Support vector regression-based model for prediction of behavior stone column parameters in soft clay under highway embankment

In order to have a proper design and analysis for the column of stone in the soft clay soil, it is essential to develop an accurate prediction model for the settlement behavior of the stone column. In the current research, to predict the behavior in the settlement of stone column a support vector machine (SVM) method is developed and examined. In addition, the proposed model has been compared with the existing reference settlement prediction model that using the monitored field data. As SVM mathematical procedure has resilient and robust generalization aptitude and ensures searching for global minima for particular training data as well. Therefore, the potential that support vector regression might perform efficiently to predict the ground soft clay settlement is relatively valuable. As a result, in this study, comparison of two different developed types of SVM method is carried out. Generally, significant reduction in the relative error (RE%) and root mean square error has been achieved. Utilizing nu-SVM-type model through tenfold cross-validation procedure could achieve outstanding performance accuracy level with RE% less than 2% and CR = 0.9987. The study demonstrates high potential for applying SVM in detecting the settlement behavior of SC prediction and ascertains that SVM could be effectively used for settlement stone columns analysis.

     

Marti i Franques Medal address Development of EFC through international cooperation,transfer of knowledge,and education

Abstract

Under the auspices of the European Federation of Corrosion, the Spanish Society of Industrial Chemistry presents every 2 years the Marti i Franqués Medal to recognise an individual who has made an outstanding contribution to the advancement of the science and technology of corrosion through international cooperation within the EFC, transfer of knowledge, and education. The following address was given by A. D. Mercer when he was awarded the medal at Eurocorr '98, held in Utrecht, The Netherlands on 28 September-1 October.     

Facile synthesis of different morphologies of Te-doped ZnO nanostructures

Te-doped ZnO nanostructures were synthesized by an annealing (vapor–solid) process under ambient conditions, and characterized in terms of their morphological, structural, compositional and optical properties. The structural and morphological characterizations revealed that the synthesized nanostructures were well-defined multipods, needles and spherical particles, and possessed well-crystalline ZnO wurtzite hexagonal phase. Also, in the X-ray diffraction studies, the presence of a shift in the peak positions towards a lower angle, and a decrease in the intensity, with an increase in the Te concentration, as compared to the undoped ZnO, were observed. The chemical composition confirmed the presence of Te, in the case of multipod and needle morphologies. The effect of doping on the crystalline quality and optical properties was also investigated, by using photoluminescence (PL) and Raman spectrometers. The Raman results demonstrated that the doped ZnO nanostructures had a lower crystalline quality than the undoped ZnO. Moreover, the PL results showed a decrease in the band gap for the doped ZnO nanostructures, in comparison to the undoped ZnO. A possible growth mechanism was also proposed.     

Study for prevention of steel corrosion by sacrificial anode cathodic protection

Corrosion of steel tube in sea water was controlled by cathodic protection. Sacrificial anode technique was used. In this technique, weight loss method was used to determine the rate of zinc consumption as a function of temperature, time, pH and solution velocity. Reaction kinetics studies showed that the rate of zinc consumption was first order. Activation parameters were obtained from Arrhenius equation and transition state equation. Two mathematical models were suggested to represent the consumption data. Statistical analysis proved that the second-order multi-terms model was better than the one-term model.     

Reinforced concrete deep beam shear strength capacity modelling using an integrative bio-inspired algorithm with an artificial intelligence model

The design and sustainability of reinforced concrete deep beam are still the main issues in the sector of structural engineering despite the existence of modern advancements in this area. Proper understanding of shear stress characteristics can assist in providing safer design and prevent failure in deep beams which consequently lead to saving lives and properties. In this investigation, a new intelligent model depending on the hybridization of support vector regression with bio-inspired optimization approach called genetic algorithm (SVR-GA) is employed to predict the shear strength of reinforced concrete (RC) deep beams based on dimensional, mechanical and material parameters properties. The adopted SVR-GA modelling approach is validated against three different well established artificial intelligent (AI) models, including classical SVR, artificial neural network (ANN) and gradient boosted decision trees (GBDTs). The comparison assessments provide a clear impression of the superior capability of the proposed SVR-GA model in the prediction of shear strength capability of simply supported deep beams. The simulated results gained by SVR-GA model are very close to the experimental ones. In quantitative results, the coefficient of determination (R²) during the testing phase (R² = 0.95), whereas the other comparable models generated relatively lower values of R² ranging from 0.884 to 0.941. All in all, the proposed SVR-GA model showed an applicable and robust computer aid technology for modelling RC deep beam shear strength that contributes to the base knowledge of material and structural engineering perspective.

