International Roughness Index (IRI) and Pavement Condition Index (PCI) are among other pavement condition indices used to assess pavement surface condition. The literature suggests that most of the pavement indices are related as a result of which several models have been developed to predict one index from the other. This study uses the Long-Term Pavement Performance (LTPP) database to develop a simplified regression model that links PCI with IRI. Measured pavement distresses from 1448 LTPP sections from the Specific Pavement Studies (SPS) and General Pavement Studies (GPS) representing 12744 data points were utilised for the PCI estimation. A total of 1208 sections with 10868 data points were used for model development while 240 sections with 1876 data points were used for the model validation. A sigmoid function is found to best express the relationship between PCI and IRI with a coefficient of determination (R²) of 0.995. The bias in the predicted IRI values is significantly very low. The model validation using a different dataset also yielded highly accurate predictions (R² = 0.992). Finally, a pavement condition rating based on IRI is proposed. This system yields rating equivalent to the widely used PCI rating method which is based on the pavement condition.

Natural weak cohesive subgrade soils are considered low quality foundations for pavement structures that may lead to significant pavement distresses. Several solutions can be proposed to enhance pavement behavior under cyclic loads such as stabilizing clays with cement or lime, removing and replacing weak material, or increasing the base thickness to reduce the stresses on subgrade. Despite the effectiveness of using additives like lime and cement for subgrade stabilization, cement and lime are considered environmentally unfriendly owing to the generated emission during their production. Recently, several researchers have shown the potential of using biopolymers as alternative for traditional additives. This study investigates the feasibility of using sodium alginate biopolymer for treating weak cohesive subgrades particularly under repeated traffic loads for pavement construction applications. Two different soils (clay and silt), representing a typical range of cohesive soils in the delta region in Egypt, were investigated. The sodium alginate was added to the cohesive soils in the range of 0% to 6% using two different mixing methods (wet and dry). The experimental program included basic engineering tests, resilient modulus, and unconfined compressive strength tests, along with other chemical tests such as Fourier transform infrared spectroscopy (FTIR) and thermal gravimetric analysis (TGA). In addition, scanning electronic microscope (SEM) images were performed for both untreated and treated soils at different alginate contents. Results showed that the resilient modulus of the treated subgrade soils generally increased with the increase in sodium alginate content up to an optimum value of 2% and 4% for clayey and silty soils, respectively. This significant improvement was reached in the first 4 days of curing with continuous gain of strength up to 28 days. This increase in resilient modulus was found to be dependent on the soil type, alginate concentration, curing time, and treatment method. The microlevel tests, TGA, SEM, and FTIR, confirmed the presence of the cementation polymer due to sodium alginate that increased the bond and cross-linking between particles, and consequently an increase in the soil stiffness and strength occurred.

The mechanistic-empirical pavement design guide (MEPDG) is the research version of the newly released DARWin-ME software by AASHTO. MEPDG includes two models for Levels 2 and 3 hot-mix asphalt (HMA) dynamic modulus (E*) predictions. The two models are NCHRP 1-37A and NCHRP 1-40D. The primary difference between the two is the binder stiffness parameter; viscosity or shear modulus. Moreover, MEPDG includes three levels for binder stiffness characterization: viscosity for Level 1 conventional binders, shear modulus for Level 1 superpave binders, and default values for Level 3. The influence of the binder characterization input level on the performance of the MEPDG E* predictive models is evaluated in this paper. To calibrate the models for Idaho, 27 HMA mixtures commonly used in Idaho were investigated. Results showed that the performance of the investigated models varied based on the temperature and the binder characterization method. The NCHRP1-37A E* model with MEPDG Level 3 binder inputs yielded ...