Quantitative Deterioration Assessment of Road Bridge Decks Based on Site Inspected Cracks
Abstract
:1. Introduction
2. Artificial Neural Network
3. Building Neural Networks
3.1. Scope of Target
3.2. Material Properties for Reference
3.3. Loading Patterns
3.4. Limit State Failure Criteria
3.5. Standardized States for Numerical Model
3.6. Crack Patterns Taken from Real Decks
4. Massive Life Simulation for ANN’s Learning
4.1. Referential RC Deck “No Damage”
4.2. Sensitivity Analysis for Crack Depth
4.3. Cracked Cases
4.4. Statistical Correlation of Remaining Fatigue Life and Inspected Cracks
= − 0.266 × ln (CD) − 0.095 ≥ 0 → CD ≥ 0.15%
5. Training Artificial Neural Networks
5.1. Methodology for Fatigue Life Identification
5.2. Requirements of Training Dataset
5.3. Neural Network Platform and Structure
5.4. Built ANN’s Performance and Input Variables
5.5. Significance of Cracks Direction
5.6. ANN Performance Evaluation
5.7. Structural Mechanistic Expressions of ANN’s Weights
6. Conclusions
- Two fast-truck quantitative assessment models for the magnitude of damages of in-situ RC bridge road decks in service were built based upon the training dataset created by numerical simulation as well as the real site inspection data. A quick and massive diagnosis, which is equivalent to the full 3D multi-scale simulation, is made possible.
- The statistical model is built on the basis of the mechanics-based parameter. Here, the conservative and safer-side assessment of the remaining fatigue life is practically made possible by avoiding the case where pre-cracking stops the preceding shear cracking.
- By examining the wide variety of crack orientation and their patterns over the bottom surfaces of RC decks, it is quantitatively proved that the geometrical patterns of cracking have much to do with the remaining fatigue life as well as crack width.
- By conduction k-fold cross-validation and testing the ANN model, the robustness and the generalization of the proposed ANN model are confirmed with the crack patterns observed at bridge construction site. Here, the numerically produced training dataset, which was offered by the multi-scale analysis, enables us to compensate the week spots of the training dataset.
- A hazard mapping to identify the high-risk location of cracking is created in use of the neuron’s weight and its sensitivity to the fatigue life. It is found that both RC deck’s central zone and their corners are the spots of caution. This map can be used as the guideline to train inspectors.
- It is proved that artificial intelligence is not just a tool for conducting predictive models but it can guide somehow to achieve physical expressions for a particular problem.
Author Contributions
Funding
Acknowledgments
Conflicts of Interest
References
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Material Type | Concrete | Steel Reinforcement | |
---|---|---|---|
Young’s Modulus | N/mm2 | 24,750 | 205,000 |
Compressive Strength | N/mm2 | 30 | 295 |
Tensile Strength | N/mm2 | 2.2 | 295 |
Specific Weight | kN/m3 | 24 | 78 |
ANN Input Variables | Variables for Each FEM Element | No. of Elements | Total Number of Variables | Cracks Direction |
---|---|---|---|---|
Case (1) | εxx, εyy, εxy | 336 | 1008 | Included “Indirectly” |
Case (2) | ε1, θ (Equations (6) and (7)) | 336 | 672 | Included “Directly” |
Case (3) | εxx, εyy, εxy | 84 | 252 | Included “Indirectly” |
Case (4) | ε1, θ (Equations (6) and (7)) | 84 | 168 | Included “Directly” |
ANN Input Variables | Number of Hidden Layers | Number of Neurons |
---|---|---|
Case 1 | 1 | 1 |
Case 2 | 1 | 1 |
Case 3 | 1 | 2 |
Case 4 | 1 | 2 |
Case 5 Section 5.5 | 1 | 2 |
ANN Input Variables | Variables for Each FEM Element | No. of Elements | Total Number of Variables | Cracks Direction |
---|---|---|---|---|
Case (3) | εxx, εyy, εxy | 84 | 252 | Included “Indirectly” |
Case (5) | ε1 | 84 | 84 | Not included |
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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. https://doi.org/10.3390/app8071197
Fathalla E, Tanaka Y, Maekawa K, Sakurai A. Quantitative Deterioration Assessment of Road Bridge Decks Based on Site Inspected Cracks. Applied Sciences. 2018; 8(7):1197. https://doi.org/10.3390/app8071197
Chicago/Turabian StyleFathalla, Eissa, Yasushi Tanaka, Koichi Maekawa, and Akito Sakurai. 2018. "Quantitative Deterioration Assessment of Road Bridge Decks Based on Site Inspected Cracks" Applied Sciences 8, no. 7: 1197. https://doi.org/10.3390/app8071197