An optimized ANN model based on genetic algorithm for predicting ripping production

Due to the environmental constraints and the limitations on blasting, ripping as a ground loosening and breaking method has become more popular in both mining and civil engineering applications. As a result, a more applicable rippability model is required to predict ripping production (Q) before conducting such tests. In this research, a hybrid genetic algorithm (GA) optimized by artificial neural network (ANN) was developed to predict ripping production results obtained from three sites in Johor state, Malaysia. It should be noted that the mentioned hybrid model was first time applied in this field. In this regard, 74 ripping tests were investigated in the studied areas and the relevant parameters were also measured. A series of GA–ANN models were conducted in order to propose a hybrid model with a higher accuracy level. To demonstrate the performance capacity of the hybrid GA–ANN model, a pre-developed ANN model was also proposed and results of predictive models were compared using several performance indices. The results revealed higher accuracy of the proposed hybrid GA–ANN model in estimating Q compared to ANN technique. As an example, root-mean-square error values of 0.092 and 0.131 for testing datasets of GA–ANN and ANN techniques, respectively, express the superiority of the newly developed model in predicting ripping production.

     

A Review on Wearable Electrospun Polymeric Piezoelectric Sensors and Energy Harvesters

In recent years, wearable sensors and energy harvesters have shown great potential for a wide range of applications in personalized healthcare, robotics, and human–machine interfaces. Among different types of materials used in wearable electronics, piezoelectric materials have gained enormous attention due to their exclusive ability to harvest energy from ambient sources. Piezoelectric materials can be utilized as sensing elements in wearable sensors while harvesting biomechanical energy. Electrospun piezoelectric polymer nanofibers are extensively investigated due to their high flexibility, ease of processing, biocompatibility, and higher piezoelectric property (in contrast to their corresponding cast films). However, as compared to piezoceramic materials, they mostly exhibit relatively lower piezoelectric coefficients. Therefore, considerable efforts have been devoted to improving the piezoelectricity of electrospun polymer nanofibers recently, resulting in significant advances. This review presents a broad overview of these advances including new material, structure designs as well as new strategies to enhance piezoelectricity of electrospun polymer nanofibers. The challenges in achieving high mechanical performance as well as high piezoelectricity are particularly discussed. The main motivation of this review is to examine these challenges and highlight effective approaches to achieving high-performance piezoelectric sensors and energy harvesters for wearable technologies.     

Effect of drainage channel dimensions on the performance of wave-plate mist eliminators

We investigated the effects of drainage channel dimensions on droplet removal efficiency and pressure drop of the gas droplet flow in a wave-plate mist eliminator. Droplet dispersion in turbulent gas flows is numerically simulated using eddy interaction model (EIM) and Eulerian-Lagrangian method. Reynolds stress transport model (RSTM) with enhanced wall treatment and shear stress transport (SST) k-ω model are used for simulating the turbulent airflow. Comparison between the numerical simulations and available experimental data shows that eddy lifetime constant (C _L ) can affect the results significantly, and by selecting suitable values of the eddy lifetime constant, both turbulence models give reasonable predictions of droplet removal efficiency. Simulations of gas droplet flow in the eliminators with various drainage channel dimensions show that the drainage channel length (L _DC ) has a greater effect on droplet removal efficiency than the drainage channel width (W _DC ).     

Fabrication of Nano/Ultra-Fine Grained IF Steel via SPD Processes: a Review

The grain size of polycrystalline materials plays a major role in dictating many critical properties including the strength and resistance to plastic flow. Nano/ultra-fine grained steels have the potential to exhibit outstanding physical, mechanical and chemical properties, which could, in principle, lead to new applications and novel technologies. Interstitial free (IF) steels in the coarse-grained condition possesses high ductility but low yield strength due to the decrease in the solid solution hardening effect of the interstitial atoms. Enhancing the strength with adequate ductility may increase the potential applications of IF steel sheets in new applications like those in aviation or defense industries. In fact, the overall trend in the development of IF steels are towards the high strength variety that will allow weight saving through down gauging. Considering the monophase microstructure of IF steel, strengthening methods to enhance its mechanical properties are limited and grain refinement seems to be the most feasible method. The most attractive method for the production of nano/ultra-fine grained IF steel is severe plastic deformation (SPD) processes. Therefore, this review is concerned with the production of nano/ultra-fine IF steel by using SPD methods such as accumulative roll bonding and equal channel angular pressing processes.     

硅含量对TIG焊Al-Si/SiCp复合层包覆A380铝合金显微组织的影响

采用钨极惰性气体（TIG）在铸态A380铝合金表面制备复合涂层。将Al,Si和SiC粉末混合物与硅酸钠溶液混合后涂覆在基材上,采用TIG焊进行表面熔化,在基体表面制备Al-SiC涂层。采用XRD、SEM和EDS研究显微组织的变化,采用显微硬度和滑动磨损试验研究包覆层的性能。结果表明,SiC粒子均匀分布在树枝状的铝基体中。加入过量的硅造成包覆层共晶和粗大硅粒子的形成,从而导致包覆层具有较高的硬度和耐磨性。     

Genetic programing and non-linear multiple regression techniques to predict backbreak in blasting operation

