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.
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 (R2), 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. 相似文献
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 (RCT) of nano-composite, anodized specimen and uncoated samples were 2164, 1449 and 1024 Ω cm2, respectively. 相似文献
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. 相似文献
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. 相似文献