demand-supply analysis, etc. |
