The deployment cost of the structural health monitoring (SHM) system is the major argument against the more widespread use of the structural health monitoring techniques. Optimization of sensor pla...

Optimization of sensor placement for structural health monitoring: a review:

Structural health monitoring plays an increasingly significant role in detecting damages for large and complex structures to ensure their serviceability and sustainability. Optimal sensor placement...

Computational methodologies for optimal sensor placement in structural health monitoring: A review:

Sensor placement algorithm for structural health monitoring with redundancy elimination model based on sub-clustering strategy

In this study we investigate the feasibility of detecting structural damage using the Hilbert--Huang Transform (HHT) method. A damage detection index, which we call the ratio of equivalent damping ratio (RED), is proposed. The damage detection index is evaluated using the HHT and the Fast Fourier Transform (FFT) methods for the analysis of shaking table test data obtained for benchmark models based upon the Kobe and El Centro earthquakes. The results indicate that, when the response of the structure is in the elastic region, the RED value changes only slightly in both the HHT and the FFT spectra. In addition, when the structural response is nonlinear (i.e., member yielding occurs), the RED and peak ground acceleration curves indicate the incremental change in the HHT spectra. However, the same phenomena cannot be found in the FFT spectra. Moreover, there is greater change in the value of the RED for the top floor than for the other floors. Furthermore, structural damage is detected only when acceleration response data from the top floor is used. Therefore, the RED estimated from the smoothed HHT spectra is an effective and sensitive index for detecting structural damage. The proposed RED strategy will be used to establish an on-line health monitoring scheme for detecting structural damage in steel structures right after the occurrence of ground motion.

A case study of damage detection in benchmark buildings using a Hilbert-Huang Transform-based method

Computational intelligence is one of the most powerful data processing tools to solve complex nonlinear problems, and thus plays a significant role in intelligent fault diagnosis and prediction However, only few comprehensive reviews have summarized the ongoing efforts of computational intelligence in machinery condition monitoring and fault diagnosis The recent research and development of computational intelligence techniques in fault diagnosis, prediction and optimal sensor placement are reviewed The advantages and limitations of computational intelligence techniques in practical applications are discussed The characteristics of different algorithms are compared, and application situations of these methods are summarized Computational intelligence methods need to be further studied in deep understanding algorithm mechanism, improving algorithm efficiency and enhancing engineering application This review may be considered as a useful guidance for researchers in selecting a suitable method for a specific situation and pointing out potential research directions

/pdf/comprehensive-overview-on-computational-intelligence-1bj9zugv2o.pdf

Comprehensive Overview on Computational Intelligence Techniques for Machinery Condition Monitoring and Fault Diagnosis

Research on optimal sensor placement (OSP) has become very important due to the need to obtain effective testing results with limited testing resources in health monitoring. In this study, a new methodology is proposed to select the best sensor locations for large structures. First, a novel fitness function derived from the nearest neighbour index is proposed to overcome the drawbacks of the effective independence method for OSP for large structures. This method maximizes the contribution of each sensor to modal observability and simultaneously avoids the redundancy of information between the selected degrees of freedom. A hybrid algorithm combining the improved discrete particle swarm optimization (DPSO) with the clonal selection algorithm is then implemented to optimize the proposed fitness function effectively. Finally, the proposed method is applied to an arch dam for performance verification. The results show that the proposed hybrid swarm intelligence algorithm outperforms a genetic algorithm with decimal two-dimension array encoding and DPSO in the capability of global optimization. The new fitness function is advantageous in terms of sensor distribution and ensuring a well-conditioned information matrix and orthogonality of modes, indicating that this method may be used to provide guidance for OSP in various large structures.

https://iopscience.iop.org/article/10.1088/0964-1726/22/9/095015/pdf

Optimal sensor placement for large structures using the nearest neighbour index and a hybrid swarm intelligence algorithm

Early damage detection not only improves the safety and reliability of structures but also reduces maintenance cost. However, damage detection is difficult to implement in large structures under ambient excitation because of the limitation of sensors, the uncertainty of ambient excitation, and the global properties of modal frequencies and displacement modes. This paper proposes a new damage detection method that employs the real encoding multi-swarm particle swarm optimization algorithm and fitness functions evolved from strain modes to find the optimal match between measured and simulated modal parameters and to determine the actual condition of structures. The proposed method requires low-frequency modes and incomplete modes and does not require mass normalization of parameters, thus making the method suitable for nondestructive dynamic damage detection of large structures under ambient excitation. Taking a concrete guide wall structure as an example, this paper studied the global searching performance...

Intelligent damage identification method for large structures based on strain modal parameters

Handle everyday research tasks with reliable, citation-backed results

Your personal Research Agent to handle research tasks with citation-backed results

Popular Tasks used by Researchers

How can I help with your research?

Meet SciSpace

Get more enhanced response by uploading the PDFs you want me to reference.

No relevant PDFs in your library

SciSpace is the AI research assistant for academics. Run systematic literature reviews on 280M+ papers, and write papers with cited sources. Trusted by 1M+ students, PhDs & researchers.

SciSpace | AI for Research

Analyze PDFs

Code & Manuscripts

Funding & Grants

Literature & Patents

Medical & Clinical Data

Systematic Review

Visualize & Present

Web & Data

Build a Google Scholar-like website for your research.

Build a website

Create charts and images for your research

Create a Chart

Write a paper for submission to a journal

Draft a manuscript

Patent Search

Design eye-catching scientific posters in minutes.

Scientific Poster Generation

Systematic Literature Review

One task is running at the moment. Your messages will be shown right after.

Drag and drop or click here to browse

Loved by <highlight>1 million+</highlight> researchers

Extract a list of specific topics and their sources from unstructured text

Topics

Compare and analyze relevant papers that matches with your search

Papers

Get insights from PDFs and bookmarked papers from your library

My library

Recent searches

Try searching for:

Catch AI-generated content in scholarly and non-scholarly content

{ai} Detector

Ai Writer

Get PDF Summaries, highlighted text explanations 

Chat with PDF

Effortlessly create in-text citations and bibliographies in APA and 2,500 other formats

Citation generator

Get explanations, summaries, and answers on academic papers

Ease up your research workflow with {scispace}'s cohort of exciting AI tools

Elevate your academic writing skills and convey your ideas the way you want

Paraphraser

Explore our range of reading and writing tools

Your file is being prepared and should be ready in a few minutes. If it's a large file, it might take a bit longer. You can close this window, and we'll email you the file when it's done.

You have reached a maximum limit of <strong>{limit}</strong> columns in the table. Remove at least <strong>1</strong> column to add or create another one.

Longjun He

Author Tools

Chat about Author

Papers

Optimal sensor placement for large structures using the nearest neighbour index and a hybrid swarm intelligence algorithm

Intelligent damage identification method for large structures based on strain modal parameters