Fuyao Yan^{1, #}, Yu hin Chan^2,#, Abhinav Saboo³ , Jiten Shah⁴, Gregory B. Olson^{1, 3}, Wei Chen^{2, *}

CMES-Computer Modeling in Engineering & Sciences, Vol.117, No.3, pp. 343-366, 2018, DOI:10.31614/cmes.2018.04452

Abstract Predicting the mechanical properties of additively manufactured parts is often a tedious process, requiring the integration of multiple stand-alone and expensive simulations. Furthermore, as properties are highly location-dependent due to repeated heating and cooling cycles, the properties prediction models must be run for multiple locations before the part-level performance can be analyzed for certification, compounding the computational expense. This work has proposed a rapid prediction framework that replaces the physics-based mechanistic models with Gaussian process metamodels, a type of machine learning model for statistical inference with limited data. The metamodels can predict the varying properties… More >

Qiang Tong¹, Qingwen Ren^{1, *}, Lei Shen ², Linfei Zhang ², Yin Yang³

CMES-Computer Modeling in Engineering & Sciences, Vol.114, No.1, pp. 33-58, 2018, DOI:10.3970/cmes.2018.114.033

Abstract In this article, the post-peak softening stage of the constitutive relation and the elastic stiffness degradation of concrete are investigated, and a highly reasonable constitutive relation curve is proposed. At the material level, the energy change in the concrete failure process is studied based on the different stress-strain curves of concrete under uniaxial tension and compression. The concrete failure criterion based on elastic strain energy density is deemed suitable and consistent with the experimental phenomena. The hysteresis phenomenon (lags behind the peak strength) is also discussed. At the structure level, the strength reduction method is More >

Swarnalata Sahoo^1,2*, Hemjyoti Kalita¹, Smita Mohanty^1,2, Sanjay Kumar Nayak^1,2

Journal of Renewable Materials, Vol.6, No.1, pp. 117-125, 2018, DOI:10.7569/JRM.2017.634155

Abstract In the current work, we have synthesized vegetable oil-based polyurethane-OMMT clay nanocomposite (PUNC) adhesive with the incorporation of different wt% of organically modified nanoclay (1 to 5 wt%) into the biobased polyurethane (PU) matrix through in-situ polymerization process via ultrasonication method. At the initial stage, PU adhesive was prepared using polyol and partially biobased aliphatic isocyanate, wherein polyol was derived from the transesterified castor oil using ethylene glycol. The formation of PU and PUNC adhesive was confirmed using Fourier transform infrared (FTIR) spectroscopy analysis. The tensile strength of PU with different wt% of nanoclay was… More >

Chengquan Wang¹, Yun Zou^1,*, Tianqi Li¹, Jie Ding¹, Xiaoping Feng¹, Tiange Lei¹

Structural Durability & Health Monitoring, Vol.12, No.2, pp. 99-110, 2018, DOI:10.3970/sdhm.2018.02061

Abstract A kind of concrete-filled lattice rectangular steel tube (CFLRST) column was put forward. The numerical simulation was modeled to analyze the mechanical characteristic of CFLRST column. By comparing the load-deformation curves from the test results, the rationality and reliability of the finite element model has been confirmed, moreover, the change of the section stiffness and stress in the forcing process and the ultimate bearing capacity of the column were analyzed. Based on the model, the comparison of ultimate bearing capacity and ductility between CFLRST column and reinforced concrete (RC) column were also analyzed. The results More >

Wenyan Liu¹, Xiangyang Luo^1,*, Yimin Liu¹, Jianqiang Liu², Minghao Liu¹, Yun Q. Shi³

CMC-Computers, Materials & Continua, Vol.55, No.1, pp. 71-93, 2018, DOI:10.3970/cmc.2018.055.071

Abstract Precise localization techniques for indoor Wi-Fi access points (APs) have important application in the security inspection. However, due to the interference of environment factors such as multipath propagation and NLOS (Non-Line-of-Sight), the existing methods for localization indoor Wi-Fi access points based on RSS ranging tend to have lower accuracy as the RSS (Received Signal Strength) is difficult to accurately measure. Therefore, the localization algorithm of indoor Wi-Fi access points based on the signal strength relative relationship and region division is proposed in this paper. The algorithm hierarchically divide the room where the target Wi-Fi AP… More >

