Details of a Researcher - Kohiyama, Masayuki

Presentations -

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Development and Response Validation of a Bi-Directionally Tunable Rigid-Body-Swinging and Horizontal-Spring Hybrid Tuned Mass Damper in a Full-Scale Model

Shimizu, Y., Kohiyama, M.

[International presentation] the 14th International Conference on Structural Safety and Reliability (ICOSSAR’25) (University of Southern California, Los Angels, CA) ,

2025.06

Oral presentation (general), The International Association for Structural Safety and Reliability

High-rise buildings can be significantly affected by external excitations such as earthquakes and wind and tuned mass dampers (TMDs) are widely used to address this issue. Conventional TMDs can only suppress a single vibration mode. Therefore, a bi-directional tuned mass damper (BTMD) has been proposed for buildings with different natural frequencies in the two horizontal directions. However, adjusting the natural frequency of a BTMD to accommodate changes and uncertainty in the natural frequencies of a building is challenging. This study proposes a novel BTMD named the hybrid tuned mass damper with rigid body swinging and horizontal spring vibration (RH-TMD) as a solution, which combines rolling motion of an auxiliary mass on a rail induced by horizontal spring and swinging motion of the rail and mass in the orthogonal direction. The natural periods can be adjusted by modifying the weight of the mass in rolling motion and the length of the pendulum in swinging motion. A rotary damper with an adjustable damping coefficient is adopted as a damping mechanism.
First, numerical analysis using a 50-story model are conducted to examine the performance of the RH-TMD. The vibration characteristics, natural periods and damping coefficients of the RH-TMD in the two horizontal directions are optimized to minimize the root mean square response to white noise ground acceleration input. Its performance is compared with a conventional (simple) TMD with a natural period and damping coefficient optimized only in a single direction with respect to absolute acceleration and story drift responses to seismic ground motion.
In addition, shaking table experiments are conducted using a small-scale single-layer specimen with a similarity ratio of 1:100, equipped with either an RH-TMD or a simple TMD to compare the responses to white noise ground acceleration and simulated seismic motions.
The results of the numerical analysis and shaking table experiments validate the excellent performance of the RH-TMD in reducing absolute acceleration and story drift responses in the two horizontal directions.

Torsional Response Due to Q–Δ Effect and Its Internal Resonance

Kohiyama, M.

[International presentation] the 14th International Conference on Structural Safety and Reliability (ICOSSAR’25) (University of Southern California, Los Angels, CA) ,

2025.06

Oral presentation (invited, special), The International Association for Structural Safety and Reliability

The P–Δ effect is well known, in which an overturning moment emerges due to geometric nonlinearity when a gravity-loaded structure undergoes a large deformation. Similarly, when a column undergoes a large horizontal displacement, a torsional torque emerges to preserve the state of equilibrium. The speaker named this effect “Q–Δ effect.” Although the effect has not been considered in structural design, he has shown that periodical nonlinear restoring torque of the Q–Δ effect can produce a non-negligible torsional response in a structure during earthquakes owing to its internal resonance, named “Q–Δ resonance.” This talk will first explain the theory of the Q–Δ effect and the mechanism of the Q–Δ resonance. Then, he will present the results of shaking-table experiments using a reduced specimen and finite element analysis of a full-scale model of high-rise building under long period ground motions. This resonance phenomenon is a blind spot in seismic design, and given the mechanism of its occurrence, it may need to be considered in wind-resistant design as well. Researchers and engineers are expected to listen to this talk to widely promote proactive countermeasures.

Accuracy Improvement of Damage Classifier for a Wooden Building Using Long Short-Term Memory with Response Surface Method

Shimizu, Y., Kohiyama, M. Yamashita, T.

[International presentation] the 14th International Conference on Structural Safety and Reliability (ICOSSAR’25) (University of Southern California, Los Angels, CA) ,

2025.06

Oral presentation (general), The International Association for Structural Safety and Reliability

