GA-SVR: a novel hybrid data-driven model to simulate vertical load capacity of driven piles

Piles are widely applied to substructures of various infrastructural buildings. Soil has a complex nature; thus, a variety of empirical models have been proposed for the prediction of the bearing capacity of piles. The aim of this study is to propose a novel artificial intelligent approach to predict vertical load capacity of driven piles in cohesionless soils using support vector regression (SVR) optimized by genetic algorithm (GA). To the best of our knowledge, no research has been developed the GA-SVR model to predict vertical load capacity of driven piles in different timescales as of yet, and the novelty of this study is to develop a new hybrid intelligent approach in this field. To investigate the efficacy of GA-SVR model, two other models, i.e., SVR and linear regression models, are also used for a comparative study. According to the obtained results, GA-SVR model clearly outperformed the SVR and linear regression models by achieving less root mean square error (RMSE) and higher coefficient of determination (R²). In other words, GA-SVR with RMSE of 0.017 and R² of 0.980 has higher performance than SVR with RMSE of 0.035 and R² of 0.912, and linear regression model with RMSE of 0.079 and R² of 0.625.

     

Highly efficient synthesis of coumarin derivatives in the presence of H14[NaP5W30O110] as a green and reusable catalyst

A variety of heteropolyanions including: Keggin, Dawson, Preyssler, mixed addenda and sandwich types, catalyzed the formation of 4-methylnaphtho-(1,2-b)-pyran-2-one (coumarin) from the condensation of α-naphthol and ethylacetoacetate in a solvent free system and under heating conditions. Our data vividly indicate that sodium30–tungsto pentaphosphate, [NaP₅W₃₀O₁₁₀]¹⁴⁻, which so-called Preyssler’s anion, with high hydrolytic (pH 0–12) and thermal stability is the catalyst of choice. This catalyst catalyzed the synthesis of other coumarin derivatives in high yields and good selectivity.     

A new combination of artificial neural network and <Emphasis Type="Italic">K</Emphasis>-nearest neighbors models to predict blast-induced ground vibration and air-overpressure

Blasting operation is widely used method for rock excavation in mining and civil works. Ground vibration and air-overpressure (AOp) are two of the most detrimental effects induced by blasting. So, evaluation and prediction of ground vibration and AOp are essential. This paper presents a new combination of artificial neural network (ANN) and K-nearest neighbors (KNN) models to predict blast-induced ground vibration and AOp. Here, this combination is abbreviated using ANN-KNN. To indicate performance of the ANN-KNN model in predicting ground vibration and AOp, a pre-developed ANN as well as two empirical equations, presented by United States Bureau of Mines (USBM), were developed. To construct the mentioned models, maximum charge per delay (MC) and distance between blast face and monitoring station (D) were set as input parameters, whereas AOp and peak particle velocity (PPV), as a vibration index, were considered as output parameters. A database consisting of 75 datasets, obtained from the Shur river dam, Iran, was utilized to develop the mentioned models. In terms of using three performance indices, namely coefficient correlation (R ²), root mean square error and variance account for, the superiority of the ANN-KNN model was proved in comparison with the ANN and USBM equations.     

Dynamic mechanical behavior and nanostructure morphology of hyperbranched‐modified polypropylene blends

Polypropylene (PP) was modified utilizing two types of polyesteramide‐based hyperbranched polymers (amphiphilic PS and hydrophilic PH). A maleicanhydride‐modified PP (PM) was used as a reactive dispersing agent to enhance the modification by grafting the hyperbranched polymers onto the PP chains. Pure PP, two different non‐reactively modified samples, i.e. excluding PM, and two different reactively modified samples, i.e. including PM, were studied. Investigating the morphology of the samples was performed by scanning electron microscopy. To follow the effect of the modification on the dynamic mechanical properties, dynamic mechanical analysis experiments both in the melt (rheometric mechanical spectrometry) and in solid state (dynamic mechanical thermal analysis) were carried out. In the next step, the nanocrystalline structure of the samples was studied by small angle X‐ray scattering (SAXS) in two different modes, i.e. static and recrystallization. Hundreds of SAXS patterns were analyzed automatically using procedures written in PV‐WAVE image‐processing software. The chord distribution function (CDF) was calculated and the long period (l_p) of the crystal lamellae was extracted from the CDFs. The rheometric mechanical spectrometry results show that both hyperbranched polymers decrease complex viscosity η^* and enhance liquid‐like behavior. This happens more significantly when PM is included. The dynamic mechanical thermal analysis results reveal that T_g decreases when PS and PH are added. In the reactively modified samples this reduction is compensated most probably because of the crosslinked structure formed through the grafting reaction between the hyperbranched polymers and PM. Such structure is confirmed by SAXS data and calculated CDFs in the recrystallization mode. Static SAXS data also show enhancement in the crosshatched morphology of the crystalline lamellae of PP for reactively modified samples compared with non‐reactively modified samples. © 2013 Society of Chemical Industry     

Highly efficient synthesis of benzopyranopyridines via ZrP2O7 nanoparticles catalyzed multicomponent reactions of salicylaldehydes with malononitrile and thiols

ZrP₂O₇ nanoparticles as an efficient catalyst have been used for the preparation of benzopyrano[2,3-b]pyridines from the four-component condensation reaction of salicylalde-hydes, thiols, and 2 equiv. of malononitrile under reflux conditions in ethanol in excellent yields and short reaction times.     

Dynamically vulcanized polypropylene/ethylene‐propylene diene monomer/organoclay nanocomposites: Effect of mixing sequence on structural,rheological, and mechanical properties

Dynamically vulcanized thermoplastic elastomer (TPE) nanocomposites based on polypropylene (PP), ethylene‐propylene diene monomer (EPDM) and cloisite 15A were prepared via direct melt mixing in a co‐rotating twin‐screw extruder. The mixing process was carried out with optimized processing parameters (barrel temperature = 180°C; screw speed = 150 rpm; and feeding rate = 0.2 kg/hr). The formulation used to prepare the nanocomposites was fixed to 75/20/5 (PP/EPDM/Cloisite^©15A), expressed in mass fraction. Effect of mixing sequence on the properties of vulcanized and unvulcanized (TPE) nanocomposites prepared under similar conditions was investigated using X‐ray diffraction (XRD) and a tensile testing machine. Results showed that the sequence of mixing does affect the properties of final TPE nanocomposites. Accordingly, nanocomposite samples prepared through mixing the preblended PP/clay masterbatch with EPDM phase, show better clay dispersion within the polymer matrix. J. VINYL ADDIT. TECHNOL., 22:320–325, 2016. © 2014 Society of Plastics Engineers