Strength Analysis Using Weighted Random Forest based on Chemical and Environmental Properties of Concrete

The one-fifth of coal ash generated through thermal power plant are represented as bottom ash and remaining are fly ash. In previous some years, researches are developed for utilize or else of them consider partly replacement of sand in manufacturing of concrete. Current research analyzes a concrete behavior manufactured through a full replacement of nature sand with optimal mix of the ashes. Impact of fly and bottom ash mixtures is determined by capacity of work, test of strength and resistance of attacks in acid. That is obtained which mix has half of fly and bottom ash mixtures as fine aggregate and closely the similar strength in compressive, 10 percent much strength in split strength and huge strength in flexural than a maintain concrete contains 12.15 percent low density than maintain concrete. The volume and value of water absorption of pore space is identified as much to concrete has a fly and bottom ash mix. Though, certain concrete processed good in limiting a sulphuric acid and sulphate attacks. And this suggests that which mixture has half of fly and bottom ash is utilized as matchable replacement for nature sand in concrete in moderate environment. The output of testing the materials and mixture proportions are considered as input data to evaluate the compressive strength of the concrete. For strength analysis of data a machine learning based Weighted random forest is proposed to estimate concrete strength accurately. Experimental results of the proposed weighted random forest based strength evaluation model achieved Root Mean Squared Error (RMSE) of 2.134 and 0.87 for compressive strength and flexible strength respectively.