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首页> 外文期刊>International journal of steel structures >Predicting the Weight of the Steel Moment-Resisting Frame Structures Using Artificial Neural Networks
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Predicting the Weight of the Steel Moment-Resisting Frame Structures Using Artificial Neural Networks

机译：利用人工神经网络预测钢制耐弯框架结构的重量

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摘要

To estimate the cost of a building prior to the detail design phase, engineers and project managers need suitable tools and guidelines. Steel is an important construction material that is used in high volumes in buildings and has a significant role in the total cost of projects. In this paper, the application of the artificial neural network (ANN) method to predict the quantity of steel used in the steel moment-resisting frame (MRF) structures is presented. First, more than 1100 steel MRF structures were designed applying the changes in the influenced parameters, then these models were transferred to the ANN, and finally, the results of the performed parametric study were analyzed. The obtained results demonstrate that by using the proposed ANN method, the weights of the structures can be estimated with an acceptable accuracy prior to the starting of the design process. Based on the performed parametric study, several sets of required inputs in terms of the parameters of the story height, the span length, the number of stories, the seismicity rate of the construction site, ductility, the class of soil site and column cross section type influenced on the weight per unit area of the structure are submitted.
机译：为了在详细设计阶段之前估算建筑物的成本，工程师和项目经理需要合适的工具和指南。钢材是一种重要的建筑材料，在建筑物中大量使用，并且在项目的总成本中起着重要作用。本文介绍了人工神经网络（ANN）方法在预测钢制抗弯框架（MRF）结构中使用的钢量中的应用。首先，利用影响参数的变化设计了1100多个MRF钢结构，然后将这些模型转移到ANN，最后，对进行的参数研究的结果进行了分析。获得的结果表明，通过使用提出的ANN方法，可以在开始设计过程之前以可接受的精度估算结构的重量。在进行的参数研究的基础上，根据层高，跨度长度，层数，施工现场的抗震率，延展性，土壤现场类别和柱横截面的参数，输入了几组所需的输入提交受结构单位面积重量影响的类型。

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