Next Article in Journal
Automated Diabetic Retinopathy Screening System Using Hybrid Simulated Annealing and Ensemble Bagging Classifier
Next Article in Special Issue
Early-Age Cracking in Concrete: Causes, Consequences, Remedial Measures, and Recommendations
Previous Article in Journal
Numerical Modeling and Performance Prediction of COS Hydrolysis Reactor in an Integrated Gasification Fuel Cell in terms of Thermo-Chemical Transport Phenomena
Previous Article in Special Issue
Micromechanical Multiscale Modeling of ITZ-Driven Failure of Recycled Concrete: Effects of Composition and Maturity on the Material Strength
Open AccessArticle

Quantitative Deterioration Assessment of Road Bridge Decks Based on Site Inspected Cracks

Department of Civil Engineering, The University of Tokyo, 7-3-1 Hongo, Bunkyo-ku, Tokyo 113-8656, Japan
Department of Civil and Environmental Engineering, Kanazawa Institute of Technology, 7-1 Nonoichi 921-8501, Japan
Institute of Advanced Sciences, Yokohama National University, 79-1 Tokiwadai, Hodogaya, Yokohama 240-8501, Japan
Author to whom correspondence should be addressed.
Appl. Sci. 2018, 8(7), 1197;
Received: 20 June 2018 / Revised: 4 July 2018 / Accepted: 18 July 2018 / Published: 21 July 2018
By integrating a multi-scale simulation with the pseudo-cracking method, the remaining fatigue life of in-service reinforced concrete (RC) bridge decks can be estimated based upon their site-inspected crack patterns. But, it still takes time for computation. In order to achieve a quick deterioration-magnitude assessment of RC decks based upon their crack patterns, two evaluation methods are proposed. A predictive correlation between the remaining fatigue life and the cracks density (both cracks length and width) is presented as a fast judgment. For fair-detailed judgment, an artificial neural network (ANN) model is also introduced which is the basis of the machine learning. Both assessment methods are built commonly by thousands of artificial random crack patterns to cover all possible ranges since the variety of the real crack patterns on site is more or less limited. The built ANN performances are examined by k-fold cross-validation besides checking the prediction accuracy of real crack patterns of bridge RC decks. Finally, the hazard map of the deck’s bottom surface is introduced to indicate the location of higher risk cracking, which derives from the estimated weight of individual neuron in the built artificial neural network. View Full-Text
Keywords: artificial intelligence; data assimilation; fatigue; bridge decks; pseudo-cracking artificial intelligence; data assimilation; fatigue; bridge decks; pseudo-cracking
Show Figures

Figure 1

MDPI and ACS Style

Fathalla, E.; Tanaka, Y.; Maekawa, K.; Sakurai, A. Quantitative Deterioration Assessment of Road Bridge Decks Based on Site Inspected Cracks. Appl. Sci. 2018, 8, 1197.

Show more citation formats Show less citations formats
Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Article Access Map by Country/Region

Back to TopTop