The failure probability of structures are rather small and therefore calculation of structural reliability generally has a high computational cost. In order to reduce computational costs, this articles proposes a hybrid approach based on combination of the LEAST SQUARES SUPPORT VECTOR regression and two advanced Monte Carlo methods: importance sampling and Latin hypercube sampling. Two frames and one truss example are used to evaluate the performance of the proposed algorithm. Results demonstrate that proposed method provides an accurate estimation of failure probability and that the computational costs are lower than those of other methods.

Iron is an essential element used as supplement in different dosage-forms. Different time and expenditure-consuming methods introduced for detection and determination of elemental ions such as Atomic Absorption Spectroscopy. In this research, two different and routine methods containing ATR-IR and atomic absorption were applied to define the amount of iron in 198 samples containing different concentrations of commercial iron drops and syrups and the output data of the methods was transferred to chemometric model to compare the accuracy and robustness of the methods. By applying this mathematical model, in addition to the confirmation of ATR-IR (a time and energy-saving method) as a replacement of Atomic Absorption Spectroscopy to produce the same results, chemometrical model was used to evaluate the output data in a faster and easier method. At first, ATR-IR spectra data converted to normal matrix by SNV preprocessing approach. Then, a relationship between iron concentrations achieved by AAS and ATR-IR data was established using PLS-(LS-SVM). Consequently, model was able to predict ~99% of the samples with low error-values (root mean square-error of cross-validation equal to 0. 98). Y-permutation test performed to confirm that the model was not assessed accidentally. Although, chemometric methods for detection of some heavy metals have been reported in the literature, combination of PLS-(LS-SVM) with ATR-IR was not cited. In this study a fast and robust method for iron assay was suggested. As a result, ATR-IR can be a suitable method in detection and qualification of iron-content in pharmaceutical dosage forms with less energy-consumption but similar accuracy.

A hybrid meta-heuristic optimization method is introduced to efficiently find the optimal shape of concrete gravity dams including dam-water-foundation rock interaction subjected to earthquake loading. The hybrid meta-heuristic optimization method is based on a hybrid of gravitational search algorithm (GSA) and particle swarm optimization (PSO), which is called GSA-PSO. The operation of GSA-PSO includes three phases. In the first phase, a preliminary optimization is accomplished using GSA as local search. In the second phase, an optimal initial swarm is produced using the optimum result of GSA.Finally, PSO is employed to find the optimum design using the optimal initial swarm. In order to reduce the computational cost of dam analysis subject to earthquake loading, weighted LEAST SQUARES SUPPORT VECTOR MACHINE (W(LS-SVM)) is employed to accurately predict dynamic responses of gravity dams. Numerical results demonstrate the high performance of the hybrid meta-heuristic optimization for optimal shape design of concrete gravity dams. The solutions obtained by GSA-PSO are compared with those of GSA and PSO. It is revealed that GSA-PSO converges to a superior solution compared to GSA and PSO, and has a lower computation cost.

This paper proposes the LEAST-SQUARES SUPPORT VECTOR MACHINE ((LS-SVM)) as an intelligent method applied on absorption spectra for the simultaneous determination of paracetamol (PCT), caffeine (CAF), and ibuprofen (IB) in Novafen. The signal to noise ratio (S/N) increased. Also, In the (LS-SVM) model, Kernel parameter (𝜎 2) and capacity factor (C) were optimized. Excellent prediction was shown using (LS-SVM), with lower root mean square error (RMSE) and relative standard deviation (RSD). In addition, Regression coefficient (R2), correlation coefficient (r), and mean recovery (%) of this method obtained for PCT, CAF, and IB. (LS-SVM) / spectrophotometry method is reliable for simultaneous quantitative analysis of components in commercial samples. The results obtained from analyzing the real sample by the proposed method compared to the high-performance liquid chromatography (HPLC) as a reference method. One-way analysis of variance (ANOVA) test at 95% confidence level used and results showed that there was no significant difference between suggested and reference methods.

In Wire and arc additive manufacturing (WAAM) based on Gas metal arc welding (GMAW) is one of the methods of manufacturing metal layer by layer. One of this method's basic steps is predicting the welding geometry created in each welding step. In the current research, an experimental study was conducted in this field considering the effective parameters of welding geometry. For this purpose, three parameters of voltage, welding speed, and wire feeding speed were considered as effective parameters on the welding geometry of the process. The width and height of the weld bead was selected as the answer according to the type and application of the research. The LEAST SQUARES SUPPORT VECTOR MACHINE was used to model the welding geometry in the process. The results obtained from the regression (R2) of train, test, validation, and total were 0. 945, 0. 793, 0. 894, and 0. 881 respectively. The comparison between the experimental data and the model data shows the significance of the proposed model.

The shear strength of deep beams of the reinforced concrete beams depends on the mechanical and geometrical parameters properties of the beam. Accurate estimation of shear strength in deep reinforced concrete deep beams of the reinforced concrete is one of the major issues in the design of engineering structures. However, some methods proposed to determine the shear strength in deep reinforced concrete beams do not have high accuracy. One method to accurately estimate shear strength is to use artificial intelligence (AI). Artificial intelligence has many different methods, one of which is the use of artificial intelligence based on the SUPPORT VECTOR MACHINE method. In this study, the weighted LEAST SQUARES SUPPORT VECTOR MACHINE (W(LS-SVM)), which is a relatively new and efficient method for predicting the shear capacity of reinforced concrete beams, has been used. In this study, a database containing experimental results on deep reinforced concrete beams was first collected. Then, after determining the input and output parameters using a training process in W(LS-SVM) method and using a part of the collected data, a model was developed to predict the shear strength of deep reinforced concrete beams. In order to determine the accuracy of the W(LS-SVM) method, the results were compared with those obtained by other AI methods and different regulations. Statistical analysis showed that W(LS-SVM) has the best performance in terms of statistical evaluation parameters (R^2 = 0. 9887, RMSE = 0. 107, MAE = 0. 478 and MAPE = 9. 48%) compared to the other methods.

In the present study, precipitation in six stations of Karun3 basin is downscaled by using the hybrid of LEAST SQUARES SUPPORT VECTOR MACHINE and whale optimization algorithm (LSSVM-WOA), K nearest neighbor (KNN), and artificial neural network (ANN). For downscaling precipitation, first, the days of year are classified into wet and dry days by using MARS and M5 algorithms. Then, the amount of precipitation for wet days is estimated by using each of LSSVM-WOA, KNN and ANN methods. Based on the findings, MARS algorithm is superior over M5 algorithm. Based on the mean precipitation in the six stations, ANN is a little bit better than LSSVM-WOA (0.5 percent more accurate). While, by regarding the mean of standard deviations, the Nash-Sutcliff for Ann is up to 5.04 percent more accurate than LSSVM-WOA. Eventually, the amount of precipitation is predicted based on the CanESM2 model under RCP2.6, RCP4.5 and RCP8.5 scenarios for 2020-2040 and 2070-2100 periods. Based on the results of applying LSSVM-WOA, the precipitation in each three scenarios is decreased compared to the base period. Maximum decrease of precipitation (18%) is calculated by RCP8.5 for 2070-2100 period. Minimum decrease of precipitation (1%) is related to RCP2.6 scenario for 2020-2040 future period. But, the precipitation variation amount that is predicted by ANN is between -43 and 72 percent. Therefore, the results of LSSVM-WOA are more reliable and less uncertain