Turbidity is a measure of water quality. Excessive turbidity poses a threat to health and causes pollution. Most of the available mathematical models of water treatment plants do not capture turbidity. A reliable model is essential for effective removal of turbidity in the water treatment plant. This paper presents a comparison of Hammerstein Wiener and neural network technique for estimating of turbidity in water treatment plant. The models were validated using an experimental data from Tamburawa water treatment plant in Kano, Nigeria. Simulation results demonstrated that the neural network model outperformed the Hammerstein-Wiener model in estimating the turbidity. The neural network model may serve as a valuable tool for predicting the turbidity in the plant.

The complex shear modulus (G*) and phase angle (δ) are fundamental viscoelastic rheological properties used in the estimation of rutting and fatigue pavement distress in asphalt binder. In the tropical regions, rutting and fatigue cracking are major pavement distress affecting the serviceability of road infrastructure. Laboratory testing of the complex shear modulus and phase angle requires expensive and advanced equipment that is not obtainable in major laboratories within the developing countries of the region, giving rise to the need for an accurate predictive model to support quality pavement design. This research aims at developing a predictive model for the estimation of rutting and fatigue susceptive of asphalt binder at intermediate and high pavement temperatures. Asphalt rheological and ageing test was conducted on eight mixes of modified binders used to build the study database containing 1976 and 1668 data points for rutting and fatigue parameters respectively. The database was divided into training and simulation dataset. The Gaussian process regression (GPR) algorithm was used to predict the rutting and fatigue parameters using unaged and aged conditioned inputs. The proposed GPR was compared with the support vector machine (SVM), recurrent neural networks (RNN) and artificial neural network (ANN) models. Results show that the model performed better in the estimation of rutting parameter than the fatigue parameter. Further, unaged input variables show better reliability in the prediction of fatigue parameter.

Asphalt pavement structures in countries with tropical climates are prone to experience distress, mainly due to water related damage. This study aims to evaluate the effects of ZycoTherm on the behaviour of natural rubber latex (NRL) modified asphalt binders. The morphology of the modified asphalt binders was also investigated. Two different percentages of NRL were incorporated in this study, at 3% and 6% respectively based on the weight of the asphalt binder. The modified asphalt binders (with or without ZycoTherm) were analysed based on softening point, penetration value, rotational viscosity, complex modulus, phase angle, rutting potential, and torsional recovery (T_R). Subsequently, the spreadability of NRL in the asphalt binder was observed using a polarising optical microscope. The results indicate that the addition of NRL improved the performance of the asphalt binder in terms of its G*/sinδ parameter. An agglomerated network of mixed microstructures was formed in the modified asphalt binder matrix irrespective of the aging condition. Meanwhile, the morphology structure of virgin and short term-aged binders prepared with ZycoTherm indicates uniform dispersed network monographs. From the modified asphalt binder test results, it can be deduced that asphalt binders prepared with ZycoTherm exhibit superior resistance towards rutting potential.