Details of a Researcher - Kohiyama, Masayuki

Papers - Kohiyama, Masayuki

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Accuracy Improvement of Damage Classifier for a Wooden Building Considering Pulse-like Seismic Ground Motions

MIZOBUCHI Rikuto, KOHIYAMA Masayuki, and YAMASHITA Takuzo

Journal of Japan Association for Earthquake Engineering (Japan Association for Earthquake Engineering) 25 ( 4 ) 97 - 108 2025.03

Research paper (scientific journal), Accepted, ISSN 1884-6246

Rapid damage assessment by a structural health monitoring system is expected to prevent casualties caused by the collapse of wooden buildings due to repeated seismic motions. While neural networks are known to be a powerful tool for determining the risk of collapse based on the response records, they are not good at classifying untrained damage patterns. This study proposed a method to consider the diversity of damage patterns in the construction of a damage classifier for wooden buildings. This is achieved by using pulse-like simulated ground motions as the input ground motions for seismic response simulation of a target building model in the development of the training dataset. The effectiveness of the proposed method was validated by comparing its accuracy on test data.

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Damage Classifier for Wooden Buildings Based on Hysteresis Loop Images Using CNN

Chiba, S., Kohiyama, M., and Yamashita, T.

Proc. of the 18th World Conference on Earthquake Engineering 2024 (WCEE2024) 2024.06

Research paper (international conference proceedings), Joint Work, Accepted

Structural health monitoring (SHM), which evaluates building damage based on seismic response data, is useful for rapidly assessing damage due to earthquakes. If a reliable damage assessment system for houses can be developed, the loss of human lives caused by the aftershocks of major earthquakes can be reduced. When a target building is damaged by strong seismic ground motions, the shape of the restoring force–story drift angle curve (hysteresis loop) changes. Therefore, based on the hysteresis loop images of wooden buildings, this study proposes a method to classify the risk of collapse because of future seismic ground motions using a convolutional neural network (CNN). To develop training data, structural response analysis is conducted using “wallstat”, which is a numerical analysis software for wooden structures. SHM has more severe consequences when it misclassifies an unsafe condition as safe (miss) than when it misclassifies a safe one as unsafe (false alarm). Therefore, we propose the usage of a dataset that does not contain safe model data near the threshold value of major damage for training to decrease the possibility of misses, and we verify the effect of this. In creating the hysteresis loop images, we examine the range of the axes using two methods: fixing it at specific values and matching it to the range of the minimum and maximum values. The investigation results suggest that the trained CNN learns the changes in structural characteristics, such as stiffness reduction, damping increase, and residual displacement.

A Warm Standby Based on the Probability to Exceed Damage Threshold for Seismic Response of a Server

Yusuke KIDA, Enon HIRAOKA, Masayuki KOHIYAMA

Journal of Social Safety Science (Institute of Social Safety Science) ( 43 ) 19 - 27 2023.11

Research paper (scientific journal), Joint Work, Last author, Accepted, ISSN 1345-2088

This study proposes a new warm standby method for computer servers based on seismic response. The proposed method triggers a warm standby in the case that the exceedance probability of the safety limit of seismic response predicted by the last two-second observation record exceeds a preset threshold. Numerical simulations showed that the proposed method reduced the loss cost by 1.64% compared to conventional methods. To validate this method, we conducted shaking table tests. When a simulated earthquake motion was input, the loss cost was reduced by 60.6%. However, when observed records were input, the loss cost was increased by 3.89%. The results suggest that it is necessary improve the input ground motion model.

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Development of Tool to Enable Autonomous In-Person and Remote Tabletop Exercises by Residents for Developing Next-Generation Leaders for Volcanic Disaster Reduction

Masayuki KOHIYAMA, Honoka TAKASHIMA, Masayasu YAMAUCHI, Mitsuhiro YOSHIMOTO, Tomohiro KUBO

Journal of Social Safety Science (Institute of Social Safety Science) ( 43 ) 315 - 325 2023.11

Research paper (scientific journal), Joint Work, Lead author, Corresponding author, Accepted, ISSN 1345-2088

To reduce volcanic disasters, it is important for residents to practice mutual support under the leadership of voluntary disaster reduction groups. However, many communities lack the human resource for their future leaders. To address this issue, we proposed a digital tool for residents to develop leaders by themselves. The system requirements were determined to enable residents to conduct autonomous and effective tabletop exercise in both in-person and remote formats. The usefulness of the tool was confirmed through workshops with residents of the areas at the foot of Mt. Fuji.

