Modeling of strength of high performance concrete using Machine Learning
The actual concrete compressive strength (MPa) for a given mixture under a specific age (days) was determined from laboratory. Data is in raw form (not scaled).The data has 8 quantitative input variables, and 1 quantitative output variable, and 1030 instances (observations).
Material manufacturing
Concrete is the most important material in civil engineering. The concrete compressive strength is a highly nonlinear function of age and ingredients. These ingredients include cement, blast furnace slag, fly ash, water, superplasticizer, coarse aggregate, and fine aggregate.
● Cement : measured in kg in a m3 mixture ● Blast : measured in kg in a m3 mixture ● Fly ash : measured in kg in a m3 mixture ● Water : measured in kg in a m3 mixture ● Superplasticizer : measured in kg in a m3 mixture ● Coarse Aggregate : measured in kg in a m3 mixture ● Fine Aggregate : measured in kg in a m3 mixture ● Age : day (1~365) ● Concrete compressive strength measured in MPa Learning Outcomes: ● Exploratory Data Analysis ● Building ML models for regression ● Hyper parameter tuning