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.

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.

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.

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.

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 (f_c), flexural strength (f_r), 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, f_c, and f_r of mortars produced using waste powders instead of standard sand (R² of UPV, f_c and f_f 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 f_c and f_r was low (R²f_c=0.612 and R²f_f=0.334).

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.