Artificial Neural Network Modelling for Asphalt Concrete Samples with Boron Waste Modification
Abstract
Civil engineering science has evolved into the 21st century with concepts of recycling and sustainability. There are many studies has included the concerns in this sense and design tries to create a minimum effect on the natural state of environment. In this context, this study is also interested to use in pavement materials through the recycling of waste materials. Effect of boron wastes on asphalt concrete samples was investigated and varied rates of Crushed Boron Waste (CBW) were used as aggregate for preparation and experiments for asphalt concrete samples by using the Marshall Design Method. Besides, an artificial neural network (ANN) model was created for the evaluation of obtained data. As a result of Marshall Design Method, it has been proved that boron wastes can be used in asphalt concrete within the specification limits. Furthermore, examination of modelling and statistical analysis, mechanical performance of asphalt concrete samples with and without CBW addition has been predicted in noticeable manner. As a result of regression analysis, training and test sets r2 values are reached 0.95-0.91 for stability and 0.91-0.87 for flow values. By this investigation, an environmental waste has been reused and sustainable living has been provided.
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