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Keeping in view the design of short to medium-span bridges in critical locations such as road/river crossings under the action of oppositely moving high-speed loads, this paper presents a hybrid model for the prediction of dynamic displacement employing a theoretical non-dimensional framework aided by an artificial neural network (ANN). The introduction of such a hybrid model reduces parametric space and time. The feed-forward ANN is implemented to train the comprehensive data set obtained from the non-dimensional theoretical scheme. The dimensionless input to ANN comprises the bridge and load parameters, whereas maximum dynamic displacement is the output parameter. The robustness and efficiency of the proposed surrogate ANN framework in terms of computational efficiency are compared with the conventional mode-superposition method formulated in dimensionless form. The training of test and validated data sets results in best-fit models with optimistic statistical metrics and accuracy ≤ 6%. Moreover, the sensitivity of different parameters to the displacement response is highlighted using Pearson's correlation and Quantile–Quantile plots. To incorporate the best-fit models into the design by bypassing the high-fidelity model, a user-friendly interface is developed, confirming the standards of the high-speed rating code (TZY2014-232 2014). Such interfaces have the potential for wide application in the preliminary design of short-to medium-span bridges under opposite moving loads in critical locations such as crossing valleys, rivers, and railroads. [ABSTRACT FROM AUTHOR]

The Π -shaped open section girder is a very common section, which has been widely used in the construction of modern bridges. However, compared with a streamlined box girder, it is easy to cause vortex-induced vibration (VIV) in bridges due to its bluff section shape and poor aerodynamic stability. In this paper, a typical open section with a height–width ratio of 10:1 is taken as the research object. Through wind tunnel tests and numerical simulation technology, the time-history data of VIV of the Π -shaped section are obtained. By selecting three different neural network models to establish the relationship between external parameters (wind attack angle, wind speed, turbulence) and structural response parameters (the onset wind speed of VIV, the length of the lock-in interval). After comparing different machine learning methods, the most suitable machine learning model is selected to predict the VIV response characteristics of the main girder. The research results show that the typical open section has a serious VIV phenomenon. The BP neural network model has the best performance in predicting the VIV performance of the open section in terms of calculation speed, prediction accuracy and solving nonlinear adaptability. At the same time, it is found that different characteristic parameters of the incoming wind field have different influences on the prediction results of the VIV response. Among them, the wind attack angle is the key parameter that affects the onset wind speed of VIV, but the turbulence intensity has a greater impact on the maximum amplitude of VIV. At the same time, the optimized BP model can predict the characteristic parameters of VIV response under unknown wind fields. This paper provides a new idea to study the effect of different incoming flow parameters on the performance of section VIV and further improves people's understanding of VIV system. [ABSTRACT FROM AUTHOR]

This paper investigates the possibility for implementing machine learning-aided prediction in analyzing the buckling phenomena of ballasted railway tracks induced by extreme temperature. In this study, artificial neural networks (ANNs) have been developed to identify the relationship between various ballasted track conditions and outputs, namely safe temperature and buckling temperature. The variables included in the objective function of the optimization problems are the lateral resistance of ballasted track provided by ballast-sleeper interaction, torsional resistance provided by fastening systems, and misalignment of the track. Due to its complexity in parameter combinations, the objective of this study is to create predictive models with the aim of minimizing the usage of scarce resources. Thus, this paper is the first to develop a novel machine learning-aided prediction of railway track buckling due to extreme temperature. Comprehensively, all 353 datasets of the safe and buckling temperatures derived from previous finite element (FE) simulation results have been collected and trained. Note that the mean squared error (MSE) and the coefficient of determination ( R 2) are considered to quantify the performance of the ANN architectures. The optimal ANN architecture with a very high rate of accuracy has been determined and highlighted. Thus, the suggested neural network model can be applied conveniently to help estimate safe and buckling temperatures of the complex track models in order to improve track conditions and thus prevent track buckling in summer. [ABSTRACT FROM AUTHOR]

Lateral distortional buckling (LDB) mode has been investigated as one of the failure modes in steel I-beams. The LDB mode in such beams is known by simultaneous lateral deflection, twist, and cross-sectional change due to web distortion. However, many studies have been conducted to investigate the behavior of this failure mode; so far, no formula has been found to calculate the ultimate LDB resistance of I-beams, which also takes into account the effect of different loading conditions. Consequently, in the current paper, by conducting an extensive parametric study, it was tried to investigate the effect of all main parameters as well as the effect of different loading conditions on the ultimate LDB resistance of I-shaped beams. Then, based on the provided database, the artificial neural network (ANN) method was employed, and based on it, a high-precision formulation was proposed to predict the ultimate LDB strength of steel I-beams. In addition to the ANN method, a regression-based formula was also developed as a classical method to examine the differences between the two methods. Finally, the proposed formulas were compared with other existing formulas for estimating the LDB strength. The results showed that the proposed formula based on ANN not only presents a reasonable accuracy compared to the existing formulations but also can be used by engineers as practical equations in the design of I-beams. [ABSTRACT FROM AUTHOR]

Vibration-based methods can be used to detect damage in a structure as its vibration characteristics change with physical changes in the structure. Arch bridge is a popular type of bridge with rather complex vibration characteristics which pose a challenge for using existing vibration-based methods to detect damage in the bridge. Further, its particular geometry with a curved arch rib and vertical members (either in compression or tension) to support the horizontal deck makes the process of damage quantification using vibration-based methods harder and challenging. This paper develops and presents a vibration-based method that utilizes damage pattern changes in frequency response functions (FRFs) and artificial neural networks (ANNs) to locate and quantify damage in the rib of deck-type arch bridge, which is the most important load bearing component in the bridge. Principal component analysis, which is performed to reduce the dimension of original FRF data series and to obtain limited principal component analysis (PCA)-compressed FRF data is used in the development of the proposed method. FRF change, which is the difference in the FRF data between the intact and the damaged structure, is compressed to a few principal components and fed to ANNs to predict the location and severity of structural damage. The process and the hierarchy of developed ANN systems are presented, including the "fusion network" concept, which individually analyses FRF-based damage indicators separated by sensor locations. Finally, results obtained for many tested damage cases (inverse problems) are presented, which demonstrate the applicability of the proposed method for locating and quantifying damage in the rib of deck type arch bridge. [ABSTRACT FROM AUTHOR]

This paper presents a novel approach for structural damage detection and estimation using incomplete noisy modal data and artificial neural network (ANN). A feed-forward back propagation network is proposed for estimating the structural damage location and severity. Incomplete modal data is used in the dynamic analysis of damaged structures by the condensed finite element model and as input parameters to the neural network for damage identification. In all cases, the first two natural modes were used for the training process. The present method is applied to three examples consisting of a simply supported beam, three-story plane frame, and spring-mass system. Also, the effect of the discrepancy in mass and stiffness between the finite element model and the actual tested dynamic system has been investigated. The results demonstrated the accuracy and efficiency of the proposed method using incomplete modal data, which may be noisy or noise-free. [ABSTRACT FROM AUTHOR]