Structural health monitoring (SHM) has attracted significant attention as a method for rapidly and efficiently assessing the damage state of buildings immediately after an earthquake. Application of SHM to houses could facilitate prompt evaluations of the risk of collapse from subsequent earthquakes, thereby reducing casualties caused by aftershocks and other seismic events. This study proposes a damage classification method for wooden buildings based on acceleration responses, using long short-term memory (LSTM), which is well-suited for capturing the long-term dependencies of time-series data.
The analysis focuses on a specimen from the dataset of “Experimental Validation of Design Methods for Three-Story Wooden Frame Structures,” published by the National Research Institute for Earth Science and Disaster Resilience. A building model was constructed and analyzed using “wallstat,” a numerical analysis software for wooden structures. To generate training data for a damage classifier, response analysis of the model was conducted using 2,000 simulated seismic ground motions consisting of equal number of pulse and non-pulse types ground motions. The acceleration response of the first and second floors were converted input data for the damage classifier so that each input segment contains one second of acceleration responses. To prevent data imbalance caused by similar input segments with small variations in acceleration, a “lag” was introduced, ensuring that each input segment shifts by a specific number of data points relative to the previous one.
In SHM, misclassifying an unsafe condition as safe poses a severe risk to building users, as users may overlook potential building collapse hazards leading to fatalities. To address this, training data near the threshold between safe and unsafe models were excluded from safe model datasets to reduce the possibility of misclassification.
Three hyperparameters of the damage classifier, the number of LSTM units, the number of nodes in the first fully connected layer, and the learning rate, were optimized using the response surface method. This optimization process identified a combination of hyperparameter values that achieved high classification accuracy. A two-level central composite design was employed for the design of experiments.
The proposed damage classifier demonstrated a high accuracy of 92.4%, marking a significant step toward practical implementation. This study highlights the effectiveness of LSTM for damage classification in wooden buildings and underscores the utility of the response surface method in optimizing machine learning models.

3次元畳み込みニューラルネットワークを用いた履歴ループに基づく木造建物の損傷判別システム

千葉荘輝，小檜山雅之，山下拓三

[Domestic presentation] 日本地震工学会・大会－2024 (じゅうろくプラザ（岐阜市文化産業交流センター，岐阜市）) ,

2024.12

Oral presentation (general), Japan Association for Earthquake Engineering

The shape of the restoring force–story drift angle relationship curve (hysteresis loop) derived from the seismic response data of a building changes depending on its structural health. Taking advantage of this property, this study proposed a method to assess the risk of collapse due to future earthquake motions based on hysteresis loop images considering the time axis direction using a three-dimensional convolutional neural network (CNN). The effectiveness of the proposed method was verified by comparing the accuracy and recall of the unsafe model between the proposed method and a prior research method using a two-dimensional CNN. It was confirmed that the proposed method adjusting the image size and division number in the time axis direction has a higher accuracy and robustness against observation noise.

Shaking Table Experiment of a Rigid Body Swing and Horizontal Spring Oscillation Combined Type Tuned Mass Damper Tunable in Two Directions

SHIMIZU Yuki, KOHIYAMA Masayuki

[Domestic presentation] Annual Meeting of Architectural Institute of Japan (Surugadai Campus, Meiji University (Chiyoda-ku, Tokyo)) ,

2024.08

Oral presentation (general), Architectural Institute of Japan

Accuracy Improvement of Damage Classifier for a Wooden Building Based on Acceleration Response Using Long Short-Term Memory

MAEDA Toshiakira, KOHIYAMA Masayuki, YAMASHITA Takuzo

[Domestic presentation] Annual Meeting of Architectural Institute of Japan (Surugadai Campus, Meiji University (Chiyoda-ku, Tokyo)) ,

2024.08

Oral presentation (general), Architectural Institute of Japan

ニューラルネットワークを用いた木造建物の地震被害判別器の学習データセット作成法に関する研究

溝淵陸大，小檜山雅之，山下拓三

[Domestic presentation] 第16回日本地震工学シンポジウム (パシフィコ横浜ノース4階（神奈川県横浜市西区みなとみらい１丁目１−２）) ,

2023.11

Poster presentation, 日本地震工学会、地域安全学会、地盤工学会、土木学会、日本活断層学会、日本機械学会、日本建築学会、日本災害情報学会、日本災害復興学会、日本地震学会、日本自然災害学会、日本都市計画学会

