A review of vibration-based damage detection in civil structures : from traditional methods to Machine Learning and Deep Learning applications

Emerging artificial intelligence methods in structural engineering

Review on the new development of vibration-based damage identification for civil engineering structures: 2010-2019

In the past few years, intelligent structural damage identification algorithms based on machine learning techniques have been developed and obtained considerable attentions worldwide, due to the ad...

https://journals.sagepub.com/doi/pdf/10.1177/1475921718804132

A novel deep learning-based method for damage identification of smart building structures:

Applications of machine learning methods for engineering risk assessment – A review

Rapid detecting damages/defeats in the large-scale civil engineering structures, assessing their conditions and timely decision making are crucial to ensure their health and ultimately enhance the level of public safety. Advanced sensor network techniques recently allow collecting large amounts of data for structural health monitoring and damage detection, while how to effectively interpret these complex sensor data to technical information posts many challenges. This paper presents three optimization-algorithm based support vector machines for damage detection. The optimization algorithms, including grid-search, partial swarm optimization and genetic algorithm, are used to optimize the penalty parameters and Gaussian kernel function parameters. Two types of feature extraction methods in terms of time-series data are selected to capture effective damage characteristics. A benchmark experimental data with the 17 different scenarios in the literature were used for verifying the proposed data-driven methods. Numerical results revealed that all three optimized machine learning methods exhibited significantly improvement in sensitivity, accuracy and effectiveness over conventional methods. The genetic algorithm based SVM had a better prediction than other methods. Two different feature methods used in this study also demonstrated the appropriate features are crucial to improve the sensitivity in detecting damage and assessing structural health conditions. The findings of this study are expected to help engineers to process big data and effectively detect the damage/defects, and thus enable them to make timely decision for supporting civil infrastructure management practices.

Data-driven support vector machine with optimization techniques for structural health monitoring and damage detection

Combined thermal power and battery low carbon scheduling method based on variational mode decomposition

To solve the problem of the large gap in the regulation resources of the power system due to the large number of wind power connected to the grid and to help achieve the strategic energy goal of “carbon neutrality and carbon peaking,” the demand-side adjustable energy-intensive load is taken as the research object in this paper, and a two-stage low-carbon economic scheduling method of the wind power system with multi-form energy-intensive load (MEL) is proposed. First, the operating characteristics of the system with MEL are analyzed. At the same time, a refined mathematical model that conforms to the actual operation characteristics is established. Then, the complementary low-carbon characteristics of the source-charge side and the difference in the response characteristics of the demand-side MEL are considered. Meanwhile, combining with the energy storage system, a day-ahead and intra-day two-stage low-carbon economic scheduling model of the wind power system with MEL is constructed. Finally, an example of the improved IEEE-39 bus system is analyzed to verify the effectiveness of the proposed scheduling method in the low-carbon economic scheduling of wind power systems and to provide a reference for power grid scheduling.

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Zhijun Yuan

Papers

Data-driven support vector machine with optimization techniques for structural health monitoring and damage detection

Combined thermal power and battery low carbon scheduling method based on variational mode decomposition

Day-ahead and intra-day two-stage low-carbon economic scheduling method considering multi-form energy-intensive load

Eccentric compressive behavior of square concrete-filled stainless steel tube (CFSST) stub columns