The effect of partial replacement of coarse aggregate by plastic waste on the performance of concrete under impact three-point bending loading was investigated experimentally and numerically. Specimens were prepared for 5%, 10% and 20 % replacements by volume of coarse aggregate. For each case, three beams of 100 mm wide, 50 mm deep and 400 mm long were loaded to failure in a drop-weight impact machine by subjecting it to 30 N weight from 400 mm height, while another three beams of the same size were tested under static load. The load-displacement of beams of concrete with plastic waste subjected to static and impact loads were studied. The dynamic beam behaviour was also analysed numerically using the finite-element method (FEM) based LUSAS software. In general, the experimental results reveal that the impact tup, inertial load and bending load increase with the increase in the percentage of coarse aggregate replacement by plastic waste, while the static peak bending load always decreases. The concrete with plastic waste is stronger and more energy-absorbing under impact loading, than under static loading. The predicted load against displacement behaviours of both ordinary concrete and concrete with plastic waste, are well matched with the experimental results.

There is a growing global interest in preserving transportation infrastructure. This necessitates routine evaluation and timely maintenance of road networks. The effectiveness of pavement management systems (PMSs) heavily relies on accurate pavement deterioration models. However, there are limited comparative studies on modeling approaches for rural roads in arid climatic conditions using the same datasets for training and testing. This study compares three approaches for developing a pavement condition index (PCI) model as a function of pavement age: classical regression, machine learning, and deep learning. The PCI is a pavement management index widely adopted by many road agencies. A dataset on pavement age and distress was collected over a twenty-year period to develop reliable predictive models. The results demonstrate that the regression model, machine learning model, and the deep learning model achieved a coefficient of determination ($R^2$) of 0.973, 0.975, and 0.978, respectively. While these values are technically equal, the average bias for the deep learning model (1.14) was significantly lower than that of the other two models, signaling its superiority. Additionally, the trend predicted by the deep learning model showed more distinct phases of PCI deterioration with age than the machine learning model. The latter exhibited a wider range of PCI deterioration rates over time compared to the regression model. The deep learning model outperforms a recently developed regression model for a similar region. These findings highlight the potential of using deep learning to estimate pavement surface conditions accurately and its efficacy in capturing the PCI-age relationship.

In this study, a series of rigid-wall downward erosion tests were conducted to investigate the mitigation effect of microbially induced calcite precipitation (MICP) on soil internal erosion. Sand-kaolin mixture was used in this study, which proves to be internally unstable in terms of particle size distribution. Binary mixture was prepared using dry tamping method. Bacteria and cementation solutions were injected into samples at several phases. For comparison, control samples without MICP treatment were prepared. All samples were subjected to downward seepage flow with incremental flow rates. Evolutions of hydraulic conductivity, effluent kaolin concentration, and total erosion mass were measured against hydraulic gradient and shear stress. Results confirmed that, with MICP treatment, both critical hydraulic gradient and shear stress were significantly increased and erosion mass was reduced under the same hydraulic conditions.