Research on Recurrent Neural Network Based Crack Opening Prediction of Concrete Dam

The concrete dam can prevent flooding events and generate a vast amount of electricity, and it is a critical national infrastructure. However, it is easy to get cracked, and cracks usually pose significant potential threats to the safety of the concrete dam. Many researchers have done much research on dam crack protection and explored various rules to protect the concrete dam from cracks. However, the complex and irregular distribution of cracks make this task a very challenging research issue. In this paper, the feature importance of crack influencing factors is firstly analyzed. Then, the Recurrent Neural Network (RNN) is introduced for dam crack modeling. Next, the crack width of the Longyangxia Dam is modeled and tested by using the Gated Recurrent Unit (GRU) and Long Short-Term Memory (LSTM). Finally, experimental results show that our proposed RNN-based method can effectively predict the crack change of the concrete dam.

[1]  Trevor Darrell,et al.  Long-term recurrent convolutional networks for visual recognition and description , 2014, 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[2]  Hongyuan Liu,et al.  Experimental Study on Cracking, Reinforcement, and Overall Stability of the Xiaowan Super-High Arch Dam , 2015, Rock Mechanics and Rock Engineering.

[3]  François Hild,et al.  Comparison between experimental and numerical results of mixed-mode crack propagation in concrete: Influence of boundary conditions choice , 2017 .

[4]  Qian Zhang,et al.  Saliency Detection via the Improved Hierarchical Principal Component Analysis Method , 2020, Wirel. Commun. Mob. Comput..

[5]  Pei Hu,et al.  Novel Parallel Heterogeneous Meta-Heuristic and Its Communication Strategies for the Prediction of Wind Power , 2019, Processes.

[6]  Bin Zheng,et al.  A robust distance-based relay selection for message dissemination in vehicular network , 2018, Wireless Networks.

[7]  Xingsi Xue,et al.  Optimizing Ontology Alignment in Vector Space , 2020 .

[8]  Amit H. Varma,et al.  A path independent energy integral approach for analytical fracture strength of steel-concrete structures with an account of interface effects , 2018, Engineering Fracture Mechanics.

[9]  Sherong Zhang,et al.  Seismic cracking analysis of concrete gravity dams with initial cracks using the extended finite element method , 2013 .

[10]  Arun Kumar Sangaiah,et al.  Edge-Computing-Based Trustworthy Data Collection Model in the Internet of Things , 2020, IEEE Internet of Things Journal.

[11]  Xiangming Zhou,et al.  Experimental and numerical investigations on fracture process zone of rock–concrete interface , 2017 .

[12]  Jeng-Shyang Pan,et al.  Novel Systolization of Subquadratic Space Complexity Multipliers Based on Toeplitz Matrix–Vector Product Approach , 2019, IEEE Transactions on Very Large Scale Integration (VLSI) Systems.

[13]  Zhenjun Yang,et al.  A heterogeneous cohesive model for quasi-brittle materials considering spatially varying random fracture properties , 2008 .

[14]  Jürgen Schmidhuber,et al.  LSTM: A Search Space Odyssey , 2015, IEEE Transactions on Neural Networks and Learning Systems.

[15]  Wenbing Wu,et al.  A Probability Preferred Priori Offloading Mechanism in Mobile Edge Computing , 2020, IEEE Access.

[16]  Xiaolong Li,et al.  Privacy-Enhanced Data Collection Based on Deep Learning for Internet of Vehicles , 2020, IEEE Transactions on Industrial Informatics.

[17]  Arun Kumar Sangaiah,et al.  An empower hamilton loop based data collection algorithm with mobile agent for WSNs , 2019, Human-centric Computing and Information Sciences.

[18]  Arun Kumar Sangaiah,et al.  An Affinity Propagation-Based Self-Adaptive Clustering Method for Wireless Sensor Networks , 2019, Sensors.

[19]  Chai Jun.  Analysis of Seepage through Dam Foundation with Closed System of Grouting Curtain, Drainage and Pumping Measures , 2003 .

