Application of EPR-MOGA in computing the liquefaction-induced settlement of a building subjected to seismic shake

Accurate prediction of the liquefaction-induced settlement (\({S}_{\mathrm{lc}}\)) is an essential requirement for a good design of buildings resting on liquefiable ground and subjected to seismic shake. However, prediction of the \({S}_{\mathrm{lc}}\) is not straightforward process and it requires advanced soil models and calibrated soil parameters that are not readily available for designers/practitioners. In addition, the available empirical models to estimate the \({S}_{\mathrm{lc}}\) have been developed using either classical regression analysis or multivariate adaptive regression splines and such techniques produce complicated models. Also, these empirical models have been developed utilizing results of numerical modelling. To overcome these limitations, novel model has been developed in this paper utilizing robust regression analysis driven by artificial intelligence called the evolutionary polynomial regression analysis. The new model has been developed using centrifuge results (real laboratory measurements) and can be easily used to accurately estimate the liquefaction induced settlement. The developed model scored a mean absolute error, root mean square error, mean, standard deviation of the predicted to measured values, coefficient of determination, \(a20 - \mathrm{index}\), and EPR coefficient of determination of 2.12 cm, 2.84 cm, 1.06, 0.19, 0.98, 0.77, and 97%, respectively, for the learning data and 1.73 cm, 3.31 cm, 0.99, 0.17, 0.97, 0.75, and 97%, respectively, for the examination data. The developed model has also been used in a parametric study to provide an insight into the sensitivity of the \({S}_{\mathrm{lc}}\) to the foundation width, building height, pressure applied on the foundation, thickness and relative density of the liquefiable layer, and earthquake intensity. The results obtained from the parametric study are reasonable and in agreement with previous studies in the literature. Thus, the developed model can be employed to optimize designs and to reduce design costs as it does not require complicated analyses and/or expensive computational facilities.

     

Mg0.5NixZn0.5-xFe2O4 spinel as a sustainable magnetic nano-photocatalyst with dopant driven band shifting and reduced recombination for visible and solar degradation of Reactive Blue-19

Focussing on visible light active ferrites for high performance removal of noxious pollutants, we report the synthesis of Mg_0.5Ni_xZn_0.5-xFe₂O₄ (x = 0.1, 0.2, 0.3, 0.4, & 0.5) ferrite nanoparticle for degradation of reactive blue-19 (RB-19). Lattice parameters calculated using intense X-ray diffraction (XRD) peaks and Nelson-Riley plots (N-R plot) are in well agreement with each other. The sample Mg_0.5Ni_0.4Zn_0.1Fe₂O₄ (M5N4) exhibits best performance with 99.5% RB-19 degradation in 90 min under visible light. Photoluminescence (PL) results confirm that recombination of charge carriers is highly reduced in the photocatalyst. Scavenging experiments suggest that O₂⁻ radicals were the dominant species responsible for photocatalytic performance. The photocatalytic mechanism was explained in terms of dopant driven shifting of conduction bands and valence bands (calculated by Mott-Schottky plots). The thermodynamic probability of radical generation along with role of redox cycles of metal ions has been discussed in the mechanism. The dye degradation was ascertained by detection of intermediates via mass spectrometry analysis and a possible degradation route was also predicted. The findings in this work provide intriguing opportunities to modify the electronic band structure of spinel ferrites for visible and solar light photocatalytic activity for environmental detoxification.     

A genetic algorithm based back propagation network for simulation of stress–strain response of ceramic-matrix-composites

Ceramic-matrix-composites (CMCs) are fast replacing other materials in many applications where the higher production costs can be offset by significant improvement in performance. In applications such as cutting and forming tools, wear parts in machinery, nozzles, valve seals and bearings, improvement in toughness and hardness translate into longer life. However, the recent resurgence in the field of development of CMCs has been due to their potential use for the Space Transport systems, Combustion engines and other energy conversion systems. The CMCs are ideal structural material for these applications. However, due to their lack of toughness, they are prone to brittle fractures. Therefore, the main consideration in the development of CMCs has been to toughen them. To achieve this, the bi-material interface should be weak and must allow debonding, resulting in crack deflection. In the present work, the stress–strain response of Al₂O₃ (matrix)/SiC (whisker) ceramic composite has been simulated using a back propagation neural network (BPN), which incorporates the effect of interface shear strength (IFS) in the analysis. For efficient and quick training, the weights for the BPN have been obtained by using a genetic algorithm (GA). The GA has been modelled with 150 genes and a chromosome string length of 750. The network simulation is based on the stress–strain response obtained from the finite element analysis. A three noded isoparametric interface element has been employed to model the whisker/matrix interface in finite element analysis. The finite element analysis has been carried out only for a limited number of specimens. However, the simulation model is capable of predicting the stress–strain relationship for a new interface shear strength even with this limited information. Thus, the robustness and the generalisation capability of the neural network model is demonstrated. The development stages of the GA/BPN model such as the preparation of training set, selection of a network configuration, training of the net and a testing scheme, etc., have been addressed at length in this paper.     

Influence of the second phase on the room-temperature tensile and creep deformation mechanisms of α-β titanium alloys: Part I. Tensile deformation

The effects of α and β phase interactions on the room-temperature tensile and creep deformation behavior of α + β titanium alloys with Widmanst?tten microstructures were studied using Ti-6.0 wt pct Mn and Ti-8.1 wt pct V as the model two-phase alloy systems. This article, Part I, deals with tensile deformation. It was found that when the α phase is present as thin (<10-μm) plates in the α + β alloys, significant twinning occurs. No significant twinning was observed in single-phase alloys with the same chemistry and similar grain size. Additionally, the β phase of Ti-8.1 V deforms by stress-induced hexagonal martensite (α′), while only twinning occurs in the single-phase β alloy with the same chemistry. Twinning in the α phase in association with stress-induced martensite (SIM) in the β phase was observed for the first time in a two-phase titanium alloy. This behavior is explained in terms of a number of factors including elastic interaction stresses between the α and β phases, coherency between the α phase and hexagonal martensite, and β phase stability.     

Model-based detection of critical driving situations with fuzzy logic decision making

A precise knowledge about the current driving condition is getting increasingly important for future driver assistance systems like global chassis control or collision avoidance systems for avoiding any critical driving situation. Moreover a precise knowledge about the driving situation can be used in testing, in evaluation, and for comparison of new passenger cars. A two degree of freedom model of vehicle lateral dynamics is used to derive a characteristic velocity stability indicator (CVSI). The CVSI is used to distinguish between different driving and stability conditions (i.e. understeering, oversteering, and neutralsteering). This forms the basis for a driving condition detection system with fixed thresholds. It is then extended to a detection system with fuzzy logic thresholds. The CVSI and the fuzzy systems are compared experimentally using (i) a slalom test drive on an icy road and (ii) a stationary circular test drive on a dry asphalt road.     

Application of aeroelastic modes on nonlinear supersonic panel flutter at elevated temperatures

This paper shows that the use of aeroelastic modes, instead of the traditional in vacuo natural modes, can reduce drastically the number of coupled nonlinear modal equations for the large amplitude nonlinear panel flutter analysis at an arbitrary yawed supersonic flow angle and elevated temperatures. All four types of panel behavior can be predicted and they are flat and stable, aerothermally buckled but dynamically stable, limit cycle oscillations, and chaos.