A study on improving energy flexibility in building engineering through generalized prediction models: Enhancing local bearing capacity of concrete for engineering structures
Document Type
Article
Publication Date
3-1-2024
Abstract
Load-bearing in structural engineering involves a structure's ability to support and distribute weight effectively. This research investigates innovative methods to enhance load-bearing capabilities while optimizing energy flexibility within structural systems. Accurately predicting the local bearing capacity of concrete is not only vital for ensuring structural stability in building engineering, especially in anchorage zones, but also for promoting environmental sustainability through optimized material use. Existing prediction models, primarily designed for ordinary-strength concrete, often overlook the nuanced influence of concrete strength and ducts. This oversight can lead to substantial inaccuracies when these models are applied to high-strength and ultra-high-strength concrete. To holistically address these challenges, this study introduces generalized prediction models that factor in crucial elements such as concrete strength, local area aspect ratio, and ducts. The results show that the Mean of the GB50010-2010 model, CECS104:99 model, and ACI318-19 model ranged from 0.845 to 0.937, which might overestimate the experimental data with high variation, while the AASHTO model might underestimate the local bearing capacity of concrete, with a mean value of 1.045. The SD, MAPE, RMSE, IAE, R2, and alpha 20 index were approximately within the range of 0.12-0.19, 0.14-0.24, 227-373, 2.4-3.4%, 0.7-0.9, 0.6-0.9 for the existing models, and 0.11-0.13, 0.09-0.1, 176-178 1.95-1.96%, 0.93-0.94, 0.90-0.91 for FA model and ANN models. This indicated that the proposed FA model and ANN model outperformed all the existing normative models used for concrete local bearing capacity.
Keywords
Concrete, Local bearing capacity, Prediction model, Artificial neural network (ANN), Fitting analysis (FA)
Divisions
sch_civ
Funders
Prince Sattam Bin Abdulaziz University (PSAU/2023/R/1445),King Khalid University King Saud University
Publication Title
Engineering Structures
Volume
303
Publisher
Elsevier
Publisher Location
125 London Wall, London, ENGLAND