# Quantitative Deterioration Assessment of Road Bridge Decks Based on Site Inspected Cracks
## 1 Introduction  
In the renewal projects of highways, it is estimated that more than half of total maintenance cost will be spent for the renewal and repair of bridge decks [1] and cost reduction in the maintenance of bridge decks is a crucial matter. Remaining fatigue life is essential information to conduct a reliable maintenance plan. Crack grading by visual inspection is used to categorize the degree of deterioration [2]. However, this grading method is not able to estimate the remaining life quantitatively. Here, one of the technologies to assess the fatigue life is a multi-scale simulation [3, 4]. By introducing the time-dependent constitutive models into the multi-scale simulation program, fatigue life of bridge decks can be predicted numerically [5]. Furthermore, the integration of inspection data with the life-simulation (pseudo-cracking method) can upgrade the reliability of life estimation of existing bridge deck [6, 7] as shown in Figure 1. As the multiscale simulation deals with the geometric crack patterns, it requires huge calculation cost. Thus, the authors try to utilize the artificial intelligence in order to achieve the remaining fatigue life in just a moment with running complex multiscale simulation. In the data assimilation technology [8, 9, 10], crack patterns observed on lower faces is induced into the life-simulation analysis as an initial state of damages for the future simulation. Lucked defect information is complemented by the predictor-corrector iterative search of energy based stability. Based on the data assimilation technology, the authors present two evaluation methods in this study for the deterioration-magnitude assessment of reinforced concrete (RC) decks which are derived from their inspected bottom surface cracks. One is the statistical model to correlate the remaining fatigue life versus the crack density. Thus, the parameter used has clear physical and mechanical meanings to be easily understood. The other one is the artificial neural network model of a machine-learning basis. Contrary to its great versatility, the physical and mechanical structures of the network are difficult to be understood, theoretically and mechanically. But, it is expected that the built neuron network may have an implicit similarity to the risk of cracking on the remaining fatigue life. Then, it is thought by the authors to be meaningful to compare both methods of different approaches. As the first step, the remaining fatigue life of in-service existing decks is examined in reference to real crack patterns of a wide variety by utilizing the life assessment technology developed previously. Thus, we check the range of the fatigue life with regard to the real crack patterns and it will be used at the final step to secure the generalization of the proposed ANN model. Here, it should be noted again that the authors apply artificially made crack patterns for building both the statistical and AI models. This is to compensate for the limitation of available crack patterns on which the model is based and it is a highlight of this study. Randomized artificial crack pattern program (RACP) is introduced to build the evaluation models to cover all possible ranges of fatigue life. The real crack patterns will be used only for the validation of the proposed ANN model since the final objective is to check the deterioration-magnitude corresponding to real crack patterns on site. Second, on the basis of the simulation results, an accurate correlation is proposed between the remaining fatigue life and a mechanics-based parameter of the bottom surface cracks (cracks length and width), where it can be used as a fast-truck judgment of the degree of deterioration of the decks. Third, the authors further challenge an artificial neural network model (ANN) for fair-detailed assessment of deterioration-magnitude. This method has the advantage to be able to handle the geometrical patterns which consist of massive numbers of cracking.