Analysis Study of Failure Structural and Preventions before Constructions

Abstract

The construction industry is intrinsically related to the problem of structural breakdowns that have been occurring recently. The accidental and violent destruction of a building component or a component of it, as well as the structural components of scaffolding, forming elements, sheet piling, and excavation linings, constitute a structural failure. This type of failure can be defined as an example of a structural failure. Failures in structural integrity invariably result in monetary and environmental losses, both of which pose significant challenges for businesses. The analysis of the structural failures not only makes it possible to identify the factors that contributed to the failures, but it also makes it possible to provide solutions that will help prevent or reduce the likelihood that similar failures will occur in the future. It is a very significant component of an organization's overall business process management not just because of the potential threats to human life, the environment, and corporate responsibility, but also because of the potential costs to the firm financially. In a great number of research paper, artificial neural networks (ANNs) have been used to investigate various elements of RC buildings. They have mostly concentrated on the aftershock effect that earthquakes have on RC constructions. During the process of optimization, a trained artificial neural network (ANN) uses the information that is generated from a number of characteristics specified as design vectors to carry out both deterministic and probabilistic constraint tests. The trained NN is then employed to forecast the structure response in terms of probabilistic and deterministic constraint test owing to varied design factors. This is done by applying the learned NN. These values are the result of a number of different phenomena, including basic periods, base bending moments, base shear force, and top-floor displacement of buildings in two different directions. Therefore, it is necessary to make predictions regarding these factors.