Intelligent ground vibration prediction in surface mines using an efficient soft computing method based on field data.

Ground vibration induced by blasting operations is considered one of the most common environmental effects of mining projects. A strong ground vibration can destroy buildings and structures, hence its prediction and minimization are of high importance. The aim of this study is to estimate the ground vibration through a hybrid soft computing (SC) method, called RSM-SVR, which comprises two main regression techniques: the response surface model (RSM) and support vector regression (SVR). The RSM-SVR model applies an RSM in the first calibrating process and an SVR in the second calibrating process to improve the accuracy of the ground vibration predictions. The predicted results of an RSM, which are obtained using the input data of problems, are used as the input dataset for the regression process of an SVR. The effectiveness and agreement of the RSM-SVR model were compared to those of an SVR optimized with the particle swarm optimization (PSO) and genetic algorithm (GA), RSM, and multivariate linear regression (MLR) based on several statistical factors. The findings confirmed that the RSM-SVR model was considerably superior to other models in terms of accuracy. The amounts of coefficient of determination (R 2) were 0.896, 0.807, 0.782, 0.752, 0.711, and 0.664 obtained from the RSM-SVR, PSO-SVR, GA-SVR, MLR, SVR, and RSM models, respectively.

Prediction of effluent arsenic concentration of wastewater treatment plants using machine learning and kriging-based models.

This study evaluates the potential of kriging-based (kriging and kriging-logistic) and machine learning models (MARS, GBRT, and ANN) in predicting the effluent arsenic concentration of a wastewater treatment plant. Two distinct input combination scenarios were established, using seven quantitative and qualitative independent influent variables. In the first scenario, all of the seven independent variables were taken into account for constructing the data-driven models. For the second input scenario, the forward selection k-fold cross-validation method was employed to select effective explanatory influent parameters. The results obtained from both input scenarios show that the kriging-logistic and machine learning models are effective and robust. However, using the feature selection procedure in the second scenario not only made the architecture of the model simpler and more effective, but also enhanced the performance of the developed models (e.g., around 7.8% performance enhancement of the RMSE). Although the standard kriging method provided the least good predictive results (RMSE = 0.18 ug/l and NSE=0.75), it was revealed that the kriging-logistic method gave the best performance among the applied models (RMSE = 0.11 ug/l and NSE=0.90).

Reinforcing bar development length modeling using integrative support vector regression model with response surface method: New approach.

Due to the non-uniformity of bond stress distribution, the full bar development length should be tested to validate the development length of the reinforcing bar embedded in concretes. The current study proposed the design of a hybrid artificial intelligence (AI) model using integration of support vector regression (SVR) coupled with response surface method (RSM) for prediction of reinforcement bar development length. Two nonlinear calibrating processes are conducted, RSM is used to connect the input data on the hardening dataset in first stage while SVR is used for determining the nonlinear relation between the hardening dataset and the output development bar stress. 534 pull-out test observations on short unit bar length are used for the modeling. Several physical dimensional properties are incorporated as input attributes for the hybrid predictive model. Stand-alone AI models, empirical formulations, and design codes are used for validation. Parametric analysis is performed to investigate the sensitivity of RSM-SVR model toward as input variables. Results evidenced the capability of the proposed RSM-RVM model to approximate the non-linear relations between physical information and bar development length. The RSM-SVR model proved to be a reliable intelligence model for bar materials design. Additionally, the model was highly consistent in the calibration of the existing design codes for more reliability. In quantitative terms, RSM-SVR model attained minimum root mean square error (RMSE = 25.60 MPa), compared with for example ACI 318-14 modeling design (306.56 MPa) or stand-alone SVR model (90.95 MPa), over the testing phase. The bar development length formulation using RSM-SVR model showed the nonlinear relationship with respect to the influence of the input variables such as transvers bars in development region, reinforcing yield stress, and concrete compressive strength in normal domain.

Time-Dependent Reliability Analysis of Reinforced Concrete Beams Subjected to Uniform and Pitting Corrosion and Brittle Fracture.