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Study on a Simplified Model of a High-Rise Shear-Type Building for Evaluation of Q-Δ Resonance

Anamizu R., Kohiyama M.

Japan Architectural Review (Wiley) 6 ( 1 ) 1 - 16 2023.10

Research paper (scientific journal), Joint Work, Last author, Corresponding author, Accepted, ISSN 2475-8876

To evaluate Q–Δ resonance, which is a torsional resonance phenomenon caused by geometric nonlinearity, a simplified model for a multistory non-eccentric building with a shear-type structure was proposed. A four-story small-scale specimen was designed and fabricated, assuming a non-eccentric steel structure with a height of 324 m, and conducted shaking table tests of the specimen. Q–Δ resonance was confirmed as predicted by the simplified model. In addition, the simplified model reproduced the resonance amplitude calculated using a finite element model of the specimen with constrained out-of-plane slab rotation. The simplified model can be used to predict the Q–Δ resonance response when the bending deformation of an entire building is very small.

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Probability Distribution of Torsional Response by Q–Δ Resonance of Multi-Story Building under Long-Period Seismic Motion

Kohiyama M.

Proceedings of the 14th International Conference on Applications of Statistics and Probability in Civil Engineering (ICASP14) 2023.07

Research paper (international conference proceedings), Single Work, Lead author, Corresponding author, Accepted

When the torsional moment owing to geometric nonlinearity synchronizes with the torsional mode response, an internal resonance phenomenon occurs in a building structure system. The author’s research group calls this phenomenon Q–Δ resonance. The probability distribution of the torsional response was evaluated using Monte Carlo simulations on a 60-story building model, the input wave being simulated using nonstationary colored-noise ground motion. The results showed that the torsional response greatly increased the horizontal acceleration response and the response amplification ratio between the center and corner of the top floor could exceed 1.2 when the model satisfied the Q–Δ resonance condition that the natural frequency of the torsional second mode matched the sum of the first natural frequencies in the x- and y-directions. It was also evident that closer proximity to the resonance condition increased the amplification ratio. These results suggest that Q–Δ resonance may need to be considered in seismic design.

BAYESIAN UPDATING OF WOODEN STRUCTURAL PARAMETERS AND SEISMIC DAMAGE CLASSIFIER USING NEURAL NETWORK

Mizobuchi R., Kohiyama M., Yamashita T.

Proceedings of the 9th International Conference on Computational Methods in Structural Dynamics and Earthquake Engineering (COMPDYN 2023) 1 2335 - 2347 2023.06

Research paper (international conference proceedings), Joint Work, Accepted, ISSN 26233347

Structural health monitoring is an effective way to quickly and efficiently detect earthquake damage to a building. However, its applications to wooden buildings are still limited. One of the challenges in predicting the seismic response of a wooden structure is that the effect of variance in its material and structural properties is relatively significant. To handle this problem, we propose a method to update the damage classifier using Bayesian system identification. The damage classifier is constructed using neural network (NN) and machine learning techniques. The method utilizes the weighted loss function for the NN to reflect the updated probability distribution of the structural parameters. The NN is tuned using backpropagation, where the training data are consequently weighed by the weighed loss function, directly using the value of the posterior probability, according to the data obtained from the numerical model of a target structure with the corresponding parameters. Using the result of the Bayesian updating, the tuned NN damage classifier achieved a higher accuracy than before tuning by reflecting the posterior probability distribution of the structural parameters.

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THREE-DIMENSIONAL FRAME ANALYSIS OF Q-Δ RESONANCE OF CENTER-CORE BIAXIALLY SYMMETRICAL HIGH-RISE BUILDING UNDER LONG-PERIOD EARTHQUAKE MOTION

Kohiyama M., Maki S.

Proceedings of the 9th International Conference on Computational Methods in Structural Dynamics and Earthquake Engineering (COMPDYN 2023) 1 961 - 970 2023.06

Research paper (international conference proceedings), Joint Work, Lead author, Accepted, ISSN 26233347

When large displacements occur in buildings with different stiffnesses in the two horizontal directions, torsional moments are generated owing to geometric nonlinearities. Consequently, when the sum or difference of the translational mode natural frequencies in the two horizontal directions is close to the torsional mode natural frequency, the torsional mode is excited. This is known as Q-Delta resonance. Therefore, we created a three-dimensional frame model of a center-core high-rise building with a biaxially symmetrical plane that satisfied the Q-Delta resonance condition and examined whether the torsional response was excited by long-period design waves. A time-history response analysis was conducted assuming elastic response and considering geometric nonlinearities. It was confirmed that the torsional mode could be induced.