     

Cost of green hydrogen: Limitations of production from a stand-alone photovoltaic system

The aim of this work is to analyse the price of renewable hydrogen production in a stand-alone photovoltaic plant. The energy studied herein is generated in a photovoltaic plant. Two dependent parameters that directly affect the price of hydrogen are analysed in detail: the price of the electricity needed to carry out its production process, and the utilisation rate of the connected electrolyser. To this end, a photovoltaic plant is dimensioned with the help of the PVsyst simulator, by means of which the hourly generation curves are obtained. A variable power electrolyser is employed to study its performance according to these photovoltaic production curves. Furthermore, the system is studied by introducing batteries capable of storing the energy left over during the day and of supplying the electrolyser when the photovoltaic power is insufficient. The selling prices calculated in the various scenarios in terms of efficiency and electricity cost are calculated. The significance of a combined analysis of these two parameters and their real impact on the final price of hydrogen is also analysed. This article aims to analyse the price of green hydrogen produced through an isolated photovoltaic system. When the hourly production is evaluated, differences are found with respect to global production that justify the importance of the variables analysed herein, which could not be determined in any other way. The behaviour of isolated production and its effects are discussed.     

Enhancement of process modelling and simulation evaluation by deploying a test for assessment and feedback individualisation

This paper demonstrates a novel approach for a computer-based course assessment. A test is introduced in which computers are deployed. This significantly contributes to the enhancement of the marking consistency, individual performance distinction and feedbacks, and widen the questions range for computer-based modules. The proposed test method, for the first time, uses the simulation files marking for individualised evaluation purposes. The methodology has successfully been implemented in practice for three modules including Process Simulation (CE2105), Advanced Process Simulation (CE4023), and Process Computation (CE3021) at Aston University (UK) over three academic years, from 2016 to 2019. The effectiveness of the proposed approach has been evaluated using several factors, including final marks, consistency multiple academic years, and mark distribution. In contrast to the common teamwork assessments, individualised feedback became possible. While ASPEN has been used for CE4023 and CE2105 tests, MATLAB has been applied as the computation platform for CE3021 module. This reveals the applicability of different software in proposed methodology. The number of students in the cohorts studied was from 52 to 204, demonstrating the applicability of the method for various cohort sizes. Even though the methodology has been demonstrated based on the chemical engineering discipline modules, it allows digitalising the delivery and assessment of a wide range of simulation techniques in many disciplines.     

Galvanic Corrosion of Steel–Brass Couple in Petroleum Waste Water in Presence of a Green Corrosion Inhibitor: Electrochemical,Kinetics, and Mathematical View

Journal of Failure Analysis and Prevention - Control of galvanic corrosion of steel–brass couples in petroleum waste water solution by Curcuma longa was studied at different temperatures,...     

Correction to: SPMI: Single Phase Multiple Initiator Protocol for Coverage in Wireless Sensor Networks

One of the complex problems nowadays in communication systems is the lack of frequency spectrum. To solve this problem, cognitive radio is considered the best candidate that can opportunistically exploit the spectrum. The periodogram based spectrum sensing technique can be used to detect the spectrum in cognitive radio. It is a useful technique since does not need to prior information about the primary signal. In this paper, a new periodogram is presented using the Discrete Cosine Transform (DCT). Results are analyzed and compared with the current raw periodogram. It is observed that the DCT periodogram outperforms the raw technique in terms of probabilities of false alarm and detection, variance, and complexity. In addition, the lowest power of DCT coefficients can be removed without compromising the sensing performance. The proposed system shows high probability of detection with low probability of false alarm even in the case of low Signal-to-Noise Ratio (SNR).