Predictive Model for Estimating the Weight of Existing RC Buildings Using Easily Accessible Structural Parameters

Abstract

The weight of existing buildings is a critical parameter in various structural engineering applications, including seismic assessment, uneven settlement evaluation, structural vibration control, building relocation, and demolition operations. While current practice typically estimates this value by multiplying floor area multiplied by an empirical unit weight coefficient. This approach faces limitations when the original design details are unavailable, making total floor area difficult to determine. To address this challenge, this study develops predictive models for estimating the weight of existing reinforce concrete (RC) buildings using easily accessible structural parameters, such as structural height, plan dimensions, number of stories, and fundamental period. A database comprising the weights and related design parameters of 732 RC buildings was developed through an extensive literature search. The maximum information coefficient and Kruskal–Wallis analysis of variance were used to identify factors that significantly influence building weight. Subsequently, regression formulas for building weight, incorporating structural height, plan dimensions of a standard floor, fundamental period, and structural type were established. These prediction formulas were applied to five building examples, and the results were compared with actual values. The comparison shows that the weight prediction formulas have good accuracy and can be used in state assessment of existing buildings and parametric modeling in disaster prevention analysis of urban buildings. Finally, the predictive models have been deployed on an online web page for the convenience of users.