Predicting Dynamic Response of Structures under Earthquake Loads Using Logical Analysis of Data

Abstract

Abstract:In this paper, logical analysis of data (LAD) is used to predict the seismic response of building structures employing the captured dynamic responses. In order to prepare the data, computational simulations using a single degree of freedom (SDOF) building model under different ground motion records are carried out. The selected excitation records are real and of different peak ground accelerations (PGA). The sensitivity of the seismic response in terms of displacements of floors to the variation in earthquake characteristics, such as soil class, characteristic period, and time step of records, peak ground displacement, and peak ground velocity, have also been considered. The dynamic equation of motion describing the building model and the applied earthquake load are presented and solved incrementally using the Runge-Kutta method. LAD then finds the characteristic patterns which lead to forecast the seismic response of building structures. The accuracy of LAD is compared to that of an artificial neural network (ANN), since the latter is the most known machine learning technique. Based on the conducted study, the proposed LAD model has been proven to be an efficient technique to learn, simulate, and blindly predict the dynamic response behaviour of building structures subjected to earthquake loads.

Data‐driven optimal predictive control of seismic induced vibrations in frame structures

“…In machine learning there are several techniques that can be used for this scope, as for example Neural Networks, Regression Trees (RT), Random Forests, Gaussian Processes, and others. Indeed many interesting results present in the literature exploit the aforementioned approaches in order to perform predictions on the response of a structure subject to an earthquake load, early warnings and damage estimation due to reduced stiffness …”

“…Indeed many interesting results present in the literature exploit the aforementioned approaches in order to perform predictions on the response of a structure subject to an earthquake load, early warnings and damage estimation due to reduced stiffness. [34][35][36][37][38][39] However, the existing techniques do not provide models that are directly suitable for structural control, and in particular for applying MPC. For this reason, a recent very active research line in the literature tackles the problem of combining Machine Learning and MPC, and demonstrates its effectiveness in different application domains, e.g.…”

The Prediction of Earthquake Building Structure Strength: Modified K-Nearest Neighbour Employment

“…Thus, it terminates the socio-economic activities of a region within a short period. Due to the earthquake's effect, the consideration of structure configuration, the type of material used, and the structural building system are substantial [18]. Magnitude earthquake loads on building structures depend on the horizontal force, vertical force, torque earthquake moment in the structures, weight, and stiffness of the structural material, configuration and structural system, vibration time, ground conditions, earthquake zones, and earthquake behavior.…”

“…In the case of a prestressed concrete bridge subject to earthquakes, Pei and Smyth [28] have been successfully investigated a feedforward neural network. Abd-elhamed et al [18] proposed a Logical analysis of data (LAD) to simulate and blindly predict the dynamic response behavior of building structures against the earthquake loads. Nevertheless, the number of input variables of the above studies is still relatively small and has been pre-defined based on domain knowledge.…”

Feasibility of Using Neural Networks to Obtain Simplified Capacity Curves for Seismic Assessment

“…In the last decades, several studies have been published for structural behaviour evaluation using neural networks, namely for earthquake engineering applications. ANNs have been used to predict the linear [7] and the nonlinear [8,9] dynamic responses of structures subject to earthquakes for damage assessment [10][11][12][13], namely using fragility curves [14,15], or for seismic reliability assessment [16,17]. A Monte Carlo simulation technique was also adopted for generating data used for training ANNs [18].…”