The paper focuses on the improvement of the clayey soil (A-6) with the locally available and cheap modifiers, in terms of stiffness behavior of the weak subgrade soil for flexible pavement. The modifiers used include lime (hydrated), marble waste and sand. The soil specimens underwent triaxial testing and Clegg impact testing. Triaxial testing involved the assessment of the resilient modulus (M_R) and impact testing using the Clegg Impact Hammer. According to the study, lime proved to be the most influencing modifier as it improves the stiffness of the weak soil better than other modifiers. A quite accurate statistical relationship between the M_R and the variables (including Clegg Impact) involved in the testing procedure has been established

Empirical based approaches play an important role in tunnel excavation and support system design. These approaches are considered to be very effective in optimising the process of tunnel excavation and particularly tunnel widening. Several reliable empirical approaches have been developed, however the selection or utilisation of an appropriate empirical method for designing the widening of a tunnel is still a challenging task. Therefore, in this work, the analysis of seven different empirical design approaches was carried out to determine the rock mass behaviour during tunnel widening in high in-situ stress state. These approaches include New Austrian Tunnelling Method, Rock Mass Rating, Rock Mass Quality, Rock Mass Index, Rock Structure Rating, Geological Strength Index and Basic Quality Index. On the basis of simulated statistical results obtained from the said empirical approaches, it was found that the application of Rock Mass Quality approach is highly effective in the tunnel widening since it can satisfactorily incorporate the equivalent dimensions and in-situ stress condition of widened tunnel. The method furnishes optimised reinforcement and support design. Additionally, this study also produces reliable data related to the initial excavation of tunnel which can be helpful in defining precise rock mass parameters during tunnel widening.

Multiscale forecasting of groundwater levels (GWLs) is essential for ensuring the sustainable management of groundwater resources, particularly considering the potential impacts of climate change. Such forecasting requires a model that is not only accurate in predicting GWLs but also computationally efficient, ensuring its suitability for practical applications. In this study, a temporal convolutional network (TCN) is implemented to forecast GWLs for 17 monitoring wells possessing diverse hydrogeological characteristics, located across South Korea. Using deep learning, the influence of meteorological variables (i.e., temperature, precipitation) on the forecasted GWLs was investigated by dividing the input features into three categories. Additionally, the models were developed for three forecast intervals (at 1-, 3-, and 6-month lead times) using each category input. When compared with state-of-the-art models, that is, long short-term memory (LSTM) and artificial neural network (ANN), the TCN model showed superior performance and required much less computational complexity. On average, the TCN model outperformed the LSTM model by 24%, 21%, and 25%, and the ANN model by 24%, 37%, and 47%, respectively, for 1-, 3-, and 6-month lead times. Based on these results, the proposed TCN model can be used for real-time GWL forecasting in hydrological applications.