The traditional method to obtain optimum bitumen content and the relevant parameters of asphalt pavements entails time-consuming, complicated and expensive laboratory procedures and requires skilled personnel. This research study uses innovative and advanced machine learning techniques, i.e., Multi-Expression Programming (MEP), to develop empirical predictive models for the Marshall parameters, i.e., Marshall Stability (MS) and Marshall Flow (MF) for Asphalt Base Course (ABC) and Asphalt Wearing Course (AWC) of flexible pavements. A comprehensive, reliable and wide range of datasets from various road projects in Pakistan were produced. The collected datasets contain 253 and 343 results for ABC and AWC, respectively. Eight input parameters were considered for modeling MS and MF. The overall performance of the developed models was assessed using various statistical measures in conjunction with external validation. The relationship between input and output parameters was determined by performing parametric analysis, and the results of trends were found to be consistent with earlier research findings stating that the developed predicted models are well trained. The results revealed that developed models are superior and efficient in terms of prediction and generalization capability for output parameters, as evident by the correlation coefficient (R) (in this case >0.90) for both ABC and AWC.
The present research aims at evaluating the mechanical performance of untreated and treated crumb rubber concrete (CRC). The study was also conducted to reduce the loss in mechanical properties of CRC. In this study, sand was replaced with crumb rubber (CR) with 0%, 5%, 10%, 15%, and 20% by volume. CR was treated with NaOH, lime, and common detergent for 24 h. Furthermore, water treatment was also carried out. All these treatments were done to enhance the mechanical properties of concrete that are affected by adding CR. The properties that were evaluated are compressive strength, indirect tensile strength, unit weight, ultrasonic pulse velocity, and water absorption. Compressive strength was assessed after 7 and 28 days of curing. The mechanical properties were decreased by increasing the percentage of the CR. The properties were improved after the treatment of CR. Lime treatment was found to be the best treatment of all four treatments followed by NaOH treatment and water treatment. Detergent treatment was found to be the worse treatment of all four methods of treatment. Despite increasing the strength it contributed to strength loss.
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