Reinforced concrete (RC) beams are basic elements used in the construction of various structures and infrastructural systems. When exposed to harsh environmental conditions, the integrity of RC beams could be compromised as a result of various deterioration mechanisms. One of the most common deterioration mechanisms is the formation of different types of corrosion in the steel reinforcements of the beams, which could impact the overall reliability of the beam. Existing classical reliability analysis methods have shown unstable results when used for the assessment of highly nonlinear problems, such as corroded RC beams. To that end, the main purpose of this paper is to explore the use of a structural reliability method for the multi-state assessment of corroded RC beams. To do so, an improved reliability method, namely the three-term conjugate map (TCM) based on the first order reliability method (FORM), is used. The application of the TCM method to identify the multi-state failure of RC beams is validated against various well-known structural reliability-based FORM formulations. The limit state function (LSF) for corroded RC beams is formulated in accordance with two corrosion types, namely uniform and pitting corrosion, and with consideration of brittle fracture due to the pit-to-crack transition probability. The time-dependent reliability analyses conducted in this study are also used to assess the influence of various parameters on the resulting failure probability of the corroded beams. The results show that the nominal bar diameter, corrosion initiation rate, and the external loads have an important influence on the safety of these structures. In addition, the proposed method is shown to outperform other reliability-based FORM formulations in predicting the level of reliability in RC beams.

Adaptive harmony search algorithm for mechanical performance optimization of properties of particleboard from cotton stalk.

In this study, the optimum conditions for manufacturing particleboard-based waste cotton stalks were evaluated to achieve a good performance of mechanical properties. The response surface methodology (RSM) is used to calibrate the experiment results based on input variables consisting of the weight ratio of melamine formaldehyde to urea-formaldehyde (MU) resins, shelling ratio (SR), and the proportion of cotton particles to poplar particle (CP) in the core layer. An adaptive harmony search (AHS) algorithm is offered to search the optimum constructing conditions of mechanical properties for the composite particleboard using two optimization models. The optimum conditions are evaluated using maximum performance of mechanical properties. Besides, the optimum conditions are searched based on the material cost of the mechanical properties of composite particleboard that are utilized in its constraints. The results showed that the RSM can provide a perfect prediction for the mechanical properties of particleboard. The AHS is successfully applied to optimize the composite conditions. In the first optimization application, the optimal point is obtained for input variables in composite as 21.91% MU, 37.10% SR, and 13.54% CP. However, in the second condition, the optimum conditions are obtained for a good level as 18.32% MU, 51.71% SR, and 8.37% CP in the core layer.

Reliability Analysis of FRP-Confined Concrete at Ultimate using Conjugate Search Direction Method.

In this paper compressive strength and ultimate strain results in the current database of fiber-reinforced polymer (FRP)-confined concrete are used to determine the reliability of their design space. The Lognormal, Normal, Frechet, Gumbel, and Weibull distributions are selected to evaluate the probabilistic characteristics of six FRP material categories. Following this, safety levels of the database are determined based on a probabilistic model. An iterative reliability method is developed with conjugate search direction for evaluating the reliability. The results show that Lognormal and Gumbel distributions provide best probability distribution for model errors of strength and strain enhancement ratios. The developed conjugate reliability method provides improved robustness over the existing reliability methods owing to its faster convergence to stable results. The results reveal that the part of the database containing normal strength concrete (NSC) heavily confined (i.e., actual confinement ratio (flu,a/f'co) > 0.5) by low and normal modulus carbon fibers (i.e., fiber elastic modulus (Ef) ≤ 260 GPa) and moderately confined (i.e., 0.3 ≤ flu,a/f'co ≤ 0.5) by aramid fibers exhibits a very high safety level. The segments of the database with a low and moderate safety level have been identified as i) NSC moderately and heavily confined by higher modulus glass fibers (i.e., Ef > 60 GPa), ii) high strength concrete (HSC) moderately and heavily confined (i.e., flu,a/f'co > 0.3) by glass fibers, iii) HSC lightly confined (i.e., flu,a/f'co ≤ 0.2) by carbon fibers, and iv) HSC lightly confined by aramid fibers. Additional experimental studies are required on these segments of the database before they can be used reliably for design and modeling purposes.

Dynamic Stability Analysis in Hybrid Nanocomposite Polymer Beams Reinforced by Carbon Fibers and Carbon Nanotubes.

The objective of this innovative research is assessment of dynamic stability for a hybrid nanocomposite polymer beam. The considered beam formed by multiphase nanocomposite, including polymer-carbon nanotubes (CNTs)-carbon fibers (CFs). Hence, as to compute the effective material characteristics related to multiphase nanocomposite layers, the Halpin-Tsai model, as well as micromechanics equations are employed. To model the structure realistically, exponential shear deformation beam theory (ESDBT) is applied and using energy methods, governing equations are achieved. Moreover, differential quadrature method (DQM) as well as Bolotin procedures are used for solving the obtained governing equations and the dynamic instability region (DIR) relative to the beam is determined. To extend this novel research, various parameters pinpointing the influences of CNT volume fraction, CFs volume percent, boundary edges as well as the structure's geometric variables on the dynamic behavior of the beam are presented. The results were validated with the theoretical and experimental results of other published papers. The outcomes reveal that increment of volume fraction of CNT is able to shift DIR to more amounts of frequency. Further, rise of carbon fibers volume percent leads to increase the excitation frequency of this structure.

