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2 Archives of Civil and Mechanical Engineering

2 2017

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Detection of fatigue cracking in steel bridge girders: A support vector machine approach

Hasni H., Alavi A. H., Jiao P., Lajnef N.

Archives of Civil and Mechanical Engineering

2017

Vol. 17, no. 3

609--622

This study presents an artificial intelligence approach for the detection of distortion-induced fatigue cracking of steel bridge girders based on the data provided by self-powered wireless sensors. The sensors have a series of memory gates that can cumulatively record the duration of the applied strain. The gates are activated as soon as the electrical charge generated by piezoelectric strain transducer exceeds pre-defined thresholds. In the present study, the distribution of the sensor output has been characterized by a Gaussian cumulative density function. For the analysis, extensive finite element simulations were carried out to obtain the structural response of an existing highway steel bridge girder (I-96/M-52) in Webberville, Michigan. Different damage states were defined by extending the lengths of the crack at the web gaps from 10 mm to 100 mm. Damage indicator features were extracted for different data acquisition nodes based on the sensor output distribution. Subsequently, support vector machine (SVM) classifiers were developed to fuse the clustered features and identify multiple damage states. The results indicate that the models have acceptable detection performance, specifically for cracks larger than 10 mm. The best classification performance was obtained using the information from a group of sensors located near the damage zone.

A new approach for modeling of flow number of asphalt mixtures

Alavi A. H., Hasni H., Zaabar I., Lajnef N.

Archives of Civil and Mechanical Engineering

2017

Vol. 17, no. 2

326--335

Flow number of asphalt-aggregate mixtures is an explanatory parameter for the analysis of rutting potential of asphalt mixtures. In this study, a new model is proposed for the determination of flow number using a robust computational intelligence technique, called multi-gene genetic programming (MGGP). MGGP integrates genetic programming and classical regression to formulate the flow number of Marshall Specimens. A reliable experimental database is used to develop the proposed model. Different analyses are performed for the performance evaluation of the model. On the basis of a comparison study, the MGGP model performs superior to the models found in the literature.