Earthquake Damage Reduction in Timber Frame Houses Using Small-Size Fluid Damper

Timber frame structures are common traditional methods of housing construction, which use squared-off timber beams, columns, and walls as lateral load-bearing members. The seismic performance of timber frame houses can be secured by the load-bearing capacity of erected braces and walls; however, past major earthquakes have caused severe damage to earthquake-resistant timber frame houses. This study investigates the effect of small-size fluid dampers on the earthquake damage reduction in a timber frame house through earthquake response analyses. A detailed analytical model was generated based on an actual two-story timber frame house, which was designed for the highest seismic grade using the latest Japanese standards. Time-history response analyses were carried out for the analytical model subjected to the 2016 Kumamoto earthquake with and without small-size fluid dampers. The small-size fluid damper is equipped with a relief mechanism for the damping force, and its damping property can be expressed using the Maxwell model. Four or seven fluid dampers were installed in the first story of the model to investigate their effect on the earthquake damage reduction. The results of the earthquake response analyses show that the four and seven fluid dampers can reduce the maximum first-story drift angle by approximately one-third and half, respectively. The dampers suppress the residual deformation, control the elongation of the fundamental period during the response, and restrain the amplitude growth. A small-size fluid damper has an equivalent quake resistance to a conventional structural wall with a wall ratio of 3 plus.

Prediction Model of Magnetorheological (MR) Fluid Damper Hysteresis Loop using Extreme Learning Machine Algorithm

Magnetorheological (MR) fluid is among the smart materials that can change its default properties with the influence of a magnetic field. Typical application of an MR fluid based device involves an adjustable damper which is commercially known as an MR fluid damper. It is used in vibration control as an isolator in vehicles and civil engineering applications. As part of the device development process, proper understanding of the device properties is essential for reliable device performance analysis. This study introduce an accurate and fast prediction model to analyse the dynamic characteristics of the MR fluid damper. This study proposes a new modelling technique called Extreme Learning Machine (ELM) to predict the dynamic behaviour of an MR fluid damper hysteresis loop. This technique was adopted to overcome the limitations of the existing models using Artificial Neural Networks (ANNs). The results indicate that the ELM is extremely faster than ANN, with the capability to produce high accuracy prediction performance. Here, the hysteresis loop, which represents the relationship of force-displacement for the MR fluid damper, was modelled and compared using three different activation functions, namely, sine, sigmoid and hard limit. Based on the results, it was found that the prediction performance of ELM model using the sigmoid activation functions produced highest accuracy, and the lowest Root Mean Square Error (RMSE).