Concrete compressive strength analysis using a combined classification and regression technique

NCUIR > School of Management at National Central University > Department of Information Management > journal & Dissertation > Item 987654321/106513

Please use this identifier to cite or link to this item: https://ir.lib.ncu.edu.tw/handle/987654321/106513

Title:	Concrete compressive strength analysis using a combined classification and regression technique
Authors:	蔡志豐;Chou, Jui-Sheng;Tsai, Chih-Fong
Contributors:	管理學院資訊管理學系
Keywords:	Applied sciences;Buildings. Public works;Classification;Compressive strength;Concretes;Concretes. Mortars. Grouts;Exact sciences and technology;Hierarchical data mining;High performance concrete;Materials;Mathematical analysis;Mathematical models;Other special applications (sand concrete, roller compacted concrete, heavy concrete, architectural concrete, etc.);Regression;Regression analysis;Strength of materials (elasticity, plasticity, buckling, etc.);Structural analysis. Stresses;Vectors (mathematics)
Date:	2012-01-01
Issue Date:	2026-04-23 13:25:46 (UTC+8)
Publisher:	Elsevier;Kidlington: Elsevier B.V
Abstract:	摘要： High performance concrete (HPC) is a complex composite material, and a model of its compressive strength must be highly nonlinear. Many studies have tried to develop accurate and effective predictive models for HPC compressive strength, including linear regression (LR), artificial neural networks (ANNs), and support vector regression (SVR). Nevertheless, in accordance with recent reports that a hierarchical structure outperforms a flat one, this study proposes a hierarchical classification and regression (HCR) approach for improving performance in predicting HPC compressive strength. Specifically, the first-level analyses of the HCR find exact classes for new unknown cases. The cases are then entered into the corresponding prediction model to obtain the final output. The analytical results for a laboratory dataset show that the HCR approach outperforms conventional flat prediction models (LR, ANNs, and SVR). Notably, the HCR with a 4-class support vector machine in the first level combined with a single ANNs obtains the lowest mean absolute percentage error. ► Concrete compressive strength (CCS) is highly nonlinear ► This study proposes a hierarchical artificial intelligence for predicting CCS ► The analytical results show that the hybrid approach outperforms conventional flat prediction models ► The approach automates concrete mix design for compressive strength in civil construction. 出版者： Kidlington: Elsevier B.V 出版日期： 2012-07-01 出處： Automation in Construction, 2012-07, Vol.24, p.52-60 資源來源： Elsevier ScienceDirect Journals Complete - AutoHoldings 版權： 2012 Elsevier B.V. 版權： 2014 INIST-CNRS 識別號： ISSN: 0926-5805 識別號： DOI: 10.1016/j.autcon.2012.02.001
Appears in Collections:	[Department of Information Management] journal & Dissertation

Files in This Item:

File	Description	Size	Format
index.html		0Kb	HTML	12	View/Open

社群 sharing

Loading...