A Machine-Learning Approach for the Prediction of Fly-Ash Concrete Strength  

作　　者：Shanqing Shao Aimin Gong Ran Wang Xiaoshuang Chen Jing Xu Fulai Wang Feipeng Liu 

机构地区：[1]College of Water Conservancy,Yunnan Agricultural University,Kunming,650201,China [2]Institute of International Rivers and Eco-Security,Yunnan University,Kunming,650500,China [3]Southwest Survey and Planning Institute of National Forestry and Grassland Administration,Kunming,650031,China

出　　处：《Fluid Dynamics & Materials Processing》2023年第12期3007-3019,共13页流体力学与材料加工（英文）

基　　金：supported by the Scientific Research Fund Project of Yunnan Education Department(Grant Numbers 2023J1974 and 2023J1976);the Yunnan University Professional Degree Graduate Student Practical Innovation Fund Project(Grant Number ZC-22222374);also supported by the Yunnan Provincial Education Department Fund(Grant No.2022Y286).

摘　　要：The composite exciter and the CaO to Na_(2)SO_(4) dosing ratios are known to have a strong impact on the mechanical strength offly-ash concrete.In the present study a hybrid approach relying on experiments and a machine-learn-ing technique has been used to tackle this problem.The tests have shown that the optimal admixture of CaO and Na_(2)SO_(4) alone is 8%.The best 3D mechanical strength offly-ash concrete is achieved at 8%of the compound activator;If the 28-day mechanical strength is considered,then,the best performances are obtained at 4%of the compound activator.Moreover,the 3D mechanical strength offly-ash concrete is better when the dosing ratio of CaO to Na_(2)SO_(4) in the compound activator is 1:1;the maximum strength offly-ash concrete at 28-day can be achieved for a 1:1 ratio of CaO to Na_(2)SO_(4) by considering a 4%compound activator.In this case,the compressive andflexural strengths are 260 MPa and 53.6 MPa,respectively;the mechanical strength offly-ash concrete at 28-day can be improved by a 4:1 ratio of CaO to Na_(2)SO_(4) by considering 8%and 12%compound excitants.It is shown that the predictions based on the aforementioned machine-learning approach are accurate and reliable.

关 键 词：Fly ash compound activator machine-learning approach 

分 类 号：TU528[建筑科学—建筑技术科学]