Structural health monitoring using AI and ML based multimodal sensors data

Shibu, Melvin; Kumar, Kukatlapalli Pradeep; Pillai, Vinay Jha; Murthy, Hari; Sarathchandra, K.

doi:10.1016/j.measen.2023.100762

Cited by 11 publications

(3 citation statements)

References 6 publications

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“…AI and ML are dynamic and inter-disciplinary fields at the intersection of computer science, cognitive science, and mathematics [ 12 ]. AI is the study and development of intelligent systems with human-like intelligence, reasoning, problem-solving, and decision-making capacities [ 13 ]. ML, a subfield of AI, concentrates on the formation of algorithms and models that facilitate the automatic acquisition of knowledge from data, thereby improving the performance of machines through experience [ 14 ].…”

Section: Origin Concept and Technological Development Of Ai And MLmentioning

confidence: 99%

Artificial intelligence and machine learning applications in the project lifecycle of the construction industry: A comprehensive review

Datta,

Islam,

Rahman Sobuz

et al. 2024

Heliyon

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Section: Origin Concept and Technological Development Of Ai And MLmentioning

confidence: 99%

Artificial intelligence and machine learning applications in the project lifecycle of the construction industry: A comprehensive review

Datta,

Islam,

Rahman Sobuz

et al. 2024

Heliyon

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“…With the development of AI and ML focused hardware, SHM with nondestructive test (NDT) methods became a common practice [71,75]. Mondal and Chen had done a systematic review on optical camera-based, vibration-based and other NDT-based methods in SHM and predicted that NDT based unmanned aerial systems for SHM will have a compound annual growth rate of 57.5% from 2021 to 2028 [76].…”

Section: Artificial Intelligence and Machine Learning For Structural ...mentioning

confidence: 99%

Influence of Smart Sensors on Structural Health Monitoring Systems and Future Asset Management Practices

Preethichandra,

Suntharavadivel,

Kalutara

et al. 2023

Sensors

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Recent developments in networked and smart sensors have significantly changed the way Structural Health Monitoring (SHM) and asset management are being carried out. Since the sensor networks continuously provide real-time data from the structure being monitored, they constitute a more realistic image of the actual status of the structure where the maintenance or repair work can be scheduled based on real requirements. This review is aimed at providing a wealth of knowledge from the working principles of sensors commonly used in SHM, to artificial-intelligence-based digital twin systems used in SHM and proposes a new asset management framework. The way this paper is structured suits researchers and practicing experts both in the fields of sensors as well as in asset management equally.

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“…The idea of structural health monitoring (SHM) first emerged in the early 2000s. Although initially, the sole focus of SHM was to monitor concrete bridges, in present times, it is defined as the method of continuously evaluating and assessing the condition and performance of any concrete structures, such as buildings, bridges, dams, pipelines, and other infrastructure, throughout their operational lifespan [4][5][6]. The objective of SHM is to detect any damage, deterioration, or changes in structural properties that could potentially compromise the safety, functionality, or longevity of the concrete structure and is crucial in maintaining structures in optimal condition [2].…”

Section: Introductionmentioning

confidence: 99%

A Comprehensive Analysis of the Integration of Deep Learning Models in Concrete Research from a Structural Health Perspective

Chowdhury,

Kaiser

2024

Construction Materials

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Concrete stands as the most widely used construction material globally due to its versatility, encompassing applications ranging from pavement, multifloor structures, and bridges to dams. However, these concrete structures endure structural stress and require close monitoring to prevent accidents and ensure sustainability throughout their complete life cycle. In recent years, artificial intelligence (AI) and computer vision (CV) have demonstrated considerable potential in diverse applications within construction engineering, including structural health monitoring (SHM) and inspection processes such as crack and damage detection, as well as rebar exposure. While it is undeniable that CV and deep learning models are transforming the construction industry by offering robust solutions for complex scenarios, there remain numerous challenges pertinent to their applications that require attention. This paper aims to systematically and critically review the literature of the past decade on the application of deep learning models in the construction industry for SHM purposes in concrete structures. The review delves into proposed methodologies and technologies while identifying opportunities and challenges associated with these applications in practice. Additionally, the paper provides insights to bridge the gap between theory and application.

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Structural health monitoring using AI and ML based multimodal sensors data

Cited by 11 publications

References 6 publications

Artificial intelligence and machine learning applications in the project lifecycle of the construction industry: A comprehensive review

Artificial intelligence and machine learning applications in the project lifecycle of the construction industry: A comprehensive review

Influence of Smart Sensors on Structural Health Monitoring Systems and Future Asset Management Practices

A Comprehensive Analysis of the Integration of Deep Learning Models in Concrete Research from a Structural Health Perspective

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