State-of-the-Art Review on Advancements of Data Mining in Structural Health Monitoring

An IoT based authentication system for therapeutic herbs measured by local descriptors using machine learning approach

HECON: Weight assessment of the product loyalty criteria considering the customer decision's halo effect using the convolutional neural networks

A review of ultrasonic sensing and machine learning methods to monitor industrial processes.

In the age of the smart city, things like the Internet of Things (IoT) and big data analytics are making big changes to the way traditional structural health monitoring (SHM) is done. Also, the capacity, flexibility, and robustness of artificial intelligence (AI) techniques for solving complex real-world problems have led to an increasing interest in applying these methods to SHM systems of infrastructures in recent years. Therefore, an analytical evaluation of recent advancements in SHM for infrastructures appears to be important. The bridge is one of the significant transportation infrastructures where existing environmental and destructive variables can have a negative impact on the structure&#x2019;s life and health. The SHM system for bridges in different stages of their life cycle, such as construction, development, management, and maintenance, is seen as a complementary part of intelligent transportation systems (ITS). The main goal of this study is to look at how AI can be used to improve the current state of the art in data-driven SHM systems for bridges, including conceptual frameworks, advantages, and challenges, as well as existing approaches. This article presents an overview of the role of AI in data-driven SHM systems for bridges in the future. Finally, some potential research possibilities in AI-assisted SHM are also emphasized and detailed. 

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Artificial Intelligence and Structural Health Monitoring of Bridges: A Review of the State-of-the-Art

Visual inspections have been typically used in condition assessment of infrastructure. However, they are based on human judgment and their interpretation of data can differ from acquired results. In psychology, this difference is called cognitive bias which directly affects Structural Health Monitoring (SHM)-based decision making. Besides, the confusion between condition state and safety of a bridge is another example of cognitive bias in bridge monitoring. Therefore, integrated computer-based approaches as powerful tools can be significantly applied in SHM systems. This paper explores the relationship between the use of advanced computational intelligence and the development of SHM solutions through conducting an infrastructure monitoring methodology. Artificial Intelligence (AI)-based algorithms, i.e., Artificial Neural Network (ANN), hybrid ANN-based Imperial Competitive Algorithm, and hybrid ANN-based Genetic Algorithm, are developed for damage assessment using a lab-scale composite bridge deck structure. Based on the comparison of the results, the employed evolutionary algorithms could improve the prediction error of the pre-developed network by enhancing the learning procedure of the ANN.

/pdf/from-cognitive-bias-toward-advanced-computational-2gygv3ny.pdf

From Cognitive Bias Toward Advanced Computational Intelligence for Smart Infrastructure Monitoring

Cities are becoming smarter in their functions; however, they are not necessarily resilient and able to survive the impact of disasters. An integrated, smart and resilient city is a quest for today and future sustainability that has been scarcely studied. Therefore, this current paper develops a comprehensive framework for re-evaluating cities’ smartness and assessing their resilience against unforeseen disruptive events. A top-down approach is adopted with an expert’s validation process and evaluated in a hypothetical city’s water supply system to demonstrate its workability. The study contributes towards a holistic framework that includes smartness re-evaluation and resilience assessment components that can be used to ensure the resiliency of smart cities from both developed and developing countries resisting disasters.

An integrated framework for assessment of smart city resilience

A structural health monitoring system contains two components, i.e. a data collection approach comprising a network of sensors for recording the structural responses as well as an extraction methodology in order to achieve beneficial information on the structural health condition. In this regard, data mining which is one of the emerging computer-based technologies, can be employed for extraction of valuable information from obtained sensor databases. On the other hand, data inverse analysis scheme as a problem-based procedure has been developing rapidly. Therefore, the aforesaid scheme and data mining should be combined in order to satisfy increasing demand of data analysis, especially in complex systems such as bridges. Consequently, this study develops a damage detection methodology based on these strategies. To this end, an inverse analysis approach using data mining is applied for a composite bridge. To aid the aim, the support vector machine (SVM) algorithm is utilized to generate the patterns by means of vibration characteristics dataset. To compare the robustness and accuracy of the predicted outputs, four kernel functions, including linear, polynomial, sigmoid, and radial basis function (RBF) are applied to build the patterns. The results point out the feasibility of the proposed method for detecting damage in composite slab-on-girder bridges.

http://jad.shahroodut.ac.ir/article_2119_45edecdc14b19dd952c9997afa2f5f9f.pdf

Kh. Ghaedi

Papers

State-of-the-Art Review on Advancements of Data Mining in Structural Health Monitoring

From Cognitive Bias Toward Advanced Computational Intelligence for Smart Infrastructure Monitoring

An integrated framework for assessment of smart city resilience

Data Mining-based Structural Damage Identification of Composite Bridge using Support Vector Machine

Experimental and numerical studies on the cyclic performance of structural frames equipped with bar dampers