基于人工神经系统的建筑物拆除成本预测

N. Concha, Arnold Nicole Cana, Rissa Mae Suzara, Ulysses Fallarcuna
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引用次数: 1

摘要

拆迁成本估算与任何工程项目一样,需要大量的时间和经验来完成,因为它涉及计算影响因素之间的复杂关系。鉴于人工神经网络(ANN)在涉及复杂参数的成本预测领域的有效性,本研究旨在开发一种能够预测奎松市建筑物拆除成本的人工神经网络。从奎松市建管局收集100个拆迁项目进行评估,随机分为两组:90%用于培训、验证和内部测试,10%用于外部应用。确定了9个影响拆除成本的因素,即:建筑条件、材料和分类、楼层数、总建筑面积、场地可达性、位置、使用的拆除方法和碎片清除方案。采用前馈反向传播算法进行训练。所选人工神经网络模型的最终架构由12个隐藏节点组成。该模型对奎松市的拆迁成本进行了预测,平均准确率为90.21%。
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An Artificial Neural System to Predict Building Demolition Cost
Cost estimation of building demolition, like any engineering project, requires ample amount of time and experience to accomplish since it involves calculations of complex relationships between its influencing factors. Since artificial neural networks (ANNs) are known to be effective in the cost-forecasting domain with complex parameters involve, the study aims to develop an ANN that can predict building demolition cost in Quezon City. One-hundred demolition projects from the Department of Building Official in Quezon City were gathered, evaluated and divided randomly into two sets: 90% for training, validation and internal testing and 10% for external application. Nine demolition cost-influencing factors were identified, namely: building condition, materials and classification, number of floors, total floor area, site accessibility, location, demolition methods used and debris removal options. The training was applied with feedforward backpropagation algorithm. The resulting architecture for the selected ANN model consists of 12 hidden nodes. The model tested and was successful in predicting demolition cost in Quezon City with an average accuracy rating of 90.21%.
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