The fabrication and testing of a self-sensing MWCNT nanocomposite sensor for oil leak detection

Abstract Oil spillage, due to either direct or indirect accidents, can cause major environmental and economic issues if not detected and remedied immediately. In this study, the unique properties of carbon nanotubes have shown a substantial sensing capability for such a purpose when incorporated into a nanostructured composite material. A high-efficiency self-sensing nanocomposite sensor was fabricated by inserting highly conductive multi-walled carbon nanotubes (MWCNTs) into an elastomeric polymer substrate. The microstructure of the nanocomposite sensor was studied using scanning electronic microscopy and Raman spectroscopy. The response rate of the sensor was evaluated against different MWCNT concentrations, geometrical thickness and applied strains (causing by stretching). The results indicated that the response rate of the sensor (β) decreased with increasing MWCNT concentration and showed the strongest response when the sensor contained a 1.0 wt % concentration of MWCNTs. Additionally, it was found that the response time of the self-sensing nanocomposite sensors decreased in keeping with decreases in the sensor thickness. Moreover, when the sensor was subjected to strain, while immersed in an oil bath, it was found that the response rate (β) of the unstretched self-sensing nanocomposite sensor was significantly lower than that of the stretched one. The sensors given a 3% applied strain presented a response rate (β) ≈ 7.91 times higher than of the unstretched one. The self-sensing nanocomposite sensor described here shows good potential to be employed for oil leakage detection purposes due to its effective self-damage sensing capability and high sensing efficiency and low power consumption.</jats:p

A Review on Fly Ash as a Raw Cementitious Material for Geopolymer Concrete

This paper presents a review on fly ash as prime materials used for geopolymer. Due to its advantages of abundant resources, less in cost, great workability and high physical properties which lead to achieve high mechanical properties. Fly ash is considered as one of the largest generated industrial solid wastes or so-called industrial by products, around the world particularly in China, India and USA. The characteristics of fly ash allow it to be a geotechnical material to produce geopolymer cement or concrete as an alternative of Ordinary Portland cement. Many efforts are made in this direction to formulate a suitable mix design of fly ash based-geopolymer by focusing on fly ash as the main prime material. The physical properties, chemical compositions and chemical activation of fly ash are analysed and evaluated in this review paper. Reference has been made to different ASTM, ACI standards and other researches work in geopolymer area. </jats:p

Hybridized Deep Learning Model for Perfobond Rib Shear Strength Connector Prediction

Accurate and reliable prediction of Perfobond Rib Shear Strength Connector (PRSC) is considered as a major issue in the structural engineering sector. Besides, selecting the most significant variables that have a major influence on PRSC in every important step for attaining economic and more accurate predictive models, this study investigates the capacity of deep learning neural network (DLNN) for shear strength prediction of PRSC. The proposed DLNN model is validated against support vector regression (SVR), artificial neural network (ANN), and M5 tree model. In the second scenario, a comparable AI model hybridized with genetic algorithm (GA) as a robust bioinspired optimization approach for optimizing the related predictors for the PRSC is proposed. Hybridizing AI models with GA as a selector tool is an attempt to acquire the best accuracy of predictions with the fewest possible related parameters. In accordance with quantitative analysis, it can be observed that the GA-DLNN models required only 7 input parameters and yielded the best prediction accuracy with highest correlation coefficient (R = 0.96) and lowest value root mean square error (RMSE = 0.03936 KN). However, the other comparable models such as GA-M5Tree, GA-ANN, and GA-SVR required 10 input parameters to obtain a relatively acceptable level of accuracy. Employing GA as a feature parameter selection technique improves the precision of almost all hybrid models by optimally removing redundant variables which decrease the efficiency of the model