畳み込みニューラルネットワークを用いた履歴ループに基づく木造建物の損傷判別システム

千葉荘輝，小檜山雅之，山下拓三

[Domestic presentation] 第16回日本地震工学シンポジウム (パシフィコ横浜ノース4階（神奈川県横浜市西区みなとみらい１丁目１−２）) ,

2023.11

畳み込みニューラルネットワークを用いた履歴ループに基づく木造建物の損傷判別法に関する基礎的研究

千葉荘輝，小檜山雅之，山下拓三

[Domestic presentation] 第10回構造物の安全性・信頼性に関する国内シンポジウム (日本学術会議（東京都港区六本木7-22-34）) ,

2023.10

Oral presentation (general), 日本学術会議土木工学・建築学委員会

東京都庁第一本庁舎で観測されたねじれ応答へのQ-Δ効果の寄与に関する基礎的研究

溝部直人，小檜山雅之

[Domestic presentation] 第10回構造物の安全性・信頼性に関する国内シンポジウム (日本学術会議（東京都港区六本木7-22-34）) ,

2023.10

Oral presentation (general), 日本学術会議土木工学・建築学委員会

地震応答の安全限界超過確率に基づくサーバのウォームスタンバイ法の提案

平岡笑暢，木田祐輔，小檜山雅之

[Domestic presentation] 第10回構造物の安全性・信頼性に関する国内シンポジウム (日本学術会議（東京都港区六本木7-22-34）) ,

2023.10

Oral presentation (general), 日本学術会議土木工学・建築学委員会

Shaking Table Test of a Warm Standby Method Based on Exceedance Probability of Sever Seismic Response over Failure Threshold

HIRAOKA Enon, KIDA Yusuke, KOHIYAMA Masayuki

[Domestic presentation] Annual Meeting of Architectural Institute of Japan (Yoshida Campus, Kyoto University) ,

2023.09

Oral presentation (general), Architectural Institute of Japan

長周期地震動によるセンターコア型二軸対称超高層建物のＱ-Δ共振に関する立体骨組解析

牧栞里，小檜山雅之

[Domestic presentation] 日本地震工学会・大会－2022 (北海道大学) ,

2022.12

Oral presentation (general), Japan Association for Earthquake Engineering

水平2方向の剛性が異なる建物に大変位が生じると幾何学的非線形性によりねじりモーメントが発生する．そのため，水平2方向の並進モードの固有振動数の和または差がねじれモードの固有振動数と近接するとねじれモードが励起されるQ–Δ共振が発生する．そこで，Q–Δ共振条件を満足する二軸対称な平面を持つセンターコア型の超高層建物の立体骨組モデルを作成し長周期地震動でねじれ応答が励起されるか検討を行った．弾性応答を仮定し幾何学的非線形性を考慮して時刻歴応答解析を行った結果，ねじれモードが励起されることを確認した．

4層曲げせん断型試験体のQ–Δ共振実験

甲斐凌平，小檜山雅之

[Domestic presentation] 日本地震工学会・大会－2022 (北海道大学) ,

2022.12

Oral presentation (general), 日本地震工学会

塔上比が大きく曲げ変形が卓越する建物に関し，幾何学的非線形性に起因するねじれモードの内部共振現象であるQ–Δ共振の特性を分析するため，縮小試験体を設計・製作し振動台実験を行った．その結果，Q–Δ共振条件を満たす場合にねじれ応答が大きくなることを確認した．また，塔上比がより小さく曲げ変形が生じにくい試験体とのねじれ応答の比較を行った結果，前者の方がねじれ応答のピークが急峻になることが確認された．

Evaluation of Torsional Response of a Single-Story Bending-Shear Type Non-Eccentric Building Induced by Q–Δ Effect

KAI Ryohei, MAKI Shiori, KOHIYAMA Masayuki

[Domestic presentation] Annual Meeting of Architectural Institute of Japan (Hokkaido University of Science and online) ,

2022.09

Oral presentation (general), Architectural Institute of Japan

Transfer Learning Method for Deep Neural Network to Improve the Accuracy of Damage Pattern Identification in a Wooden Building

HIRAOKA Enon, KOHIYAMA Masayuki, YAMASHITA Takuzo

[Domestic presentation] Annual Meeting of Architectural Institute of Japan (Hokkaido University of Science and online) ,

2022.09

Oral presentation (general), Architectural Institute of Japan

Damage Recognition of Wooden House Using Deep Neural Network Trained with Simulated Responses Considering Epistemic Uncertainty

Masayuki Kohiyama, Takuma Kumagai and Takuzo Yamashita

[International presentation] 15th World Congress on Computational Mechanics (WCCM-XV) & 8th Asian Pacific Congress on Computational Mechanics (APCOM-VIII) (Yokohama, Japan Virtual) ,

2022.07

2022.08

Oral presentation (invited, special), International Association for Computational Mechanics (IACM) and the Japan Society for Computational Engineering and Science (JSCES)

The Data Science Working Group of the Numerical Shaking Table Committee of the National Research Institute for Earth Science and Disaster Resilience is working on the development of structural health monitoring technology using artificial intelligence.
In the 2016 Kumamoto earthquakes, some residents returned to their wooden houses that had been severely damaged by the strong ground motion, and the houses collapsed due to the following main shock causing human casualties. This kind of disaster should be reduced if they could understand the remaining performance of the house using a structural health monitoring system.
Deep neural networks have been reported to achieve high accuracy in various fields in pattern recognition and evaluation of regression equations with a large amount of training data. The authors have also taken the same approach of preparing big data for training by conducting time history response analysis with many input earthquake motions and damage patterns in advance.
However, the stiffness and strength of the members assumed at the design stage do not necessarily match the actual values of the completed building. Epistemic uncertainty exists, and it is desirable to recognize damage patterns and evaluate residual performance with features that are robust to such variations.
In this study, we propose a method of generating training data with variability based on the results of elemental experiments conducted in conjunction with full-scale wooden building experiments in the E-Defense experiment, and constructing training data by providing input seismic motions of appropriate intensity so that the number of training data with and without damage is balanced.

