Damage detection of truss bridge joints using Artificial Neural Networks

Mehrjoo, Mohsen; Khaji, N.; Moharrami, Hamid; Bahreininejad, Ardeshir

doi:10.1016/j.eswa.2007.08.008

Cited by 253 publications

(139 citation statements)

References 19 publications

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“…Robust regression (RR) [14,21] 3. Artificial neural networks (ANN) [22,23] 4. Support vector regression (SVR) [24,25] The above set of algorithms is chosen for the variety they present in terms of computational complexity and due to their previous applications to interpreting measurements from SHM.…”

Section: Regression Algorithmsmentioning

confidence: 99%

Predicting thermal response of bridges using regression models derived from measurement histories

Kromanis

Kripakaran

2014

Computers & Structures

102

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This study investigates the application of novel computational techniques for structural performance monitoring of bridges that enable quantification of temperature-induced response during the measurement interpretation process. The goal is to support evaluation of bridge response to diurnal and seasonal changes in environmental conditions, which have widely been cited to produce significantly large deformations that exceed even the effects of live loads and damage. This paper proposes a regression-based methodology to generate numerical models, which capture the relationships between temperature distributions and structural response, from distributed measurements collected during a reference period. It compares the performance of various regression algorithms such as multiple linear regression (MLR), robust regression (RR) and support vector regression (SVR) for application within the proposed methodology. The methodology is successfully validated on measurements collected from two structures -a laboratory truss and a concrete footbridge. Results show that the methodology is capable of accurately predicting thermal response and can therefore help with interpreting measurements from continuous bridge monitoring.

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Section: Regression Algorithmsmentioning

confidence: 99%

Predicting thermal response of bridges using regression models derived from measurement histories

Kromanis

Kripakaran

2014

Computers & Structures

102

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“…Mehrjoo et al [54] presented a fault detection inverse algorithm to estimate the damage intensities of joints in truss bridge structure using back propagation neural network method. Just-Agosto et al [55] applied neural network method with a combination of vibration and thermal damage detection signatures to develop a damage defection tool.…”

Section: Neural Network Technique Used For Fault Detectionmentioning

confidence: 99%

Review of Techniques for Fault Diagnosis in Damaged Structure and Engineering System

Thatoi

Das

Parhi

2012

Advances in Mechanical Engineering

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Focus has been made to give an overview of various methodologies used in fault diagnosis and condition monitoring. A crack in vibrating structures can lead to premature failure if it is not detected in time. Researchers have been working on the dynamics of cracked structures for decades to be able to monitor a structure and diagnose fault at the earliest possible stage. An effort has been made in the current paper to understand different techniques and methodologies for fault diagnosis and condition monitoring of damaged structures subjected to varied dynamic loading. The methods used are classical, wavelet transform, and finite element methods, artificial intelligence methods, and numerical and experimental methods. Using classical methods, engineers are able to predict faults. But using artificial intelligence techniques, it is observed that the forecasting time for fault diagnosis improves a lot in comparison to other methodologies.

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“…Neural networks (NN) are the most familiar soft computing approach for inference tasks, from which many neural network derivatives have been developed and applied in various categories [4,8,10]. However, NN has been primarily argued as a "black box" model due to the massive number of nodes and connections within its structure.…”

Section: Introductionmentioning

confidence: 99%

Modeling Deep Beam Strengths with a Genetic Programming System

Tsai¹,

Lin²

2011

28th International Symposium on Automation and Robotics in Construction (ISARC 2011)

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This paper proposes a genetic programming system (GPS) to both predict and program deep beam strength.The soft prediction is to provide estimated results for problems and the soft programming is to represent problems with meaningful formulas. In proposed GPS, two models of genetic programming (GP) and weighted genetic programming (WGP) are involved. GP is structured of a tree topology, inputs, and operators. WGP improves GP on introducing weights to balance tree blanches. Both GP and WGP results are provided in the GPS. Besides, this paper further designed a WGP to provide polynomial-like formulas, which seemed pretty helpful to parameter studies. Deep beam strengths were used as the case study. Results reveal that GPS has two distinct advantages over other black-box approaches in programming formulas and parameter studies. Furthermore, GPS does not surrender in prediction accuracy at all. Summarily, uses of proposed GPS are to predict, program, and model engineering problems.

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Damage detection of truss bridge joints using Artificial Neural Networks

Cited by 253 publications

References 19 publications

Predicting thermal response of bridges using regression models derived from measurement histories

Predicting thermal response of bridges using regression models derived from measurement histories

Review of Techniques for Fault Diagnosis in Damaged Structure and Engineering System

Modeling Deep Beam Strengths with a Genetic Programming System

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