The reinforced concrete (RC) member’s shear strength estimation has been experimentally studied in most cases due to its nonlinear behavior. Many empirical equations have been derived from the experimental data; however, even those adopted in the construction codes do not thoroughly and accurately describe their shear behavior. Theoretically explained equations, on the other hand, are aligned with the experiment; however, they are complicated to use in practice. As shear behavior research is data-driven, the machine learning technique is applicable. Herein, an artificial neural network (ANN) algorithm is trained with 776 experiment results collected from available publications. The raw data is preprocessed by principal component analysis (PCA) before the application of the ANN technique. The predictions of the trained algorithm using ANN with PCA are compared to those of formulae adopted in a few existing building codes. Finally, a parametric study is conducted, and the significance of each variable to the strength of RC members is analyzed.

Artificial neural networks (ANNs) are an emerging field of research and have proven to have significant potential for use in structural engineering. In previous literature, many studies successfully utilized ANNs to analyze the structures under different loading conditions and verified the accuracy of the approach. Several studies investigated the use of ANNs to analyze the shear behavior of reinforced concrete (RC) members. However, few studies have focused on the potential use of an ANN for analysis of the torsional behavior of an RC member. Torsion is a complex problem and modeling the torsional fracture mechanism using the traditional analytical approach is problematic. Recent studies show that the nonlinear behavior of RC members under torsion can be modeled using ANNs. This paper presents a comprehensive analytical and parametric study of the torsional response of RC beams using ANNs. The ANN model was trained and validated against an experimental database of 159 RC beams reported in the literature. The results were compared with the predictions of design codes. The results show that ANNs can effectively model the torsional behavior of RC beams. The parametric study presented in this paper provides greater insight into the torsional resistance mechanism of RC beams and its characteristic parameters.

The mode spectrum in an optical nanowaveguide consisting of a dielectric-core layer surrounded by two identical metal layers is investigated. A simple model based on mode matching to predict the properties of mode propagation in such optical nanowaveguides is proposed. It is shown that quasi-TM₀₀ and quasi-TM₁₀ modes supported by an optical microstrip line do not have a cutoff frequency, regardless of the size of the metal strips, the thickness of the dielectric slab, and the cross-sectional shape. The transverse size of the TM₀₀ mode supported by a nanosized microstrip line was found to be approximately equal to the transverse dimension of the microstrip line. In closed rectangular and elliptical nanowaveguides, i.e., in which all dielectric surfaces are covered with metal films, the cross-sectional shape of the waveguide should be stretched along one side to produce propagation conditions for the fundamental mode.

Although it is widely known that the shear strength of reinforced concrete (RC) deep beams is higher than that of general RC beams to which Bernoulli’s assumption can be applied, the maximum shear strength limit of deep beams is identical to that of general beams. Therefore, the shear strength of deep beams is limited to a low value due to the shear strength limit in many cases. Different codes currently in use have different limit values for the maximum shear strength of deep beams. The strength limit of deep beams in the ACI 318–19 code is a function of the square root of concrete compressive strength ( $$\sqrt{{{\varvec{f}}}_{{\varvec{c}}}\boldsymbol{^{\prime}}}$$ ), but that in the AASHTO-LRFD code is a linear function of $${{\varvec{f}}}_{{\varvec{c}}}\boldsymbol{^{\prime}}$$ . Therefore, the ACI 318–19 code tends to underestimate the shear strength of deep beams with high-strength concrete. In addition, because the strength limits of the two codes do not consider the influences of the shear span-to-depth ratio, they may overestimate the strength of large-size deep beams with a large shear span-to-depth ratio. In this study, an equation for evaluating the maximum shear strength limit of deep beams was developed and verified against the experimental results, considering the effective compressive strength of concrete, shear span-to-depth ratio. The proposed maximum strength limit of deep beams was compared with the shear strengths of 672 deep beams, and the results indicated that the prediction closely matched the measured strength limit. The coefficient of variation of the shear strength ratio of ACI318-19 code was 39.2%, while that of the proposed equation was 32.3%, which was the smallest among the three predictions.