Edisson Alberto Moscoso Alcantara

Bio: Edisson Alberto Moscoso Alcantara is an academic researcher from Toyohashi University of Technology. The author has contributed to research in topics: Computer science & Structural engineering. The author has co-authored 1 publications.

Structural Response Prediction for Damage Identification Using Wavelet Spectra in Convolutional Neural Network.

Abstract: If damage to a building caused by an earthquake is not detected immediately, the opportunity to decide on quick action, such as evacuating the building, is lost. For this reason, it is necessary to develop modern technologies that can quickly obtain the structural safety condition of buildings after an earthquake in order to resume economic and social activities and mitigate future damage by aftershocks. A methodology for the prediction of damage identification is proposed in this study. Using the wavelet spectrum of the absolute acceleration record measured by a single accelerometer located on the upper floor of a building as input data, a CNN model is trained to predict the damage information of the building. The maximum ductility factor, inter-story drift ratio, and maximum response acceleration of each floor are predicted as the damage information, and their accuracy is verified by comparing with the results of seismic response analysis using actual earthquakes. Finally, when an earthquake occurs, the proposed methodology enables immediate action by revealing the damage status of the building from the accelerometer observation records.

Convolutional Neural Network-Based Rapid Post-Earthquake Structural Damage Detection: Case Study

Abstract: It is necessary to detect the structural damage condition of essential buildings immediately after an earthquake to identify safe structures, evacuate, or resume crucial activities. For this reason, a CNN methodology proposed to detect the structural damage condition of a building is here improved and validated for two currently instrumented essential buildings (Tahara City Hall and Toyohashi Fire Station). Three-dimensional frames instead of lumped mass models are used for the buildings. Besides this, a methodology to select records is introduced to reduce the variability of the structural responses. The maximum inter-storey drift and absolute acceleration of each storey are used as damage indicators. The accuracy is evaluated by the usability of the building, total damage condition, storey damage condition, and total comparison of the damage indicators. Finally, the maximum accuracy and R2 of the responses are obtained as follows: for the Tahara City Hall building, 90.0% and 0.825, respectively; for the Toyohashi Fire Station building, 100% and 0.909, respectively.

Machine Learning-Based Rapid Post-Earthquake Damage Detection of RC Resisting-Moment Frame Buildings

Abstract: This study proposes a methodology to predict the damage condition of Reinforced Concrete (RC) resisting-moment frame buildings using Machine Learning (ML) methods. Structural members of six hundred RC buildings with varying stories and spans in X and Y directions were designed using the virtual work method. Sixty thousand time-history analyses using ten spectrum-matched earthquake records and ten scaling factors were carried out to cover the structures’ elastic and inelastic behavior. The buildings and earthquake records were split randomly into training data and testing data to predict the damage condition of new ones. In order to reduce bias, the random selection of buildings and earthquake records was carried out several times, and the mean and standard deviation of the accuracy were obtained. Moreover, 27 Intensity Measures (IM) based on acceleration, velocity, or displacement from the ground and roof sensor responses were used to capture the building’s behavior features. The ML methods used IMs, the number of stories, and the number of spans in X and Y directions as input data and the maximum inter-story drift ratio as output data. Finally, seven Machine Learning (ML) methods were trained to predict the damage condition of buildings, finding the best set of training buildings, IMs, and ML methods for the highest prediction accuracy.

Convolutional Neural Network-Based Rapid Post-Earthquake Structural Damage Detection: Case Study

Abstract: It is necessary to detect the structural damage condition of essential buildings immediately after an earthquake to identify safe structures, evacuate, or resume crucial activities. For this reason, a CNN methodology proposed to detect the structural damage condition of a building is here improved and validated for two currently instrumented essential buildings (Tahara City Hall and Toyohashi Fire Station). Three-dimensional frames instead of lumped mass models are used for the buildings. Besides this, a methodology to select records is introduced to reduce the variability of the structural responses. The maximum inter-storey drift and absolute acceleration of each storey are used as damage indicators. The accuracy is evaluated by the usability of the building, total damage condition, storey damage condition, and total comparison of the damage indicators. Finally, the maximum accuracy and R2 of the responses are obtained as follows: for the Tahara City Hall building, 90.0% and 0.825, respectively; for the Toyohashi Fire Station building, 100% and 0.909, respectively.

Machine Learning-Based Rapid Post-Earthquake Damage Detection of RC Resisting-Moment Frame Buildings

Abstract: This study proposes a methodology to predict the damage condition of Reinforced Concrete (RC) resisting-moment frame buildings using Machine Learning (ML) methods. Structural members of six hundred RC buildings with varying stories and spans in X and Y directions were designed using the virtual work method. Sixty thousand time-history analyses using ten spectrum-matched earthquake records and ten scaling factors were carried out to cover the structures’ elastic and inelastic behavior. The buildings and earthquake records were split randomly into training data and testing data to predict the damage condition of new ones. In order to reduce bias, the random selection of buildings and earthquake records was carried out several times, and the mean and standard deviation of the accuracy were obtained. Moreover, 27 Intensity Measures (IM) based on acceleration, velocity, or displacement from the ground and roof sensor responses were used to capture the building’s behavior features. The ML methods used IMs, the number of stories, and the number of spans in X and Y directions as input data and the maximum inter-story drift ratio as output data. Finally, seven Machine Learning (ML) methods were trained to predict the damage condition of buildings, finding the best set of training buildings, IMs, and ML methods for the highest prediction accuracy.

Structural Damage Condition of Buildings with a Sparse Number of Sensors Using Machine Learning: Case Study

Abstract: The optimum location of signal sensors in actual buildings to determine the structural damage condition using machine learning is discussed in this study. The target buildings are a local government office and a Fire Station in Japan, with two acceleration sensors located on the ground and the roof level of the buildings. An additional sensor location is considered in this study. The structural damage condition is evaluated by machine learning (ML) methods from the sensor signals for five cases of single and multiple sensor locations. The maximum story drift is used as an identifier of the structural damage condition. Seven ML methods are developed, and their accuracy is compared. Several intensity measures (IM) obtained from each sensor signal are used as input features for the ML models, and the prediction importance level of each IM is evaluated in order to establish its usefulness. Finally, the results are compared to the methodology using wavelet power spectrum and convolutional neural network to predict the damage condition of buildings.