In addition to all benefits of blasting in mining and civil engineering applications, it has some undesirable environmental impacts. Backbreak is an unwanted phenomenon of blasting which can cause instability of mine walls, decreasing efficiency of drilling, falling down of machinery, etc. Recently, the use of new approaches such as artificial intelligence (AI) is greatly recommended by many researchers. In this paper, a new AI technique namely genetic programing (GP) was developed to predict BB. To prepare a sufficient database, 175 blasting works were investigated in Sungun copper mine, Iran. In these operations, the most influential parameters on BB including burden, spacing, stemming length, powder factor and stiffness ratio were measured and used to develop BB predictive models. To demonstrate capability of GP technique, a non-linear multiple regression (NLMR) model was also employed for prediction of BB. Value account for (VAF), root mean square error (RMSE) and coefficient of determination (R ²) were used to control the capacity performance of the predictive models. The performance indices obtained by GP approach indicate the higher reliability of GP compared to NLMR model. RMSE and VAF values of 0.327 and 97.655, respectively, for testing datasets of GP approach reveal the superiority of this model in predicting BB, while these values were obtained as 0.865 and 81.816, respectively, for NLMR model.     

Rock strength assessment based on regression tree technique

Uniaxial compressive strength (UCS) is one of the most important parameters for investigation of rock behaviour in civil and mining engineering applications. The direct method to determine UCS is time consuming and expensive in the laboratory. Therefore, indirect estimation of UCS values using other rock index tests is of interest. In this study, extensive laboratory tests including density test, Schmidt hammer test, point load strength test and UCS test were conducted on 106 samples of sandstone which were taken from three sites in Malaysia. Based on the laboratory results, some new equations with acceptable reliability were developed to predict UCS using simple regression analysis. Additionally, results of simple regression analysis show that there is a need to propose UCS predictive models by multiple inputs. Therefore, considering the same laboratory results, multiple regression (MR) and regression tree (RT) models were also performed. To evaluate performance prediction of the developed models, several performance indices, i.e. coefficient of determination (R ²), variance account for and root mean squared error were examined. The results indicated that the RT model can predict UCS with higher performance capacity compared to MR technique. R ² values of 0.857 and 0.801 for training and testing datasets, respectively, suggests the superiority of the RT model in predicting UCS, while these values are obtained as 0.754 and 0.770 for MR model, respectively.     

Settlement prediction of the rock-socketed piles through a new technique based on gene expression programming

The settlement design of bored piles socketed into rock has received considerable attention. Although many design methods of pile settlement are recommended in the literature, proposing new/practical technique(s) with higher performance prediction is of advantage. A new model based on gene expression programming (GEP) is presented in this paper for predicting the settlement of the rock-socketed pile. To do this, 96 piles socketed in different types of rock (mostly granite) as part of the Klang Valley Mass Rapid Transit project, Malaysia, were studied. In order to propose a predictive model with higher performance prediction, a series of GEP analyses were conducted using the most important factors on pile settlement, i.e. ratio of length in soil layer to length in rock layer, ratio of total length to diameter, uniaxial compressive strength, standard penetration test and ultimate bearing capacity. For comparison purpose, using the same dataset, linear multiple regression (LMR) technique was also performed. After developing the equations, their prediction performances were checked through several performance indices. The results demonstrated the feasibility of GEP-based predictive model of settlement. Coefficients of determination (CoD) values of 0.872 and 0.861 for training and testing datasets of GEP equation, respectively, show superiority of this model in predicting pile settlement while these values were obtained as 0.835 and 0.751 for the LMR model. Moreover, root mean square error (RMSE) values of (1.293 and 1.656 for training and testing) and (1.737 and 1.767 for training and testing) were achieved for the developed GEP and LMR models, respectively.

     

A particle swarm–BFGS algorithm for nonlinear programming problems

This article proposes a hybrid optimization algorithm based on a modified BFGS and particle swarm optimization to solve medium scale nonlinear programs. The hybrid algorithm integrates the modified BFGS into particle swarm optimization to solve augmented Lagrangian penalty function. In doing so, the algorithm launches into a global search over the solution space while keeping a detailed exploration into the neighborhoods. To shed light on the merit of the algorithm, we provide a test bed consisting of 30 test problems to compare our algorithm against two of its variations along with two state-of-the-art nonlinear optimization algorithms. The numerical experiments illustrate that the proposed algorithm makes an effective use of hybrid framework when dealing with nonlinear equality constraints although its convergence cannot be guaranteed.     

Temperature of a photovoltaic module under the influence of different environmental conditions – experimental investigation

The climate changes affect photovoltaic (PV) module temperature significantly. The module temperature is one of the most important factors that influence the PV module efficiency and a deep analysis of PV module temperature will aid in better understanding of the environmental influences on the PV module performance. The module temperature depends on many parameters such as solar radiation, ambient temperature, air humidity, speed and direction of the wind, PV module orientation, dust and sand deposition on PV module, and PV module materials. An experimental research was conducted to investigate the effect of these factors on the PV module temperature in the Renewable Energy Laboratory of the Graduate University of Advanced Technology in Iran. The results of this study highlighted that the deposited dust over the PV module surface increases the module temperature and this consequently decreases the PV module power. It was also revealed that a combination of the temperature increase and the incident solar radiation decrease due to the dust deposition over the PV module enhances significantly the module power reduction.