A Novel Optimal Sensor Placement Framework for Concrete Arch Dams Based on IAHA Considering the Effects of Cracks and Elastic Modulus Degradation
Source abstract
Abstract
Optimal sensor placement (OSP) for arch dams is important to ensure their long-term service, but the evolution of structural states and material properties is less considered at present. This paper considers the effects of cracks, contraction joints, and elastic modulus zoning degradation of dam bodies, proposing an OSP framework based on an improved artificial hummingbird algorithm (IAHA). First, considering the compressibility of reservoir water, a finite element model of the arch dam–reservoir–foundation system is established. Second, by introducing improved circle chaotic mapping and Levy flight, IAHA is proposed. Then, a method for selecting the optimal number of sensors (ONS) based on modal assurance criterion (MAC), fitness values, and maximum singular value ratio (S) criteria is proposed. Finally, an OSP framework for arch dams with cracks is constructed and verified through a concrete arch dam. The final sensor placement is carried out for the current state of this arch dam after 45 years of operation, and the ONS is selected to give the results of the spatial location of the sensors. The results indicate that the OSP performance of the arch dam based on IAHA is the best, with MAC-MAX, MAC-AVE, MAC-RMS, and S values of 0.1521, 0.1069, 0.5478, and 1.8591, respectively, showing the best performance among the selected algorithms. The method of selecting the ONS based on MAC, fitness values, and S criteria is reasonable and feasible, considering that the changes in structural states and material properties have varying degrees of influence on the number and spatial location of sensors. The research results of this paper can provide effective technical support for the health diagnosis of arch dams with cracks and provide references and new ideas for structural health monitoring.
Source: crossref · Quality: baseline
Research context
OpenAlex topics: Dam Engineering and Safety · Structural Health Monitoring Techniques · Infrastructure Maintenance and Monitoring
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
Affiliations
College of Hydraulic Science and Engineering, Yangzhou University, Yangzhou 225009, China
Suzhou Research Institute of Hohai University, Suzhou 215100, China
School of Urban Construction, Changzhou University, Changzhou 213164, China
Yellow River Laboratory, Zhengzhou University, Zhengzhou 450001, China
College of Civil Engineering, Nanjing Forestry University, Nanjing 210037, China
Funding
National Natural Science Foundation of China · Henan Provincial Science and Technology Research Project
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