Earthquake Response Reduction of Building Structures Using Learning-Based Lattice Pattern Active Controller

Abstract

An active vibration control technique for building structures using a learning-based lattice pattern controller (LBLPC) is proposed in this article. The training pattern of the LBLPC is composed of a lattice form for the control force and a state vector. The training pattern was trained by a learning rule using the gradient descent method (GDM) in earthquakes. The LBLPC calculates the control force using only the adjacent input information, thus making the corresponding calculation process much faster. A three-story building in the El Centro earthquake was used to train the LBLPC. And the California and Northridge earthquakes were used to verify the performance of the proposed method. In order to prove the control capability of the LBLPC, the control results of the LBLPC were compared with those of a lattice type probabilistic neural network (LPNN) in a numerical example. The results demonstrated that the proposed LBLPC algorithm reduces the response of the building structure during earthquakes more effectively than the LPNN.

Optimization of Two Soil–Structure Interaction Parameters Using Dynamic Centrifuge Tests and an Analytical Approach

“…A time-domain analysis by the State Space Equation (SSE) has been used for the estimation of the dynamic structural response or system identification [11,25,26], and the analytical approach in this study was implemented using MATLAB R2014b. Equation ( 1) can be expressed as Equation ( 7) and the equation was converted to continuous-time SSE (Equation ( 8)), wherein Y in {X} consists of u s , u x , and φ.…”

H∞ control method for seismically excited building structures with time-delay

“…Unlike traditional control systems, the active control method proposed by these researchers has two degrees of freedom. A learning-based lattice pattern controller (LBLPC) was introduced by Chang et al (2012) for suppressing the seismic vibrations of building structures. For the training pattern of this controller, Chang and colleagues considered a state vector and a lattice form for the control force.…”