Enhancing solids deposit prediction in gully pots with explainable hybrid models: A review.

Urban flooding has made it necessary to gain a better understanding of how well gully pots perform when overwhelmed by solids deposition due to various climatic and anthropogenic variables. This study investigates solids deposition in gully pots through the review of eight models, comprising four deterministic models, two hybrid models, a statistical model, and a conceptual model, representing a wide spectrum of solid depositional processes. Traditional models understand and manage the impact of climatic and anthropogenic variables on solid deposition but they are prone to uncertainties due to inadequate handling of complex and non-linear variables, restricted applicability, inflexibility and data bias.

Wet-Dry Cycles and Microstructural Characteristics of Expansive Subgrade Treated with Sustainable Cementitious Waste Materials.

This work presents an experimental study on the physico-mechanical and microstructural characteristics of stabilised soils and the effect of wetting and drying cycles on their durability as road subgrade materials. The durability of expansive road subgrade with a high plasticity index treated with different ratios of ground granulated blast furnace slag (GGBS) and brick dust waste (BDW) was investigated. Treated and cured samples of the expansive subgrade were subjected to wetting-drying cycles, California bearing ratio (CBR) tests, and microstructural analysis.

Modified Orange Peel Waste as a Sustainable Material for Adsorption of Contaminants.

World orange production is estimated at 60 million tons per annum, while the annual production of orange peel waste is 32 million tons. According to available data, the adsorption capacity of orange peel ranges from 3 mg/g to 5 mg/g, while their water uptake is lower than 1 mg/g. The low water uptake of orange peel and the abundance of biomass in nature has made orange peel an excellent biosorption material.

Strength Predictive Modelling of Soils Treated with Calcium-Based Additives Blended with Eco-Friendly Pozzolans-A Machine Learning Approach.

The unconfined compressive strength (UCS) of a stabilised soil is a major mechanical parameter in understanding and developing geomechanical models, and it can be estimated directly by either lab testing of retrieved core samples or remoulded samples. However, due to the effort, high cost and time associated with these methods, there is a need to develop a new technique for predicting UCS values in real time. An artificial intelligence paradigm of machine learning (ML) using the gradient boosting (GB) technique is applied in this study to model the unconfined compressive strength of soils stabilised by cementitious additive-enriched agro-based pozzolans.