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Artificial Neural Network Approach for Grading of Maintainability in Wet Areas of High-Rise Buildings

机译:高层建筑湿区可维修性分级的人工神经网络方法

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摘要

A grading system using artificial neural networks to enhance decision-making of wet area design was developed. The model was derived from condition survey of 450 tall buildings and in-depth assessment of a further 120 tall buildings and interviews with the relevant building professionals. The system allows comparison of various alternative designs, materials, construction and maintenance practices, so as to achieve optimum solutions of technical attributes that lead to minimum life cycle maintenance cost.
机译:开发了使用人工神经网络来增强湿地设计决策的评分系统。该模型源自对450座高层建筑的状况调查,并对另外120座高层建筑进行了深入评估,并与相关建筑专业人士进行了访谈。该系统可以比较各种替代设计,材料,构造和维护方法,从而实现技术特性的最佳解决方案,从而使生命周期维护成本降至最低。

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