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CONTENTS
Volume 29, Number 3, March 2022
 


Abstract
To investigate the flexural stiffness of the steel-composite beam, the contributions of the concrete slab and steel beam to the stiffness were considered separately. The method for flexural stiffness of the composite beam, considering the stiffness of the concrete slab and steel beam, was proposed in this paper. In addition, finite element models of the composite beams were established and validated. Parametric analyses were carried out to study the effects of different parameters on the neutral axis distance reduction factors of the concrete slab and steel beam. Afterward, the neutral axis distance reduction factors were fitted, and the stiffness combination coefficients of the two parts were solved. Based on the stiffness combination coefficients, the flexural stiffness of the composite beam can be obtained. The proposed method was validated by the tested and analyzed results. The method has a simple form and high accuracy in predicting the flexural stiffness of the steel-concrete composite beam, even though the degree of shear connection is less than 0.5.

Key Words
combination coefficient; degree of shear connection; flexural stiffness; neutral axis distance reduction factor; steel-concrete composite beam

Address
Faxing Ding: School of Civil Engineering, Central South University, 22 South Shaoshan Rd., Changsha, Hunan Province, China; Engineering Technology Research Center for Prefabricated Construction Industrialization of Hunan Province, 22 South Shaoshan Rd., Changsha, Hunan Province, China
Hu Ding: Hunan No.6 Engineering Co., Ltd., 788 West Laodong Rd., Changsha, Hunan Province, China
Chang He: School of Civil Engineering, Central South University, 22 South Shaoshan Rd., Changsha, Hunan Province, China
Liping Wang: School of Civil Engineering, Central South University, 22 South Shaoshan Rd., Changsha, Hunan Province, China
Fei Lyu: School of Civil Engineering, Central South University, 22 South Shaoshan Rd., Changsha, Hunan Province, China

Abstract
In this study, the extreme learning machine and deep learning models were devised to estimate the bond strength of corroded reinforcement in concrete. The six inputs and one output were used in this study. The compressive strength, concrete cover, bond length, steel type, diameter of steel bar, and corrosion level were selected as the input variables. The results of bond strength were used as the output variable. Moreover, the Analysis of variance (Anova) was used to find the effect of input variables on the bond strength of corroded reinforcement in concrete. The prediction results were compared to the experimental results and each other. The extreme learning machine and the deep learning models estimated the bond strength by 99.81% and 99.99% accuracy, respectively. This study found that the deep learning model can be estimated the bond strength of corroded reinforcement with higher accuracy than the extreme learning machine model. The Anova results found that the corrosion level was found to be the input variable that most affects the bond strength of corroded reinforcement in concrete.

Key Words
anova analysis; bond strength; concrete; corroded reinforcement; deep learning; extreme learning machine

Address
Harun Tanyildizi: Department of Civil Engineering, Faculty of Technology, Firat University, Elazig, Turkey

Abstract
Bridges using double-tee (DT) girders from 12 m to 15 m are one of the good choices to improve accessibility in rural areas of the Mekong River Delta. In this study, nonlinear finite element method (FEM) analysis was conducted with different constitutive laws of materials. The FEM analysis results were compared to experimental results to confirm the applicability of the constitutive laws of materials for DT girders. A parametric study through FEM analysis was then conducted to investigate the effect of span lengths, top flange depths, and a number of prestressing tendons on the capacity of DT girders in order that propose DT girders for rural bridges. Parametric results showed that the top flange depth of a DT girder for rural bridges could be 120 mm. The DT girder with a span length of 12 m or 13 m could be used 16 tendons, while the DT girder with a span length of 14 m or 15 m could be set up with 20 tendons. The prestressed concrete DT girders based on FEM results can be suggested for the construction of rural bridges.

Key Words
compressive strength; concrete damage plasticity; double-tee girder; prestressed concrete

Address
Dinh Hung Nguyen: Department of Civil Engineering, International University-Vietnam National University Ho Chi Minh City Quarter 6, Linh Trung Ward, Thu Duc City, Ho Chi Minh City, Vietnam; Vietnam National University Ho Chi Minh City, Vietnam
Hong Nghiep Vu: Department of Civil Engineering, Van Lang University, No. 45, Nguyen Khac Nhu St., District 1, Ho Chi Minh City, Vietnam
Thac Quang Nguyen: University of Transport and Communications, Campus in Ho Chi Minh City,
No. 450-451 Le Van Viet St., Tang Nhon Phu A Ward, Thu Duc City, Ho Chi Minh City, Vietnam