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EXPERIMENT AND SIMULATION OF Q-Δ RESONANCE USING FOUR-LAYER BENDING-SHEAR TYPE SPECIMEN

Kai R., Kohiyama M.

Proceedings of the 9th International Conference on Computational Methods in Structural Dynamics and Earthquake Engineering (COMPDYN 2023) 1 971 - 981 2023.06

Research paper (international conference proceedings), Joint Work, Last author, Accepted, ISSN 26233347

Torsional vibration of buildings subjected to earthquake motion has been studied for many years, but the torsional vibration of high-rise buildings has not yet been clarified. Many previous studies have focused on eccentricity, the mismatch between the center of gravity and the rigid center, as the cause of torsional vibration. However, in buildings with no eccentricity, torsional vibrations could be observed. The authors' research group focused on the “Q-Delta effect,” a phenomenon in which torsional torque is generated due to geometric nonlinearity and “Q-Delta resonance,” a phenomenon in which resonance occurs when the period of torsional torque generation coincides with the natural period of the torsional mode. In order to analyze the characteristics of Q-Delta resonance, a small-scale specimen was designed and fabricated from an 81-story high-rise building with biaxially symmetrical plans, which was reduced to four stories. As a result, the torsional response of the experimental and simulation results generally agreed, confirming that the torsional response increases when the Q-Delta resonance condition is satisfied. In addition, a comparison of the torsional response with a specimen with a smaller tower ratio and less bending deformation was conducted, and it was confirmed that the peak of the torsional response was steeper in the present study case.

Study on A Simplified Model of a High-Rise Shear-Type Building for Evaluation of Q–Δ Resonance

Anamizu R., Kohiyama M.

Journal of Structural and Construction Engineering (Architectural Institute of Japan) 88 ( 803 ) 59 - 70 2023.01

Research paper (scientific journal), Joint Work, Accepted, ISSN 13404202

To evaluate Q–Δ resonance, which is a torsional resonance phenomenon caused by geometric non-linearity, we proposed a simplified model for an n-story non-eccentric building model with a shear-type structure. We designed and fabricated a four-story small-scale specimen assuming a non-eccentric steel structure with a height of 324 m, and conducted shaking table tests. As a result, the occurrence of Q–Δ resonance was confirmed as predicted by the simplified model. In addition, the simplified model reproduced the resonance amplitude in a finite element model of the specimen with the out-of-plane rotation of slabs constrained and its validity was confirmed.

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Torsional behavior of a non-eccentric structure considering the effect of shear deformations in vertical members

Maki S., Kohiyama M.

Japan Architectural Review (Wiley) 5 ( 4 ) 404 - 431 2022.10

Research paper (scientific journal), Joint Work, Accepted, ISSN 2475-8876

Torsional moments can occur in a column owing to the geometric nonlinearity when the top of the column undergoes a large displacement in two horizontal directions. Periodically generated torsional moments can excite the torsional mode of a building owing to the internal resonance. For high-rise buildings, there is a risk that long-period seismic motions can cause a non-negligible torsional response due to this phenomenon. To investigate this risk, this study focuses on non-eccentric single-story and two-story shear-type structural models with an aggregate column, which is assumed to be an elastic Timoshenko beam. We derived the equations of motion for the models under horizontal ground motions and confirmed that the equations were identical to those of the models with an aggregate column of an elastic Euler beam. We then performed time-history response analyses of the models satisfying the internal resonance conditions under the input ground motions of harmonic waves, Gaussian white noise acceleration, acceleration records of previous earthquakes, and a long-period design wave. The results showed that the maximum horizontal acceleration at the corner of the top floor slab could be increased by more than 60% of the acceleration at the center of the slab in the worst case.

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Fundamental Study of Q–Δ Resonance Considering Higher-Order Modes by Shaking Table Experiments and Finite Element Analysis

Ryosuke ANAMIZU, Masayuki KOHIYAMA

Journal of Structural Engineering (Architectural Institute of Japan) 68B 386 - 396 2022.04

Research paper (scientific journal), Joint Work, Accepted, ISSN 0910-8033

The Q–Δ effect is a phenomenon that a column with different lateral stiffness generates a restoring torsional moment owing to geometric nonlinearity when its top displaces in two horizontal directions simultaneously. The Q–Δ resonance that is resonance of a torsional mode induced by the periodic torsional moment due to the Q–Δ effect could cause considerable torsional vibration even in non-eccentric buildings. However, the Q–Δ resonance in higher-order modes has not been investigated well. We first constructed a model of a two-story non-eccentricity structure and derived the conditions of the Q–Δ resonance considering higher-order modes. Then, we conducted shaking table experiments and finite element analysis to reproduce the experiments. As a result, it was confirmed that the second mode in the torsional direction was excited as predicted by the derived Q–Δ resonance condition. Moreover, the finite element analysis could reproduce the experimental result with high accuracy.

