Determination of Mass Properties in Floor Slabs from the Dynamic Response Using Artificial Neural Networks

Abstract

Most of the research on accidental eccentricity is directed at both the evaluation of accidental eccentricity design code recommendations and the study of building torsional response. In contrast, this paper addresses how the mass properties of each of the levels of a building could be determined from the dynamic response of a building. Using the dynamic response of buildings, this paper presents the application of multilayer feed forward artificial neural networks (ANNs) to determine the magnitude, the radial distance, and the polar moment of inertia of the mass for each level of reinforced concrete (RC) buildings. Analytical models were developed for three regular buildings. Live-load magnitude and mass position are considered as random variables. Seven load cases were generated for the 1, 2 and 4-story models using two excitations. As for the input parameters of the ANNs, three different choices of input data to the network were used. The developed ANN models are able to predict with adequate accuracy the radial position, magnitude, and polar moment of inertia of masses of each level. The implementation of this method based on ANNs would allow the monitoring, either permanently or temporarily, of changes in mass properties at each building floor slab. Doi: 10.28991/CEJ-2022-08-08-01 Full Text: PDF

Prediction of the Dynamic Properties of Concrete Using Artificial Neural Networks

“…These forecasts guide maintenance and repair efforts [26]. ANNs can help with concrete structure design optimization, such as identifying the best reinforcement scheme, slab thickness, or column dimensions for load-bearing capacity, safety, and cost-effectiveness [27,28]. It is also used to study and forecast the complicated stress-strain behavior of restricted concrete columns under varied loading circumstances.…”