Abstract
Mortar is produced with different degrees of temperature and ratios of admixture materials. In order to produce strength mortar, the degree of temperature and the ratio of admixture materials must be optimized. This paper examines experimental effects by applying certain degrees of temperature and ratios of admixture materials to statistically understand the changes in the strength of mortar. The Taguchi method is used for the above-mentioned optimization problem. Firstly, factorial ANOVA is used to investigate the difference of means between the experiments. Due to the significant differences in obtained means, the regression analysis is applied. On the other hand, the effects of the varying degrees of temperature and admixture ratios are presented via 3D plots. Finally, the statistical results of ANOVA and Taguchi indicate that degree of temperature and the different ratios of admixture materials can affect the behaviour of mortar under the tests.

Key Words
admixture; ANOVA; mortar; taguchi method; temperature

Address
Ilker Bekir Topcu: Department of Civil Engineering, Eskisehir Osmangazi University, Eskişehir, Turkey
Aytac Unverdi: Department of Civil Engineering, Eskisehir Osmangazi University, Eskişehir, Turkey
Vural Yildirim: Institute of Earth and Space Sciences, Eskisehir Technical University, Eskişehir, Turkey

Abstract
The usability of waste materials as raw materials is necessary for sustainable production. This study investigates the effects of different powder materials used to replace cement (0%, 5% and 10%) and standard sand (0%, 20% and 30%) (basalt, limestone, and dolomite) on the compressive strength (fc), flexural strength (fr), and ultrasonic pulse velocity (UPV) of mortars exposed to freeze-thaw cycles (56, 86, 126, 186 and 226 cycles). Furthermore, the usability of artificial intelligence models is compared, and the prediction accuracy of the outputs is examined according to the inputs (powder type, replacement ratio, and the number of cycles). The results show that the variability of the outputs was significantly high under the freeze-thaw effect in mortars produced with waste powder instead of those produced with cement and with standard sand. The highest prediction accuracy for all outputs was obtained using the adaptive-network-based fuzzy inference system model. The significantly high prediction accuracy was obtained for the UPV, fc, and fr of mortars produced using waste powders instead of standard sand (R2 of UPV, fc and ff is 0.931, 0.759 and 0.825 respectively), when under the freeze-thaw effect. However, for the mortars produced using waste powders instead of cement, the prediction accuracy of UPV was significantly high (R2=0.889) but the prediction accuracy of fc and fr was low (R2fc=0.612 and R2ff=0.334).

Key Words
artificial intelligence; freeze-thaw effect; mortar; waste powder

Address
Mehmet T. Cihan and İbrahim F. Aral: Department of Civil Engineering, Çorlu Engineering Faculty, Tekirdağ Nam

Abstract
The evaluation of velocity profile for large values of buoyancy parameter and Bioconvected Rayleigh number is examined. The non-linear problem has been tackled numerically by shooting technique. Nanofluid temperature and nanoparticle concentration slightly elevates for increasing values of thermophoresis coefficient. Thickness of thermal boundary layer is significantly increased with thermophoresis coefficient whereas thickness of concentration boundary layer is more slightly enhanced. The response of temperature and nanoparticles concentration is observed due to change in Brownian motion parameter. As Brownian motion parameter increased temperature distribution is slightly enhanced. The reverse behavior is observed in case of nanoparticles concentration. Comparison of numerical technique with the extant literature is made and an acceptable agreement is attained.

Key Words
Bioconvected Rayleigh number; nanoparticle; numerical technique; shooting technique; thermophoresis coefficient

Address
Humaira Sharif: Department of Mathematics, Govt. College University Faisalabad, 38000, Faisalabad, Pakistan
Muhammad Nawaz Naeem: Department of Mathematics, Govt. College University Faisalabad, 38000, Faisalabad, Pakistan
Mohamed A. Khadimallah: Civil Engineering Department, College of Engineering, Prince Sattam Bin Abdulaziz University, BP 655, Al-Kharj, 16273, Saudi Arabia; Laboratory of Systems and Applied Mechanics, Polytechnic School of Tunisia, University of Carthage, Tunis, Tunisia
Hamdi Ayed: Department of Civil Engineering, College of Engineering, King Khalid University, Abha, Kingdom of Saudi Arabia; Higher Institute of Transport and Logistics of Sousse, University Sousse, Tunisia
Muzamal Hussain: Department of Mathematics, Govt. College University Faisalabad, 38000, Faisalabad, Pakistan
Adil Alshoaibi: Department of Physics, College of Science, King Faisal University, Al-Hassa, P.O. Box 400, Hofuf 31982, Saudi Arabia


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