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Torsional response of a bisymmetric structure induced by bending–torsion interaction in vertical members

Kohiyama M., Yokoyama H., Maki S.

Japan Architectural Review (Wiley) 5 ( 1 ) 3 - 19 2022.01

Research paper (scientific journal), Joint Work, Lead author, Last author, Corresponding author, Accepted

The biaxially symmetric column induces torsional moment owing to the bending–torsion interaction when the top of the column is given horizontal displacement in the two directions of the principal axes of the cross section and the rotation of the column ends around the axes is constrained. Hence, horizontal ground motions in two directions can cause non-negligible torsional vibration in a high-rise building owing to autoparametric resonance. To investigate this phenomenon, in this study, we focus on a single-story structure with an aggregated column, which is a model for columns or core structures in tall buildings and is assumed to be an elastic Euler beam; subsequently we derive the equations of motion under horizontal ground motions in two directions. The resonance condition in the torsional direction is also investigated. Time-series response analyses are performed for input ground motions of harmonic waves, white noise, acceleration records of prior earthquakes, and a long-period design wave, and the relationship between the ratio of two translational natural frequencies and the increase in maximum responses is explained. The results demonstrate that the absolute acceleration response to the long-period design wave could increase by more than 50% in the worst-case scenario.

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火山災害対応における認知バイアスを克服するための訓練ツール

Yuzo KONNO, Honoka TAKASHIMA, Masayuki KOHIYAMA, Mitsuhiro YOSHIMOTO, Tomohiro KUBO

Journal of Social Safety Science (Institute of Social Safety Science) ( 39 ) 233 - 243 2021.11

Research paper (scientific journal), Joint Work, Accepted, ISSN 1345-2088

In recent disasters, there have been many cases that people failed to escape despite evacuation advisories or did not make appropriate preparations in advance. One of the possible causes is the influence of cognitive bias. However, countermeasures against cognitive bias have not been developed in community disaster management. In this study, we developed a training tool in the form of web application software to overcome cognitive bias by acquiring knowledge about possible cognitive bias in response to volcanic disasters and discussing countermeasures with community members. The effectiveness of the tool was verified in workshops held in Fujiyoshida City. This training tool allows residents to conduct training on their own using a video conferencing system.

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Experiment of torsional response induced by the Q–delta resonance

Mizutori F., Kohiyama M.

The Structural Design of Tall and Special Buildings (Wiley) 30 ( 4 ) 2021.03

Research paper (scientific journal), Joint Work, Accepted, ISSN 15417794

Because of geometric nonlinearities, vibrations in two horizontal directions can generate a torsional moment that induces a torsional response, even in a structure with no eccentricity; we term this phenomenon Q–Δ effect. In this study, the torsional response caused by the Q–Δ resonance was investigated by performing a shaking table test that involves a single-layer symmetric specimen. The specimen was designed and fabricated by focusing on one of two resonance conditions. Its moment of inertia was adjustable by changing locations of weights, and the natural frequency in the torsional mode could be modified. We developed a numerical model of the specimen, in which the columns connected to the slab were integrated into an elastic Euler beam. It was confirmed that the torsional response increased near the predicted Q–Δ resonance point. In addition, the acceleration at the corner of the slab was significantly increased. The formulated equations of motion provided a better prediction of the actual phenomena. Because the specimen corresponds to a stiff and slender high-rise building when converted into a full scale, the result suggests a need to be aware of the risk of torsional response increase due to the Q–Δ resonance.

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Deep neural network for detecting earthquake damage to brace members installed in a steel frame

Yamashita, T., Kohiyama, M., Oka, K.