Global Sensitivity Analysis of Wooden Building Based on Polynomial Chaos Expansion Considering Uncertainty in Material Property

Rikuto Mizobuchi and Masayuki Kohiyama

[International presentation] 15th World Congress on Computational Mechanics (WCCM-XV) & 8th Asian Pacific Congress on Computational Mechanics (APCOM-VIII) (Yokohama, Japan Virtual) ,

2022.07

2022.08

Oral presentation (general), International Association for Computational Mechanics (IACM) and the Japan Society for Computational Engineering and Science (JSCES)

Structural health monitoring (SHM) is useful for quick and efficient damage recognition of a building under seismic excitation. It has the potential to replace the emergency safety evaluation, which is conducted after major earthquakes in purpose to prevent a secondary disaster from aftershocks. SHM systems are popular for large-scale civil structures in the world, but their application to low-rise buildings such as wooden houses are still limited.
One of the challenges in predicting the response of a wooden structure is that the effect of variance in its material property is considerably significant. To solve this problem, we propose a method using global sensitivity analysis with Sobol’ indices based on polynomial chaos expansion (PCE) to find dependent parameters. By considering only material parameters with a high impact on the variability of the quality of interest, we can reduce computational cost in time history response analyses. PCE is a popular technique in the field of uncertainty quantification, which enables us to model the response of a dynamical system with uncertainty. PCE is also known to be highly compatible with global sensitivity analysis, which allows to identify the dependent input variables that contribute most to the variability of the computational model response.
Based on the results of a full-scale three-story wooden building experiments and its member experiments conducted by E-Defense of National Research Institute for Earth Science and Disaster Resilience, we performed three-dimensional structural response analyses of a model of the specimen using a numerical collapsing simulation program, wallstat. As input ground motions, we used design ground motion specified by the Building Standard Law with an amplification factor of 120%. Multiple models were prepared with a varied value of material properties to consider the epistemic uncertainty. As a result, the sensitivity analysis clarified that the variance of Young's modulus in columns and beams had a major contribution to the variance of the maximum inter-story drift angle of the model.

Construction of a Damage Classifier for a Wooden Building Using Sensitivity Analysis with Polynomial Chaos Expansion

Rikuto MIZOBUCHI and Masayuki KOHIYAMA

[Domestic presentation] The 51st Symposium Reliability, Maintainability and Safety (Online (Union of Japanese Scientists and Engineers, Higashi Koenji Bldg., Tokyo)) ,

2022.07

Oral presentation (general), Union of Japanese Scientists and Engineers

Structural health monitoring is effective for quick and efficient recognition of earthquake damage to a building. However, its applications to wooden buildings are still limited. One of the challenges in predicting the response of a wooden building is that the variance in its material property considerably affect its response. To handle this characteristic, we propose a method using global sensitivity analysis based on polynomial chaos expansion to select dominant material parameters in the variability of a quantity of interest. The method enables us to reduce the computational cost in multiple time-history response analysis, which is necessary to construct a damage classifier that takes the variance of material property into account by machine learning.

Linear Quadratic Regulator Design for a Seismic Control System to Minimize Recovery Time of Medical Facility

Masayuki Kohiyama and Akiho Kitatani

[International presentation] 8th World Conference on Structural Control and Monitoring (8WCSCM) (Orlando, Florida (hybrid conference)) ,

2022.06

Oral presentation (general), The International Association for Structural Control and Monitoring (IASCM)

This study investigates the design of a linear quadratic regulator (LQR) for a seismic control device to improve the resilience of a medical facility. The functionality of a medical facility is quantified based on a fault tree. A frame-work to evaluate the recovery curve of a medical facility is constructed based on the fault tree and recovery-time probability models for damaged medical devices. Furthermore, the approach to design parameters for an LQR control system is investigated to minimize the time required to recover up to the de-fined functionality level after the onset of an earthquake. A case analysis is conducted using a model of a ten-story hospital building with a control device installed on its top floor, and the resulting expected recovery time is evaluated. It was confirmed that the proposed method could reduce the recovery time even for the different input ground motion from that used in a design stage.

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