Neural network-based analytical model to predict the shear strength of steel girders with a trapezoidal corrugated web

Corrugated webs are used to increase the shear stability of steel webs of beam-like members and to eliminate the need of transverse stiffeners. Previously developed formulas for predicting the shear strength of trapezoidal corrugated steel webs, along with the corresponding theory, are summarized. An artificial neural network (ANN)-based model is proposed to estimate the shear strength of steel girders with a trapezoidal corrugated web, and under a concentrated load. 210 test results from previous published research were collected into a database according to relevant test specimen parameters in order to feed the simulated ANNs. Seven (geometrical and material) parameters were identified as input variables and the ultimate shear stress at failure was considered the output variable. The proposed ANN-based analytical model yielded maximum and mean relative errors of 0.0% for the 210 points from the database. Moreover, still based on those points, it was illustrated that the ANN-based model clearly outperforms the other existing analytical models, which yield mean errors larger than 13%

Neural network-based formula for shear capacity prediction of one-way slabs under concentrated loads

According to the current codes and guidelines, shear assessment of existing reinforced concrete slab bridges sometimes leads to the conclusion that the bridge under consideration has insufficient shear capacity. The calculated shear capacity, however, does not consider the transverse redistribution capacity of slabs, thus leading to overconservative values. This paper proposes an artificial neural network (ANN)-based formula to come up with estimates of the shear capacity of one-way reinforced concrete slabs under a concentrated load, based on 287 test results gathered from the literature. The proposed model yields maximum and mean relative errors of 0.0% for the 287 data points. Moreover, it was illustrated to clearly outperform (mean Vtest / VANN =1.00) the Eurocode 2 provisions (mean VE,EC / VR,c =1.59) for that dataset. A step-by-step assessment scheme for reinforced concrete slab bridges by means of the ANN-based model is also proposed, which results in an improvement of the current assessment procedures

ANN-based Shear Capacity of Steel Fiber-Reinforced Concrete Beams Without Stirrups

Comparing experimental results on the shear capacity of steel fiber-reinforced concrete (SFRC) beams without mild steel stirrups, to the ones predicted by current design equations and other available formulations, still shows significant differences. In this paper we propose the use of artificial intelligence to estimate the shear capacity of these members. A database of 430 test results reported in the literature is used to develop an artificial neural network-based formula that predicts the shear capacity of SFRC beams without shear reinforcement. The proposed model yields maximum and mean relative errors of 0.0% for the 430 data points, which represents a better prediction (mean Vtest / VANN = 1.00 with a coefficient of variation of 1× 10-15) than the existing expressions, where the best model yields a mean value of Vtest / Vpred = 1.01 and a coefficient of variation of 27%

Neural network-based analytical model to predict the shear strength of steel girders with a trapezoidal corrugated web

Corrugated webs are used to increase the shear stability of steel webs of beam-like members and to eliminate the need of transverse stiffeners. Previously developed formulas for predicting the shear strength of trapezoidal corrugated steel webs, along with the corresponding theory, are summarized. An artificial neural network (ANN)-based model is proposed to estimate the shear strength of steel girders with a trapezoidal corrugated web, and under a concentrated load. 210 test results from previous published research were collected into a database according to relevant test specimen parameters in order to feed the simulated ANNs. Seven (geometrical and material) parameters were identified as input variables and the ultimate shear stress at failure was considered the output variable. The proposed ANN-based analytical model yielded maximum and mean relative errors of 0.0% for the 210 points from the database. Moreover, still based on those points, it was illustrated that the ANN-based model clearly outperforms the other existing analytical models, which yield mean errors larger than 13%

Shear Capacity of Headed Studs in Steel-Concrete Structures: Analytical Prediction via Soft Computing

Headed studs are commonly used as shear connectors to transfer longitudinal shear force at the interface between steel and concrete in composite structures (e.g., bridge decks). Code-based equations for predicting the shear capacity of headed studs are summarized. An artificial neural network (ANN)-based analytical model is proposed to estimate the shear capacity of headed steel studs. 234 push-out test results from previous published research were collected into a database in order to feed the simulated ANNs. Three parameters were identified as input variables for the prediction of the headed stud shear force at failure, namely the steel stud tensile strength and diameter, and the concrete (cylinder) compressive strength. The proposed ANN-based analytical model yielded, for all collected data, maximum and mean relative errors of 3.3 % and 0.6 %, respectively. Moreover, it was illustrated that, for that data, the neural network approach clearly outperforms the existing code-based equations, which yield mean errors greater than 13 %

Potential of neural networks for structural damage localization

Fabrication technology and structural engineering states-of-art have led to a growing use of slender structures, making them more susceptible to static and dynamic actions that may lead to some sort of damage. In this context, regular inspections and evaluations are necessary to detect and predict structural damage and establish maintenance actions able to guarantee structural safety and durability with minimal cost. However, these procedures are traditionally quite time-consuming and costly, and techniques allowing a more effective damage detection are necessary. This paper assesses the potential of Artificial Neural Network (ANN) models in the prediction of damage localization in structural members, as function of their dynamic properties – the three first natural frequencies are used. Based on 64 numerical examples from damaged (mostly) and undamaged steel channel beams, an ANN-based analytical model is proposed as a highly accurate and efficient damage localization estimator. The proposed model yielded maximum errors of 0.2 and 0.7 % concerning 64 numerical and 3 experimental data points, respectively. Due to the high-quality of results, authors’ next step is the application of similar approaches to entire structures, based on much larger datasets

