This study aims to investigate the seismic response of a large span thin shell structures and assess their displacement under seismic loads. The study employs finite element analysis to model a thin shell structure subjected to seismic excitation. The analysis includes eigenvalue analysis and time history analysis to evaluate the natural frequencies and displacement response of the structure under seismic loads. The findings show that the seismic response of the large span thin shell structure is highly dependent on the frequency content of the seismic excitation. The eigenvalue analysis reveals that the tenth mode of vibration of the structure corresponds to a large-span mode. The time history analysis further demonstrates, with 5% damping, that the displacement response of the structure at the critical node number 4920 increases with increasing seismic intensity, reaching a maximum displacement of 49.87mm at 3.615 seconds. Nevertheless, the maximum displacement is well below the allowable limit of the thin shell. The results of this study provide insight into the behaviour of complex large span thin shell structures as elevated foundations for buildings under seismic excitation, based on the displacement contours on different modes of eigenvalues. The findings suggest that the displacement response of the structure is significant for this new application of thin shell, and it is recommended to enhance the critical displacement area in the next design phase to align with the findings of this study to resist the seismic impact.
Phayathonzu temple in Myanmar was made of masonry bricks, and so it was vulnerable to lateral load such as earthquake. Especially, it has many difficulties in structural modeling and dynamic analysis because the discontinuous characteristics of masonry structure should be considered. So, it is necessary to provide the seismic performance evaluation technology through the inelastic dynamic modeling and analysis under earthquake loads for the safety security of masonry brick temple. Therefore, this study analyzes the seismic behavior characteristics and evaluates the seismic performance for the 479 structure with many cracks and deformations. Through the evaluation results, we found out the structural weak parts on earthquake loads.
The cultural heritage of fortresses is often exposed to external elements, leading to significant damage from stone weathering and natural disasters. However, due to the nature of cultural heritage, dismantling and restoration are often impractical. Therefore, the stability of fortress cultural heritage was evaluated through non-destructive testing. The durability of masonry cultural heritages is greatly influenced by the physical characteristics of the back-fille material. Dynamic characteristics were assessed, and endoscopy was used to inspect internal fillings. Additionally, a finite element analysis model was developed considering the surrounding ground through elastic wave exploration. The analysis showed that the loss of internal fillings in the target cultural heritage site could lead to further deformation in the future, emphasizing the need for careful observation.
In this study, we propose an optimal design method by applying the Prefabricated Buckling Restrained Brace (PF-BRB) to structures with asymmetrically rigidity plan. As a result of the PF-BRB optimal design of a structure with an asymmetrically rigidity plan, it can be seen that the reduction effect of dynamic response is greater in the case of arrangement considering the asymmetric distribution of stiffness (Asym) than in the case of arrangement in the form of a symmetric distribution (Sym), especially It was confirmed that at an eccentricity rate of 20%, the total amount of reinforced PF-BRBs was also small. As a result of analyzing the dynamic response characteristics according to the change in eccentricity of the asymmetrically rigidity plan, the distribution of the reinforced PF-BRB showed that the larger the eccentricity, the greater the amount of damper distribution around the eccentric position. Additionally, when comparing the analysis models with an eccentricity rate of 20% and an eccentricity rate of 12%, the response reduction ratio of the 20% eccentricity rate was found to be large.
Since atypical high-rise buildings are vulnerable to gravity loads and seismic loads, various structural systems must be applied to ensure the stability of the structure. In this study, the authors selected a 60-story twisted-shaped structure among atypical high-rise structures as an analytical model to investigate its structural behavior concerning the outrigger system. The structural analyses were performed varying the number of installed layers and the arrangement of the outrigger system, as well as the placement of the mega column, as design variables. The analysis revealed that the most effective position for the outrigger was 0.455H from the top layer, consistent with previous studies. Additionally, connecting outriggers and mega columns significantly reduced the displacement response of the model. From an economic standpoint, it is deemed efficient to connect and install outriggers and mega columns at the structure's ends.
Machine learning is widely applied to various engineering fields. In structural engineering area, machine learning is generally used to predict structural responses of building structures. The aging deterioration of reinforced concrete structure affects its structural behavior. Therefore, the aging deterioration of R.C. structure should be consider to exactly predict seismic responses of the structure. In this study, the machine learning based seismic response prediction model was developed. To this end, four machine learning algorithms were employed and prediction performance of each algorithm was compared. A 3-story coupled shear wall structure was selected as an example structure for numerical simulation. Artificial ground motions were generated based on domestic site characteristics. Elastic modulus, damping ratio and density were changed to considering concrete degradation due to chloride penetration and carbonation, etc. Various intensity measures were used input parameters of the training database. Performance evaluation was performed using metrics like root mean square error, mean square error, mean absolute error, and coefficient of determination. The optimization of hyperparameters was achieved through k-fold cross-validation and grid search techniques. The analysis results show that neural networks and extreme gradient boosting algorithms present good prediction performance.
In this paper, machine learning models were applied to predict the seismic response of steel frame structures. Both geometric and material nonlinearities were considered in the structural analysis, and nonlinear inelastic dynamic analysis was performed. The ground acceleration response of the El Centro earthquake was applied to obtain the displacement of the top floor, which was used as the dataset for the machine learning methods. Learning was performed using two methods: Decision Tree and Random Forest, and their efficiency was demonstrated through application to 2-story and 6-story 3-D steel frame structure examples.
In this study, the characteristics of wind pressure distribution on circular retractable dome roofs with a low rise-to-span ratio were analyzed under various approaching flow conditions by obtaining and analyzing wind pressures under three different turbulent boundary layers. Compared to the results of previous studies with a rise-to-span ratio of 0.1, it was confirmed that a lower rise-to-span ratio increases the reattachment length of the separated approaching flow, thereby increasing the influence of negative pressure. Additionally, it was found that wind pressures varied significantly according to the characteristics of the turbulence intensity. Based on these experimental results, a model for peak net pressure coefficients for cladding design was proposed, considering variations in turbulence intensity and height.