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.

     

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.     

Synthesis,Corrosion and Bioactivity Evaluation of Gelatin/Silicon and Magnesium Co-Doped Fluorapatite Nanocomposite Coating Applied on AZ31 Mg Alloy

In this study, a nano-composite composed of gelatin as the matrix and Si-Mg-FA nano-particles as an additive was deposited on the AZ31 Mg alloy via dip coating method. In addition, a coating composed of MgO, MgSiO₃ and Mg₂SiO₄ phases was applied on the AZ31 Mg alloy by anodizing process. It was found that the Nano-composite coating with a uniform distribution of nano-particles within the gelatin matrix with the thickness of about 9 µm was dense, crack-free and uniform whereas the surface of anodized layer was relatively coarse due to the presence of flaws and micro-cracks. The surface morphology, EDS analysis and FTIR results revealed the ability of nano-composite coated specimen to form the bone-like apatite. Due to the presence of aforementioned phases and special surface features, the anodized specimen possessed higher and lower corrosion resistance than uncoated and nano-composite coated specimens, respectively. The passive coating resistances (R_CT) of nano-composite, anodized specimen and uncoated samples were 2164, 1449 and 1024 Ω cm², respectively.     

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     

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     

Preparation of New Fibers on the Basis of Codeine Imprinted Polymer

A monolithic copolymer of methacrylic acid-ethylene glycol dimathacrylate as a fiber with 2 cm length and 0.3 mm diameter, containing codeine (CO) template was prepared through thermal radical co-polymerization procedure. This fiber is a robust recognition material capable of mimicking natural systems, combined with solid-phase micro-extraction (SPME) and gas chromatography/mass spectrometry for the extraction of trace CO from various street-drug samples. Effective experimental parameters such as Methacrylic Acid (MAA), Ethylene glycol dimethacrylate (EDMA), and CO proportions, nature, and dimension of mold, copolymerization time and temperature were optimized. Experimental studies such as scanning electron microscopy (SEM) reveal that highly homogenate fiber was achieved that can preciously be used for the above mentioned goals.     

Biomimetic Natural Killer Membrane Camouflaged Polymeric Nanoparticle for Targeted Bioimaging

In the present study, a biomimetic nanoconstruct (BNc) with a multimodal imaging system is engineered using tumor homing natural killer cell membrane (NKM), near‐infrared (NIR) fluorescent dye, and gadolinium (Gd) conjugate‐based magnetic resonance imaging contrast agent onto the surface of a polymeric nanoparticle. The engineered BNc is 110 ± 20 nm in size and showed successful retention of NKM proteins. The magnetic properties of the BNc are found to be tunable from 2.1 ± 0.17 to 5.3 ± 0.5 mm ^?1 s^?1 under 14.1 T, by adjusting the concentration of Gd‐lipid conjugate onto the surface of the BNc. Confocal imaging and cell sorting analysis reveal a distinguishable cellular interaction of the BNc with MCF‐7 cells in comparison to that of bare polymeric nanoparticles suggesting the tumor homing properties of NKM camouflage system. The in vitro cellular interaction results are further confirmed by in vivo NIR fluorescent tumor imaging and ex vivo MR imaging, respectively. Pharmacokinetics and biodistribution analysis of the BNc show longer circulation half‐life (≈9.5 h) and higher tumor accumulation (10% of injected dose) in MCF‐7 induced tumor‐bearing immunodeficient NU/NU nude mice. Owing to the proven immunosurveillance potential of NK‐cell in the field of immunotherapy, the BNc engineered herein would hold promises in the design consideration of nanomedicine engineering.