基于多源数据的建筑综合风险评估与更新优化策略——以福州市仓山区为例

Building Comprehensive Risk Assessment and Optimization Strategy Based on Multi-source Data:A Case Study of Cangshan District, Fuzhou

评估建筑安全是城市更新的重要基础环节,既往的建筑预警评估体系存在准确性低、主观性强、测度尺度有限的问题。以正处于快速发展阶段的福建省福州市仓山区为对象,应用PS-InSAR技术与机器学习相结合的方法测度建筑自身风险,划定高风险建筑集聚区;基于多源数据构建综合风险评估模型,实现多维度建筑风险评估,为城市开发建设提供量化分析路径。得出结论:PS-InSAR技术结合机器学习所得到的建筑自身风险符合实际情况,其结果可进一步快速、科学测定高风险建筑集聚区;综合社会经济因素与自然环境因素,结合实地调研,可分析不同风险区发展前景及更新次序,从而提出针对性的更新优化策略。

Evaluation of building safety is an important factor in urban renewal. The previous early warning evaluation system has problems of low accuracy, strong subjectivity and limited measure scale. Taking the Cangshan District of Fuzhou as the object, which is in the rapid development stage, the PS-InSAR technology combined with the machine learning method is used to measure the risk of building itself, and the high-risk building cluster area is defined. The comprehensive risk assessment model is constructed based on multi-source data to realize multi-dimensional building risk assessment and provide a quantitative analysis path for urban development and construction. It is concluded that the building risk obtained by PS-InSAR technology combined with machine learning is in line with the actual situation, and the results can further determine the high-risk building cluster area quickly and scientifically. The development prospect and renewal order of different risk areas can be analyzed by combining social and economic factors and natural environment factors and field investigation, so as to put forward targeted renewal and optimization strategies.