Partial Least Squares Regression Approach in the Analysis of Damage Intensity Changes to Prefabricated RC Buildings during the Long Term of Mining Activity

Abstract

This article presents the results of the analysis of the extent of damage to 138 multi-storey buildings with reinforced concrete prefabricated structure, which are located in the mining terrain of the Legnica-Głogów Copper District. These objects are residential and public utility buildings of up to 43 years old, erected in industrialized prefabricated technologies: large-block and large-panel systems. The research was based on the results of technical condition inventory carried out in 2002, 2007 and 2012. As part of the analysis, the damage intensity index wu was established for individual structural and finishing elements of the studied buildings. This index is defined on a six-point scale, which includes a detailed description of the extent of damage that corresponds to the successive degrees of intensity. As part of the research, the databases were significantly expanded and the generalized formulas of the damage intensity index wu for individual groups of buildings were verified. For this purpose, the partial least squares regression (PLSR) method was applied. Thereafter, the analysis of changes of this intensity in time was carried out and the relations between the extent of damage and the impacts of mining exploitation were examined. The approach presented in this paper and obtained research results are characterized by a high degree of utilitarianism and can be applied to increase the efficiency in the optimal maintenance management of buildings, including planning of repairs and retrofits throughout the technical life cycle of the buildings.

Convolutional Neural Network and Support Vector Machine for Prediction of Damage Intensity to Multi-Storey Prefabricated RC Buildings

“…with the idea of sustainable development. The basic assumption of this doctrine is to achieve social sustainability by increasing the economic and environmental efficiency of construction projects [23][24][25], increasingly using Artificial Intelligence and Machine Learning [26,27]. All of this creates a need to use more accurate tools for predicting building damage and to implement ML methods in research that have not been previously used for such issues.…”

“…Based on the damage intensity indices of particular building elements (w ui ) and the formulas determined in the published paper [25], the values of the damage intensity index (w u ) for buildings were determined. This formula has the general form…”

“…where a i -Directional coefficients of the linear combination of components occurring at individual damage indices determined by the PLSR method [25], w ui -The value of indicators of damage intensity of individual building elements.…”

“…Analyses of the extent and intensity of damage caused to multi-storey RC buildings in conjunction with the considered socio-economic and environmental aspects fit well with the idea of sustainable development. The basic assumption of this doctrine is to achieve social sustainability by increasing the economic and environmental efficiency of construction projects [23][24][25], increasingly using Artificial Intelligence and Machine Learning [26,27].…”

Prediction of Soil Shear Strength Parameters Using Combined Data and Different Machine Learning Models

“…where N is the sample size; p is the number of PCFs; RSS is the sum of squared residuals for prediction with the PLSR model. The PLSR method assumes that the variables from the input space X can be written as a linear combination of vectors t and e (namely scores) and that the variables from the output space Y can be written as a linear combination of q and u (namely loadings) [48]; therefore, these relationships are as follows [49]:…”