Ikumasa Yoshida

The application of data science technologies in geotechnical and earthquake engineering is a hot topic. This study aimed to identify the macroscopic dynamic properties of the soil from the previous records of seismic motions observed at the ground surface utilizing the dynamic mode decomposition (DMD). The key to our ingenuity was to replace the soil layer composition and dynamic properties with a single-degree-of-freedom (SDOF) vibration model based on the ground surface observation records. In the validation process, first, a comparison was made between the proposed method and the analytical solution for an SDOF vibration system; second, a comparison was made with a one-dimensional equivalent linear multiple reflection theory analysis considering the nonlinear soil profile. The proposed method effectively approximated complex ground profiles to an equivalent SDOF vibration system and constructed shear strain-dependent models of the macroscopic pseudo-shear modulus and damping constant from the observed ground surface seismic motions. This study was based on numerical experiments and limited conditions of small seismic amplitudes for which equivalent linear approximations could be made. Based on the results obtained in this paper, we aim to extend the model to wide-area forecasting by improving it to a practical model that covers strong nonlinearities.

近年，大規模災害に対するインフラ施設のレジリエンス性向上の重要性が指摘されている．本研究は，構造物設計側からレジリエンス性能向上へ貢献するためのモニタリング設計法の構築を試みている．液状化懸念層上の盛土の地震時挙動評価を例題として，観測点最適化法および適応型即時予測法の基本的な枠組みを提案している．数値解析結果から対象施設の空間モード関数群を導き，空間モード関数群の特性を有効活用した簡易法を提案している．観測点最適化では，QR-pivoting による貪欲法が工学的に有用な情報を提供することを確認した．適応型即時予測では，周辺尤度を用いて予測に用いる空間モード関数の数を即時に選択（モデル選択）した上で，カルマンフィルタにより構成係数を予測する方法を提案した．最後に，検証用入力地震動の数値解析結果と提案手法による適応型予測を比較した．提案手法は，入力地震動の特性に応じて，リアルタイムに適切なモデル選択を行い予測精度を向上させることを確認した．

Benchmarking is very valuable for evaluating and comparing methodologies. Here, Gaussian process regression using multiple Gaussian random fields (GPR-MR) is applied to benchmarking data for spatial variability problems. The benchmarking data used were from the literature and included four types of virtual ground models (VG1 to VG4) and one real ground measurement data set. The spatial variability of geological properties is often divided into a trend component and a random component. In GPR-MR, the trend component is expressed by a random field with a large scale of fluctuation (SOF), leading to a smooth (slow) variability, whereas the random component is expressed by one with a small SOF, leading to a rapidly changing variability. The SOF and the standard deviation of random fields were estimated using the maximum likelihood method based on the measured data provided in the benchmarking data. GPR-MR was used to estimate the spatial variabilities of all cases, and its performance was evaluated. For the real ground measured data, model selection was also performed with respect to the autocorrelation function of the random component in terms of information criteria, whereas the Markovian autocorrelation function was used for the virtual ground data without the model selection. Based on the results, the Whittle-Matérn (WM) model was selected for the random component. GPR-MR was used to estimate the spatial variability, and its performance with the WM model was evaluated.