Japan Architectural Review (Wiley) 4 ( 1 ) 56 - 64 2021.01

Research paper (scientific journal), Joint Work, Accepted

We are developing an artificial intelligence system for structural health monitoring that can detect local damage in a building structure by using the E‐Simulator numerical simulation system that is being developed by the Japanese National Research Institute for Earth Science and Disaster Resilience. In this study, we confirmed the applicability of a multiclass classifier using a deep neural network to address the problem of identifying damage patterns in braces installed in a steel frame. Experimental data obtained from shaking table tests were used for training and testing. Cross-validation tests were conducted for several cases with different numbers of sensors, sensor degrees of freedom, and nodes in the hidden layers of the network. The results demonstrated that the accuracy of the damage pattern detection from the constructed classifier exceeded 77% when the appropriate hidden layers were selected and reached 87.9% for the best case.

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Volcanic Hazard Map of Mt. Fuji Using Augmented Reality

Masayuki KOHIYAMA, Tatsumi AKAHORI, Mitsuhiro YOSHIMOTO, Tomohiro KUBO

Journal of Social Safety Science (Institute of Social Safety Science) ( 37 ) 147 - 155 2020.11

Research paper (scientific journal), Joint Work, Accepted, ISSN 1345-2088

The volcanic hazard map is one of the effective proactive measures for volcanic disasters. However, the general public has little interest in the hazard maps because it is not easy to understand them. To address this problem, this study develops a volcanic hazard map using augmented reality (AR). The effectiveness of the proposed AR hazard map was verified through the workshops held in Fujiyoshida City, in which participants actually used the prototype map. As a result, it was clarified that the AR hazard map could effectively supplement a printed hazard map and it was also found that the display switching function of the hazards was particularly effective for promoting understanding.

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A Framework of Disaster Imagination Game by Residents for Developing Next-Generation Leaders for Volcanic Disaster Reduction

Honoka TAKASHIMA, Masayuki KOHIYAMA, Mitsuhiro YOSHIMOTO, Tomohiro KUBO

Journal of Social Safety Science (Institute of Social Safety Science) ( 37 ) 175 - 185 2020.11

Research paper (scientific journal), Joint Work, Accepted, ISSN 1345-2088

In order to reduce volcanic disasters, it is important for residents to practice mutual assistance, which can be led by a voluntary disaster reduction group. However, many communities lack the human resources for their future leaders. To address this issue, we created a framework of disaster imagination game to develop next-generation leaders. The effectiveness of the game was verified through workshops held in Fujiyoshida City. The framework allows residents to train independently by using a web application software.

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Detection method of unlearned pattern using support vector machine in damage classification based on deep neural network

Kohiyama M., Oka K., Yamashita T.

Structural Control and Health Monitoring (Wiley) 27 ( 8 ) 2020.08

Research paper (scientific journal), Joint Work, Lead author, Last author, Corresponding author, Accepted, ISSN 15452255

Deep neural networks (DNNs) are a powerful tool for structural health monitoring because they can automatically identify features that are useful for classifying and recognizing damage patterns of a target structure with high accuracy. However, it can misclassify input data of an unlearned damage pattern as any of the learned damage patterns. To address this shortcoming, this paper presents a method to detect unlearned damage patterns by using the collective decision of support vector machines (SVMs). SVMs are constructed using feature vectors from training data, which are stored in the output layer of a DNN. To validate the proposed method, we used two different datasets, one containing experimental data of a steel frame structure and the other containing simulated and experimental data of a wooden house. In both cases, it correctly identified data of both learned and unlearned damage patterns. The proposed method can enhance the effectiveness of structural health monitoring (SHM). In addition, because it does not employ SHM-specific characteristics, it can be used in various pattern recognition applications, such as image and audio processing.

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Seismic Response Control of an Instrument With Casters Using Computer Vision Based on Reinforced Learning of Dynamic Analysis Results Considering Friction

Kento WAKABAYASHI, Yosuke KAWANISHI, Masayuki KOHIYAMA, Ryo EGUCHI, and Masaki TAKAHASHI

Journal of Structural Engineering (Architectural Institute of Japan) 66B 305 - 314 2020.03

Research paper (scientific journal), Joint Work, Accepted, ISSN 0910-8033

We developed a computer vision system to control the seismic response of medical equipment with casters in a building subjected to earthquake excitation. The controller employs a neural network and the reinforced learning was conducted based on dynamic simulations of a controlled equipment under building floor motion. In the dynamic simulations, we used Open Dynamics Engine, which is a simulator library of three-dimensional rigid body dynamics, and we introduced a friction model to simulate the dynamic behavior of the casters. To validate the proposed system, we conducted shaking table tests and it was confirmed that the proposed control could successfully suppress the response of the equipment.

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