Considerable advantages could be achieved if the cold bituminous emulsion mixtures (CBEMs) show comparable engineering properties to hot-mix asphalt (HMA), particularly because CBEMs require no heat within the manufacture and laying process. As a result, such pavement produces less environmental impact, is more cost effective, and requires less energy consumption. This paper focuses on upgrading the CBEMs to a stage whereby their mechanical properties are comparable to traditional HMA. Waste or by-product materials were used within the CBEMs to improve the mechanical properties, namely indirect tensile stiffness modulus and creep stiffness. Five percentages from 0 to 5.5% of aggregate mass in the mixture of the LJMU-FA1 that is waste or by-product material was incorporated in the CBEMs. The results have illustrated a comparative enhancement in the mechanical properties of the new cold mixtures attributable to the use of the specific waste materials. Thus, new CBEMs having superior mechanical prope...

Concerns about the environment, the cost of energy, and safety mean that low-energy cold-mix asphalt materials are very interesting as a potential replacement for present-day hot mix asphalt. The main disadvantage of cold bituminous emulsion mixtures is their poor early life strength, meaning they require a long time to achieve mature strength. This research work aims to study the protentional utilization of waste and by-product materials as a filler in cold emulsion mixtures with mechanical properties comparable to those of traditional hot mix asphalt. Accordingly, cold mix asphalt was prepared to utilize paper sludge ash (PSA) and cement kiln dust (CKD) as a substitution for conventional mineral filler with percentages ranging from 0–6% and 0–4%, respectively. Test results have shown that the incorporation of such waste materials reflected a significant improvement in the mixture’s stiffness and strength evolution. The cementitious reactivity of PSA produces bonding inside the mixtures, while CKD is used as an additive to activate the hydration process of PSA. Therefore, based on the results, it will be easier to build cold mixtures by shortening the amount of time needed to reach full curing conditions.

Subbase strength characteristics is one of the main inputs of pavement design, and such strength characteristics are normally represented by indices such as resilient modulus, dynamic modulus, and California Bearing Ratio (CBR), with the latter being a widely used index among pavement and geotechnical engineers. This paper examines the capability of Artificial Neural Networks (ANN) to develop a correlation between subbase CBR and primary soil data, which could help with estimating CBR for prediction purposes and with identifying the significance of each index with regard to subbase strength. Data were sampled from different areas in Karbala, Iraq, and a total of 358 subbase samples were used for model training and validation. The results showed that the proposed ANN model could successfully predict the CBR value using soil index data. Additionally, a sensitivity analysis was conducted to determine the importance of each contributing factor, and within the boundaries of the local subbase characteristics, the test results indicated that soluble salts were the most effective factor among soil parameters with an importance percentage of 39.46%, while the Plasticity Index (PI) was the least important factor, with a percentage of 2.06%. Based on the validity and quality of subbase soil tests, using ANN to predict CBR value may offer a suitable replacement for lengthy and expensive laboratory testing based on validated data for materials supplied from Karbala quarries.