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.

In visual inspection of bridges at sites, much attention is given to the density and width of cracks of concrete, but little attention is paid to crack orientation for the diagnosis of bridge performance. In this research, the effect of crack orientation on the remaining fatigue life of reinforced concrete (RC) bridge decks is investigated for crack patterns with a wide range of possible crack orientations. The data assimilation technology of multi-scale simulation and the pseudo-cracking method, which are widely validated for fatigue-lifetime simulation, are utilized in this study. The impact of the crack direction on fatigue life is found to be associated with the coupled flexure-shear mode of failure, and the mechanism to arrest shear cracking by preceding cracks is quantitatively estimated. This mechanism is similar to the stop-hole to prevent fatigue cracks in steel structures, and it enables us to enhance the fatigue life of RC decks. It is demonstrated that the crack orientations that approximate the longitudinal and transverse directions of RC decks are the ones that most extend remaining fatigue life. Finally, the higher risk cracking locations on the bottom surface of RC decks are discussed, presenting information of use to site inspectors.

Stagnant water on reinforced concrete (RC) decks reduces their life significantly compared to the case of dry states. Fully submerged states have been investigated as the most severe case, which is however rarely experienced in reality. Currently, it is possible to simulate concrete–water interactions for lifetime prediction of RC decks. In this study, fatigue lifetime is systematically computed for various locations of stagnant water at the upper layer of RC decks. It is found that the patterns of wet and dry areas have a great influence on the remaining fatigue life even though the same magnitude of cracking develops. Then, a hazard map for the wetting locations with regard to the remaining fatigue life is presented based on the systematically arranged simulation. Finally, a nonlinear correlation is introduced for fatigue life prediction based upon site inspected wetting locations, which can be detected by non-destructive testing technology.

Multiple factors, regarding mechanical properties, load levels, and environmental conditions, affect the fatigue lifetime of reinforced concrete (RC) bridge decks. For the mechanical behavior, previous research predicts the fatigue lifetime of RC decks as a function of their punching shear capacity, where they give less attention to other modes of failure due to experimental limitations of fatigue loading and the restriction of girders spacing in the past design practice. Nowadays, multi-scale simulation can deal with fatigue loading problems, which secures examining complex situations that cannot be easily reproduced in the laboratories of fatigue tests. In this study, various RC decks with wide range of dimensions, material properties, reinforcement ratio, and load levels are analyzed by the validated multi-scale simulation. Then, artificial neural network (ANN) based model is also proposed based on wide-range of studied cases, which estimates the fatigue life of newly constructed RC decks, where it can be the basis of performance-based design. After that, the impact of deck’s properties on fatigue life is evaluated based on the built ANN model, which matches the conceptual design of RC decks. Finally, coupling of an empirical equation and ANN model is proposed, which may support conceptual decision-making.

市町村は多くのインフラを管理しているが，そのメンテナンスに苦労している．特に北陸地方のコンクリート構造物では，塩害やASRによる劣化が生じやすく，早急な対応が望まれる．このような背景を踏まえて本稿では，地元の大学・高専教員が連携して，市町村が管理する道路橋を対象にした，メンテナンスの合理化に向けた支援について論ずる．はじめに，新潟県上越地区・富山県・石川県・福井県の41市町に対してヒアリング調査を実施し，課題とニーズを抽出した．次に，これらを改善すべく，コンクリート橋に対する点検・診断・措置の手順案を策定し，また技術展示会を開催し，さらに実橋を用いた補修効果の評価に取り掛かった．以上により，市町村の実情を起点とした，官学連携による道路橋の維持管理の合理化に向けた実績とその評価を整理した．