Machine-Learning-Based uncertainty and sensitivity analysis of Reinforced-Concrete slabs subjected to fire
Machine-Learning-Based uncertainty and sensitivity analysis of Reinforced-Concrete slabs subjected to fire
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DOI：
10.1016/j.istruc.2023.04.030
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发表时间：
2023-07
期刊：
影响因子：
4.1
通讯作者：
Dashan Zhang;Xu Lin;Y. Dong;Xiaohui Yu
中科院分区：
文献类型：
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作者：
Dashan Zhang;Xu Lin;Y. Dong;Xiaohui Yu
钢筋混凝土（RC）板是建筑结构的组成部分，在遭受火灾时提供分隔功能。然而，RC板的耐火性能受到许多因素的影响，这些因素大多是固有的不确定性。因此，进行不确定性和敏感性分析，考察潜在不确定性参数对RC板耐火性能的影响和意义。为此，生成了一组具有不同设计参数的RC板试样，并利用参数化火灾曲线考虑了不同的火灾情景。楼板试样与火灾情景试样随机耦合。对于每一对板-火对，进行了有限元模拟，并确定了三个特定的火灾持续时间，以表示与钢温度（SP-Ⅰ），未暴露表面温度（SP-Ⅱ）和跨中挠度（SP- III）对应的板耐火破坏准则。因此，通过收集所有考虑的板火样品的计算耐火系数建立了一个数据库。采用线性回归、随机森林、梯度增强决策树和极端梯度增强四种常用的ML算法，建立了不确定参数下楼板耐火性能的预测模型。在已开发的基于ml的预测模型中，极端梯度增强模型被证明具有较好的预测精度。因此，进一步利用不确定性分析，考虑了5种场景、材料强度、几何尺寸、外载荷等14个不确定参数。不确定性分析结果表明，考虑的不确定性参数导致了楼板耐火性能的显著变异，与SP-Ⅰ、SP-Ⅱ和SP- III相关的火灾持续时间的变异系数分别为11.5%、13.1%和20.6%。此外，使用SHapley加性解释方法来检验所考虑的不确定参数的敏感性。研究发现，与火灾情景相关的参数对楼板耐火性能的影响大于楼板设计参数。开口系数、板厚和火载密度对SP-Ⅰ、SP-Ⅱ和SP- III的耐火性能影响显著，而材料强度和对流条件的不确定性对其耐火性能的影响可以忽略不计。
Reinforced concrete (RC) slabs are integral parts of building structures and provide compartmentation functionality when subjected to fire. However, the fire resistance of RC slabs is affected by numerous factors that are mostly inherent uncertainties. Therefore, uncertainty and sensitivity analyses were conducted to examine the influence and significance of the potential uncertain parameters on the fire resistance of RC slabs. To this end, a set of RC slab samples with various design parameters were generated and different fire scenarios were considered using parametric fire curves. The slab samples were randomly coupled with the fire scenario samples. For each of the slab-fire pairs, finite-element simulations were conducted, and three specific fire durations were identified to represent the slab fire resistance corresponding to the failure criteria on the steel temperature (SP-Ⅰ), unexposed surface temperature (SP-Ⅱ), and mid-span deflection (SP- III). As a consequence, a database was established by collecting the calculated fire resistance for all the considered slab-fire samples. The prediction models of the slab fire resistance in association with uncertain parameters were developed by four commonly used ML algorithms, including linear regression, random forest, gradient boosting decision tree, and extreme gradient boosting. Among the developed ML-based prediction models, the extreme gradient boosting model was proven to have superior predictive accuracy. Therefore, it was further utilized for uncertainty analysis by considering 14 uncertain parameters from fire scenarios, material strengths, geometric dimensions, and external loads. The uncertainty analysis results showed that the considered uncertain parameters cause significant variability in the fire resistance of slabs, and the coefficients of variation were 11.5%, 13.1%, and 20.6% for the fire durations related to SP-Ⅰ, SP-Ⅱ, and SP- III, respectively. Moreover, the SHapley Additive exPlanations method was used to examine the sensitivity of the considered uncertain parameters. It was found that the parameters related to fire scenario were more influential to the fire resistance of slabs than the slab design parameters. The opening factor, slab thickness, and fire load density had noticeable effects on the fire resistance of SP-Ⅰ, SP-Ⅱ, and SP- III, respectively, whereas the uncertainties from material strengths and convection conditions had negligible effects.