The Evolution and Performance of Cold-Formed Steel Built-Up Battened Columns

The cold-formed steel (CFS) built-up battened column represents a transformative and innovative solution in the construction industry due to its versatility, cost-effectiveness, and superior strength-to-weight ratio. In design consideration, chord spacing and batten plates influence the structural performance and stability of the column. The emphasis lies in the design flexibility of built-up battened columns, in which the composite action of several chord members connected by batten plates can enhance axial compressive strength. The batten plate plays a crucial role in preventing buckling and increasing the stability of the column while distributing the load evenly. The batten plates need to be properly designed to ensure stability and reduce buckling failure in the built-up battened column. However, implementing CFS built-up battened columns presents several challenges, including design complexity, connection design, and limited standards and guidelines. 

Adaptive Neuro Fuzzy Inference System (ANFIS) and Artificial Neural Networks (ANNs) for structural damage identification

In this paper, adaptive neuro-fuzzy inference system (ANFIS) and artificial neural networks (ANNs) techniques are developed and applied to identify damage in a model steel girder bridge using dynamic parameters. The required data in the form of natural frequencies are obtained from experimental modal analysis. A comparative study is made using the ANNs and ANFIS techniques and results showed that both ANFIS and ANN present good predictions. However the proposed ANFIS architecture using hybrid learning algorithm was found to perform better than the multilayer feedforward ANN which learns using the backpropagation algorithm. This paper also highlights the concept of ANNs and ANFIS followed by the detail presentation of the experimental modal analysis for natural frequencies extraction

SA-EVPS ALGORITHM FOR DISCRETE SIZE OPTIMIZATION OF THE 582-BAR SPATIAL TRUSS STRUCTURE

Metaheuristic algorithms have become increasingly popular in recent years as a method for determining the optimal design of structures. Nowadays, approximate optimization methods are widely used. This study utilized the Self Adaptive Enhanced Vibrating Particle System (SA-EVPS) algorithm as an approximate optimization method, since the EVPS algorithm requires experimental parameters. As a well-known and large-scale structure, the 582-bar spatial truss structure was analyzed using the finite element method, and optimization processes were implemented using MATLAB. In order to obtain weight optimization, the self-adaptive enhanced vibration particle system (SA-EVPS) is compared with the EVPS algorithm

Damage detection of steel bridge girder using Artificial Neural Networks

Civil structures are exposed to damage during their service life which can severely affect their safety and functionality. Thus it is important to monitor structures for the occurrence, location and extent of damage. Artificial Neural Networks (ANNs) are inspired by human biological neurons and have been applied dramatically for damage identification with varied success. The feasibility of ANNs as strong tool for predicting the severity of damage in a model steel girder bridge is examined in this research. Natural frequencies of a structure have strong effect on damage and are applied as effective input parameters to train the ANN in present study. The required data for the ANNs in the form of natural frequencies are obtained from experimental modal analysis. It has been shown that an ANN trained only with natural frequency data can determine the severity of damage with less than 5.6 error. The results seem to be quite promising as accurately as possible. © 2012 Taylor & Francis Group, London

Structural damage detection of steel bridge girder using artificial neural networks and finite element models

Damage in structures often leads to failure. Thus it is very important to monitor structures for the occurrence of damage. When damage happens in a structure the consequence is a change in its modal parameters such as natural frequencies and mode shapes. Artificial Neural Networks (ANNs) are inspired by human biological neurons and have been applied for damage identification with varied success. Natural frequencies of a structure have a strong effect on damage and are applied as effective input parameters used to train the ANN in this study. The applicability of ANNs as a powerful tool for predicting the severity of damage in a model steel girder bridge is examined in this study. The data required for the ANNs which are in the form of natural frequencies were obtained from numerical modal analysis. By incorporating the training data, ANNs are capable of producing outputs in terms of damage severity using the first five natural frequencies. It has been demonstrated that an ANN trained only with natural frequency data can determine the severity of damage with a 6.8 error. The results shows that ANNs trained with numerically obtained samples have a strong potential for structural damage identification. Copyright © 2013 Techno Press

Experimental study on the strain contribution of horizontal and vertical web reinforced bar of HSSCC deep beams

Deep beams are structural elements loaded as beams in which a significant amount of the load is transferred to the supports by a compression strut trajectory joining the loads and the reactions. A comparative study is performed to predict the strain contribution of horizontal and vertical web bar located at compression strut trajectory from support points to load points. For investigation in this purpose, three high strength self compacted concrete (HSSCC) rectangular-section deep beams with the length to depth ratio less than three were designed based on American Concrete Institute (ACI) code with variation of tensile bar percentage and casted and loaded in laboratory. The longitudinal, web steel strains and concrete strains were measured for every incremental load. Before of first crack occurrences, the vertical bar strain is more than horizontal bar strain. When first crack happened, strain in vertical and horizontal web bar was same and by load increasing the strain in horizontal increased more than vertical. The strain became more than two times in horizontal bras comparison to strain in vertical bar when tensile bar yielded. At ultimate load the strain contribution of horizontal bar was more than four times in comparison by vertical bars