Anwar Khitab

Development of sediment load estimation models by using artificial neural networking techniques.

This study aims at the development of an artificial neural network-based model for the estimation of weekly sediment load at a catchment located in northern part of Pakistan. The adopted methodology has been based upon antecedent sediment conditions, discharge, and temperature information. Model input and data length selection was carried out using a novel mathematical tool, Gamma test. Model training was carried out by using three popular algorithms namely Broyden-Fletcher-Goldfarb-Shanno (BFGS), back propagation (BP), and local linear regression (LLR) using forward selection of input variables. Evaluation of the best model was carried out on the basis of basic statistical parameters namely R-square, root mean squared error (RMSE), and mean biased error (MBE). Results indicated that BFGS-based ANN model outperformed all other models with significantly low values of RMSE and MBE. A strong correlation was also found between the observed and estimated sediment load values for the same model as the value of Nash-Sutcliffe model efficiency coefficient (R-square) was found to be quite high as well.

Lunar concrete: Prospects and challenges

The possibility of using concrete as a construction material at the Moon surface is considered. Dissimilarities between the Earth and the Moon and their possible effects on concrete are also emphasized. Availability of constituent materials for concrete at lunar surface is addressed. An emphasis is given to two types of materials, namely, hydraulic concrete and sulfur concrete. Hydraulic concrete necessitates the use of water and sulfur concrete makes use of molten sulfur in lieu of cement and water.

Fracture toughness and failure mechanism of high performance concrete incorporating carbon nanotubes

Cement and concrete composites are inherently brittle and exhibit very less tensile/flexural strength capacity as compared to their compressive strength. Use of thoroughly dispersed carbon nanotubes in the concrete matrix is one of the possible solution for enhancing mechanical properties in tension/flexure. In the present research work, small fractions of multiwall carbon nanotube (MWCNTs) i.e. 0.05 and 0.10 wt% of cement have been integrated into the cement concrete to study their effect on the mechanical properties of the resultant concrete mixtures. The enhanced performance of the whole mix lies on a single point that MWCNTs must be thoroughly disperse in the mixture. Hence, special arrangement through usage of high energy sonication along with amended acrylic based polymer (performing as a surfactant) was made to have a uniform dispersion of MWCNTs in the concrete mix. The testing of concrete samples includes i.e., flexure, splitting tensile and compressive strengths after 3, 7, 28 and 56 days of curing. After having comparison with the control mix cured for 28 days, it was observed that the addition of 0.05 wt% MWCNTs increased the splitting tensile strength by 20.58%, flexural strength by 26.29% and compressive strength by 15.60%. Through above results, which verify the increase in concrete mix strength after adding MWCNTs, these MWCNTs may be incorporated in the treatment of Nano/micro cracks completed through process of connecting, branching and pinning. Similarly, as proved in threepoint bending tests, MWCNTs also enhances the breaking strains as well as the fracture energy of the concrete mixes, besides, imparting increase to the strength. The investigations have shown that incorporating lesser amounts of MWCNTs i.e., 0.05 and 0.10 wt% of cement to the concrete mixes after insuring there complete dispersion, unusually improve their properties like mechanical strengths and fracture behavior.

Ionic Contamination and Decontamination of Porous Media

We document in this work the problem of the ionic species transport through porous media. The transport of ions may be due to several processes, namely diffusion, convection and capillary suction. We do not account for the latter because we are dealing with saturated materials, and we also assume that convection phenomena are not present. The transport of species is hence due to diffusion only. In some cases, the ionic transport may be enhanced by the application of an external electrical field and this phenomenon is called electromigration.

Development of streamflow prediction models for a weir using ANN and step-wise regression

The study was aimed to develop an accurate prediction model for streamflow at Patrind Hydropower Station located on Kunhar river right on the border line of district Abbottabad, KPK and district Muzaffarabad, AJK. Antecedent meteorological data condition of upstream site was considered as inputs. A variety of input combinations were made to select the suitable ones for smooth model development. The model training was carried out using two artificial neural networking techniques; two layer back propagation and Broyden Fletcher GoldfrabShano. The results were also compared with a step-wise regression based model. The best model was evaluated on the basis of distributive statistics analysis such as root mean square error, bias, variance and correlation coefficient R2. All the developed models showed extraordinary results with very high values of model efficiency (more than 95%) but model based upon step-wise regression technique outperformed all the other models with low values of variance and BIAS.