Noboru Yasuda

Inverse Analysis of Aesthetic Evaluation of Planted Concrete Structures by Neural Networks

Scenery evaluation depends on the evaluators experiences or perceptions. Thus inverse analysis by neural networks of scenery evaluation of planted concrete structures (concrete retaining walls) was examined in this study. In addition, in order to identify the evaluation schemes of evaluators, sensitivity analysis was performed on the obtained neural network structure. The efficacy of neural network inverse analysis and genetic algorithm analysis using fuzzy-set theory in reproduction of the same scenery evaluation questionnaires results also was compared.

Assessment of deteriorating reinforced concrete structures using artificial neural networks

An artificial neural network was used to assess deteriorating reinforced concrete (RC) structures using periodical inspection data for thermal power plants along the coast of Tokyo Bay arranged by the Tokyo Electric Power Company. In the analysis, the focus is on chloride-induced corrosion damage of RC structures. 13 input variables such as crack width, crack direction, number of cracks, etc. were selected as the inputs to the artificial neural network, and four output variables were chosen as the desired damage levels. Using a successfully trained neural network, a sensitivity analysis determines the influence of a change in each variable such as maximum crack width, area of peeling-off of concrete, exposure of reinforcement, etc., on the damage level.<<ETX>>

A STUDY ON MODELING THE DISTRIBUTION OF LUGEON VALUES IN ROCK MASS

岩盤の止水性評価はルジオン値の超過確率分布で行われてきた. ダム建設時に十分な止水性を確保するためにカーテンウォールを構築し, その止水性もルジオン値で評価している. 本研究では, ルジオン値の新しい分布形状のモデル化提案し, この分布形状からルジオン値の超過確率を求め, カーテンウォールの効果を評価している. 本手法を対象となった2地点に適用し, 有効な評価結果を得ている.