SVR-RSM: a hybrid heuristic method for modeling monthly pan evaporation.

In the present study, a hybrid intelligent model called SVR_RSM, which was extracted using response surface method (RSM) combined by the support vector regression (SVR) approaches was applied for predicting monthly pan evaporation (Epan). This method is established based on two basic calibrating process using RSM and SVR. In the first process, an input data group with two different input variables are used to calibrate the RSM; hence, the calibrating data by RSM in the first process are applied as input database for calibrating the SVR in the second process. Results obtained using the proposed SVR_RSM was compared with those obtained using the RSM, SVR, and the well-known multilayer perceptron neural network (MLPNN) models. Climatic variables including maximum and minimum temperatures (Tmax, Tmin), wind speed (U2), and relative humidity (H%), and the periodicity represented by the month number (α) were selected for predicting the monthly Epan measured with the standard class A evaporation pan. Data was collected at six climatic stations located at the northern East of Algeria. The performances of the proposed models were compared using the RMSE, MAE, modified index of agreement (d), coefficient of correlation (R), and modified Nash and Sutcliffe efficiency (NSE). Using various input combination, the results show that the hybrid SVR_RSM model performed better than all the proposed models. Overall, better accuracy was observed when the model contained the periodicity (α), and it was demonstrated that the best accuracy was obtained using only Tmax and Tmin, coupled with the periodicity.

Understanding the hidden relations between pro- and anti-inflammatory cytokine genes in bovine oviduct epithelium using a multilayer response surface method.

An understanding gene-gene interaction helps users to design the next experiments efficiently and (if applicable) to make a better decision of drugs application based on the different biological conditions of the patients. This study aimed to identify changes in the hidden relationships between pro- and anti-inflammatory cytokine genes in the bovine oviduct epithelial cells (BOECs) under various experimental conditions using a multilayer response surface method. It was noted that under physiological conditions (BOECs with sperm or sex hormones, such as ovarian sex steroids and LH), the mRNA expressions of IL10, IL1B, TNFA, TLR4, and TNFA were associated with IL1B, TNFA, TLR4, IL4, and IL10, respectively. Under pathophysiological + physiological conditions (BOECs with lipopolysaccharide + hormones, alpha-1-acid glycoprotein + hormones, zearalenone + hormones, or urea + hormones), the relationship among genes was changed. For example, the expression of IL10 and TNFA was associated with (IL1B, TNFA, or IL4) and TLR4 expression, respectively. Furthermore, under physiological conditions, the co-expression of IL10 + TNFA, TLR4 + IL4, TNFA + IL4, TNFA + IL4, or IL10 + IL1B and under pathophysiological + physiological conditions, the co-expression of IL10 + IL4, IL4 + IL10, TNFA + IL10, TNFA + TLR4, or IL10 + IL1B were associated with IL1B, TNFA, TLR4, IL10, or IL4 expression, respectively. Collectively, the relationships between pro- and anti-inflammatory cytokine genes can be changed with respect to the presence/absence of toxins, sex hormones, sperm, and co-expression of other gene pairs in BOECs, suggesting that considerable cautions are needed in interpreting the results obtained from such narrowly focused in vitro studies.

An autoregressive logistic model to predict the reciprocal effects of oviductal fluid components on in vitro spermophagy by neutrophils in cattle.

After intercourse/insemination, large numbers of sperm are deposited in the female reproductive tract (FRT), triggering a massive recruitment of neutrophils (PMNs) into the FRT, possibly to eliminate excessive sperm via phagocytosis. Some bovine oviductal fluid components (BOFCs) have been shown to regulate in vitro sperm phagocytosis (spermophagy) by PMNs. The modeling approach-based logistic regression (LR) and autoregressive logistic regression (ALR) can be used to predict the behavior of complex biological systems. We, first, compared the LR and ALR models using in vitro data to find which of them provides a better prediction of in vitro spermophagy in bovine. Then, the best model was used to identify and classify the reciprocal effects of BOFCs in regulating spermophagy. The ALR model was calibrated using an iterative procedure with a dynamical search direction. The superoxide production data were used to illustrate the accuracy in validating logit model-based ALR and LR. The ALR model was more accurate than the LR model. Based on in vitro data, the ALR predicted that the regulation of spermophagy by PMNs in bovine oviduct is more sensitive to alpha-1 acid glycoprotein (AGP), PGE2, bovine serum albumin (BSA), and to the combination of AGP or BSA with other BOFCs.