[20]  J. M. Chandra Kishen,et al.  Stress intensity factors based fracture criteria for kinking and branching of interface crack: application to dams , 2001 .

[21]  Jin Wang,et al.  An intelligent data gathering schema with data fusion supported for mobile sink in wireless sensor networks , 2019, Int. J. Distributed Sens. Networks.

[22]  Arun Kumar Sangaiah,et al.  An Energy-Efficient Off-Loading Scheme for Low Latency in Collaborative Edge Computing , 2019, IEEE Access.

[23]  Jiwon Oh,et al.  Synergistic approach to quantifying information on a crack-based network in loess/water material composites using deep learning and network science , 2019, Computational Materials Science.

[24]  Yuantao Chen,et al.  The fire recognition algorithm using dynamic feature fusion and IV-SVM classifier , 2019, Cluster Computing.

[25]  J. Friedman.  Greedy function approximation: A gradient boosting machine. , 2001 .

[26]  Chongmin Song,et al.  Hydraulic fracture at the dam-foundation interface using the scaled boundary finite element method coupled with the cohesive crack model , 2018 .

[27]  Andrew Y. Ng,et al.  Semantic Compositionality through Recursive Matrix-Vector Spaces , 2012, EMNLP.

[28]  Jürgen Schmidhuber,et al.  Long Short-Term Memory , 1997, Neural Computation.

[29]  Daniel Dias,et al.  Artificial neural networks for the interpretation of piezometric levels at the rock-concrete interface of arch dams , 2019, Engineering Structures.

[30]  Wei Dong,et al.  Fracture mechanisms of rock-concrete interface: Experimental and numerical , 2016 .

[31]  Jun Zhao,et al.  Relation Classification via Convolutional Deep Neural Network , 2014, COLING.

[32]  Yong Yu,et al.  A Review of Recurrent Neural Networks: LSTM Cells and Network Architectures , 2019, Neural Computation.

[33]  Yoshua Bengio,et al.  Learning Phrase Representations using RNN Encoder–Decoder for Statistical Machine Translation , 2014, EMNLP.

[34]  Chang Zhou,et al.  Optimal Coverage Multi-Path Scheduling Scheme with Multiple Mobile Sinks for WSNs , 2020 .

[35]  Jin Wang,et al.  ARNS: Adaptive Relay-Node Selection Method for Message Broadcasting in the Internet of Vehicles , 2020, Sensors.

[36]  Wei Dong,et al.  Numerical Method for Mixed-Mode I-II Crack Propagation in Concrete , 2013 .

[37]  Turhan Bilir,et al.  A novel study for the estimation of crack propagation in concrete using machine learning algorithms , 2019, Construction and Building Materials.

[38]  J. Červenka,et al.  Mixed mode fracture of cementitious bimaterial interfaces: ; Part II: numerical simulation , 1998 .

[39]  Wojciech Zaremba,et al.  An Empirical Exploration of Recurrent Network Architectures , 2015, ICML.

[40]  Dong Yang,et al.  SIF-based fracture criterion of rock-concrete interface and its application to the prediction of cracking paths in gravity dam , 2019, Engineering Fracture Mechanics.

[41]  Anfeng Liu,et al.  An Intelligent Edge-Computing-Based Method to Counter Coupling Problems in Cyber-Physical Systems , 2020, IEEE Network.

[42]  Luis E. Vallejo,et al.  Numerical analysis of the causes of face slab cracks in Gongboxia rockfill dam , 2014 .

[43]  Kuldip K. Paliwal,et al.  Bidirectional recurrent neural networks , 1997, IEEE Trans. Signal Process..

[44]  Arun Kumar Sangaiah,et al.  Blockchain-Enabled Distributed Security Framework for Next-Generation IoT: An Edge Cloud and Software-Defined Network-Integrated Approach , 2020, IEEE Internet of Things Journal.