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In Japan, large earthquakes have caused many damages to structures. Cracks in the reinforced concrete and plasticization of the steel bar and steel components resulted in increased damage. In the seismic design, the dynamic response analysis is carried out by using a mathematical model that appropriately evaluates the nonlinearity of the material and the components and the seismic performance is confirmed by performing a dynamic response analysis. However, when applying new materials and components, much time and much effort are required to select a mathematical model. In this study, we focused on the high pattern recognition capability of the neural network. We attempt to directly model using a neural network without replacing the complex nonlinear hysteretic behavior using the mathematical model. By improvement of learning data and introduction of deep learning, it was confirmed that the recognition ability for nonlinear hysteretic behavior of neural network was greatly improved, and its applicability as a numerical operation subroutine for time history response analysis was drastically improved.

近年，III種地盤では共振しにくい橋として，多点固定橋が適用されている．しかし，多点固定橋では，温度変化やクリープ等による桁の伸縮に起因する不静定力により，地震時の挙動に影響を及ぼす．PC多点固定橋では，不静定量のうちクリープ等の影響は地震時の設計に見込まれているが，温度変化の影響は見込まれていない．本論文ではPC多点固定橋を対象に，温度変化による桁収縮が地震時に及ぼす影響を評価した．検討結果より，PC多点固定橋では地震時応答への不静定量の影響は大きいものの，温度変化の影響は大きくないことが明らかとなった．これは，常時状態で不静定量により橋脚断面が決定されており，不静定量に占める温度変化の影響はクリープ等に比べて小さいためである．

既設橋梁などの耐震補強検討に対してよく使用される摩擦履歴型の制震ダンパーはその特性が抵抗力－速度の関係で表されるが，速度依存性が小さいため，解析時には抵抗力－変位の関係でモデル化されることがある．本検討では抵抗力－速度の関係でモデル化した場合と，抵抗力－変位の関係でモデル化した場合の応答値を単純なモデルで動的解析によって比較している．その結果，固有周期差等によっては応答が大きく異なる場合があることが明らかになり，ダンパーを使用して解析する際のモデル化上の留意点を示した．

This paper presents results of theoretical analyses on in situ vibration test results of large-scale rigid foundations on bedrock, and estimation method of interaction characteristics of foundation on bedrock, where attention is paid to the layered structure of the bedrock. Stiffness and damping of the foundation estimated from theoretical analysis using ground material properties based on elastic wave detection proves to compare well with experimental ones. Using the results of parameter survey with two layered ground model, damping characteristics of the foundation on layered bedrock is shown to be estimated by simple two-layered ground model.

Generally, in formulating a spring-mass model for hysteric behavior of materials and members with inelastic characteristic, a mathematical model based on load-deformation experimental results is considered. The model must approximate the inelastic hysteresis of the material. However, assumption of material’s behavior using mathematical models is crucial, since it may cause serious errors if inappropriate model is applied for a particular situation. This paper describes multiple layered neural network to simulate the non-linear hysteretic behavior like Ramberg-Osgood model, modified bilinear model and Takeda model. In this study, based on the pattern recognition ability of neural network, non-linear hysteretic behavior was modeled by the network directly without replacing it with a mathematical model. The effectiveness and applicability of the network in numerical analysis were evaluated. Generalized multiple layered neural network to evaluate non-linear hysteretic curve was constructed. The network can recognize well the three types of hysteretic curve. The network is available as a subroutine of non-linear spring in dynamic response analysis.Copyright © 2003 by ASME

1999年に発生した台湾集集地震により, 我国では例の無い送電鉄塔の地震被害が発生した. 本研究では, まず超高圧送電鉄塔に関する最新の被害調査結果をまとめた. 次に, 架渉線-連成系の影響を簡易に考慮した鉄塔の単体モデルを提案した. 更に, 台湾で最大の345kV鉄塔で唯一完全倒壊した#203鉄塔が片継脚鉄塔であることに着目し, 地震時の振動により倒壊した可能性を解析的に検討した. その結果, 付近で観測された地震波を入力した場合, #203鉄塔は脚部に全体座屈が発生する可能性が高いとの知見が得られた. 最後に, #203鉄塔の復旧時において基礎形式がつなぎばり基礎に変更されたことを受けて, 基礎の不同沈下時の鉄塔の耐震性向上策として, つなぎばり基礎の有効性を示した.