Research Counts, Not the Journal

‘If there is one thing every bibliometrician agrees, is that you should never use the journal impact factor (JIF) to evaluate research performance for an article or an individual – that is a mortal sin’. Few sentences could define so precisely the uses and misuses of the Journal Impact Factor (JIF) better than Anthony van Raan’s. This manuscript presents a critical overview on the international use, by governments and institutions, of the JIF and/or journal indexing information for individual research quality assessment. Interviews given by Nobel Laureates speaking on this matter are partially illustrated in this work. Furthermore, the authors propose complementary and alternative versions of the journal impact factor, respectively named Complementary (CIF) and Timeless (TIF) Impact Factors, aiming to better assess the average quality of a journal – never of a paper or an author. The idea behind impact factors is not useless, it has just been misused

Comportamento e Modelação do Aço

El presente trabajo de revisión pone a disposición de toda la comunidad técnica y científica vinculada al estudio del comportamiento de estructuras de acero las leyes constitutivas utilizadas con frecuencia, y de manera eficaz, en la modelación del comportamiento elástico-plástico de aceros al carbono e inoxidables en simulaciones numéricas por elementos finitos. Ya que el acero inoxidable es un material relativamente nuevo en aplicaciones estructurales, y con un comportamiento material altamente no lineal y muy distinto al acero dulce (el acero inoxidable no tiene un límite de fluencia bien definido), el artículo se enfoca principalmente en los aceros inoxidables, incluyendo una descripción detallada (i) de los principales tipos de aplicaciones, y ventajas en la construcción, y (ii) de las principales expresiones analíticas propuestas en la literatura para modelar el comportamiento uniaxial de toda aleación (austeníticos, ferríticos o duplex). En particular, se recomienda el uso de la ley típica bilineal para modelar el acero al carbono (con o sin endurecimiento) y la relación no lineal (ε-σ) propuesta por Quach et al. (2008) para simular el acero inoxidable, la cual es (i) válida para el comportamiento a la tracción/compresión hasta la última extensión, y (ii) depende solo de dos parámetros básicos de Ramberg-Osgood (E, σ0.2, n). Asimismo, se sugiere que ese acero se modele con un comportamiento lineal en el régimen elástico, teniendo en cuenta el límite de tensión proporcional a 0.01% (σ0.01) como tensión de fluencia inicial.Este trabalho de revisão disponibiliza a toda a comunidade técnica e científica ligadas ao estudo do comportamento de estruturas de aço, leis constitutivas frequente e eficazmente utilizadas na modelação do comportamento elasto-plástico de aços carbono e inoxidáveis em simulações numéricas por elementos finitos. Sendo o aço inoxidável um material relativamente recente em aplicações estruturais, e tendo este um comportamento material altamente não-linear e bem distinto do aço macio (o inox não tem um ponto de cedência bem definido), o artigo focase principalmente nos aços inoxidáveis, incluindo uma descrição detalhada (i) dos principais tipos, aplicações, e vantagens na construção, e (ii) das principais expressões analíticas propostas na literatura para modelar o comportamento uniaxial de qualquer liga (austenítica, ferrítica ou duplex). Em particular, recomenda-se a utilização da típica lei bi-linear para modelar o aço carbono (com ou sem endurecimento) e a relação nãolinear (ε-σ) proposta por Quach et al. (2008) para simular o aço inoxidável, a qual (i) é válida para o comportamento à tracção/compressão até à extensão última, e (ii) depende apenas dos 3 parâmetros básicos de Ramberg-Osgood (E, σ0.2, n). Sugere-se ainda que esse aço seja modelado com um comportamento linear em regime elástico, tomando-se a tensão limite de proporcionalidade a 0.01% (σ0.01) como tensão de cedência inicial

Potential of neural networks for maximum displacement predictions in railway beams on frictionally damped foundations

Since the use of finite element (FE) simulations for the dynamic analysis of railway beams on frictionally damped foundations are (i) very time consuming, and (ii) require advanced know-how and software that go beyond the available resources of typical civil engineering firms, this paper aims to demonstrate the potential of Artificial Neural Networks (ANN) to effectively predict the maximum displacements and the critical velocity in railway beams under moving loads. Four ANN-based models are proposed, one per load velocity range ([50, 175] ∪ [250, 300] m/s; ]175, 250[ m/s) and per displacement type (upward or downward). Each model is function of two independent variables, a frictional parameter and the load velocity. Among all models and the 663 data points used, a maximum error of 5.4 % was obtained when comparing the ANN- and FE-based solutions. Whereas the latter involves an average computing time per data point of thousands of seconds, the former does not even need a millisecond. This study was an important step towards the development of more versatile (i.e., including other types of input variables) ANN-based models for the same type of problem

3D FEM to predict residual stresses of press-braked thin-walled steel sections

Cold-formed steel sections are normally produced by cold work manufacturing processes. The amount of cold work to form the sections may have induced residual stresses in the section especially in the area of bending. Hence, these cold work processes may have significant effects on the section behaviour and load-bearing capacity. There was a lack of studies in investigating the effects of residual stresses raised by press-braking operations unlike the roll-forming operation. Therefore, a 3D finite element simulation was employed to simulate this forming process. This study investigated the magnitude of the maximum residual stresses along the length of the corner region and through-thickness residual stress variations induced by the press-braking forming process. The study concluded that residual stresses are not linear longitudinally (along the corner region). Maximum residual stresses exist near the middle surface of the plate. The comparison of the 3D-FE results with the 2D-FE results illustrate that 3D-FE has a variation in transverse and longitudinal residual stresses along the plate length. In addition, 2D-FE results overestimate the residual stresses along the corner region