G. Jayaprakash¹, M. P. Muthuraj^2,*

CMC-Computers, Materials & Continua, Vol.54, No.1, pp. 83-102, 2018, DOI:10.3970/cmc.2018.054.083

Abstract This paper discusses the applicability of relevance vector machine (RVM) based regression to predict the compressive strength of various self compacting concrete (SCC) mixes. Compressive strength data various SCC mixes has been consolidated by considering the effect of water cement ratio, water binder ratio and steel fibres. Relevance vector machine (RVM) is a machine learning technique that uses Bayesian inference to obtain parsimonious solutions for regression and classification. The RVM has an identical functional form to the support vector machine, but provides probabilistic classification and regression. RVM is based on a Bayesian formulation of a More >

Linyue Guan, Mingjun Pang, Hui Wang, Xuedong Jiang, Yongbin Zhang^*

Frontiers in Heat and Mass Transfer, Vol.9, pp. 1-5, 2017, DOI:10.5098/hmt.9.12

Abstract The paper presents an analysis for the relative slip amount related to the fluid-wall interfacial shear strength in a nano channel formed by two parallel smooth solid planes sliding against one another. A closed-form equation formulation for the relative slip amount was obtained. It was shown that the relative slip amount is constant in the inlet zone of the channel, where the interfacial slippage occurs. This substantiates the assumption made in the analysis of this kind of channel as shown in the study by Zhang (2015). It was also shown that with the increases of More >

Kun Li, Xiaona Li, Jing Luo, Jingjing Li, Qiang Gao^*, Jianzhang Li^*

Journal of Renewable Materials, Vol.5, Suppl.1, pp. 31-38, 2017, DOI:10.7569/JRM.2017.634131

Abstract The objective of this study was to use soybean meal and 1,4-butanediol diglycidyl ether (BDDE) to develop a highly water-resistant, soy-based bioadhesive for plywood fabrication. The physical properties and performance characteristics of the resulting adhesive, including solid content, viscosity, water resistance, crystallinity, fracture morphology, thermal behavior, and cracks, were evaluated. The proposed adhesive was compared against the traditional soy adhesive with polyamidoamine-epichlorohydrin (PAE). Results showed that adding 8 g of BDDE into the adhesive formulation improved the solid content up to 32.83% and reduced the viscosity to 27340 mPa·s. The wet shear strength of plywood More >

M. Himabindu¹, K. Kamalakar², MSL Karuna², Aruna Palanisamy^1*

Journal of Renewable Materials, Vol.5, No.2, pp. 124-131, 2017, DOI:10.7569/JRM.2016.634137

Abstract Rigid polyurethane biofoams were prepared from karanja polyol which was derived by ring-opening reaction of epoxidized karanja oil. The polyol, which had a hydroxyl value of 186 mg KOH/g, was thoroughly characterized and the structure confirmed by spectral techniques. The foam formulations were developed to achieve shrinkage-free foams with water used as the blowing agent. The resulting foams were characterized for their mechanical properties like density, compression strength and flexural strength. The densities and mechanical properties, such as compression and flexural strength, varied with the amount of methylene diphenyl diisocyanate (MDI) for a fixed amount More >

Susom Dutta¹, A. Ramach,ra Murthy², Dookie Kim³, Pijush Samui⁴

CMC-Computers, Materials & Continua, Vol.53, No.2, pp. 157-174, 2017, DOI:10.3970/cmc.2017.053.167

Abstract In the present scenario, computational modeling has gained much importance for the prediction of the properties of concrete. This paper depicts that how computational intelligence can be applied for the prediction of compressive strength of Self Compacting Concrete (SCC). Three models, namely, Extreme Learning Machine (ELM), Adaptive Neuro Fuzzy Inference System (ANFIS) and Multi Adaptive Regression Spline (MARS) have been employed in the present study for the prediction of compressive strength of self compacting concrete. The contents of cement (c), sand (s), coarse aggregate (a), fly ash (f), water/powder (w/p) ratio and superplasticizer (sp) dosage More >