Testing for Knowledge: Maximizing Information Obtained from Fire Tests, Using Machine Learning Techniques

知识测试：使用机器学习技术最大化从火灾测试中获得的信息

• 批准号: 2275249
• 金额: --
• 依托单位: University of Edinburgh
• 依托单位国家: 英国
• 项目类别: Studentship
• 财政年份: 2018
• 资助国家: 英国
• 起止时间: 2018 至 无数据
• 项目状态: 已结题
• 来源: https://gtr.ukri.org/projects?ref=studentship-2275249
• 关键词: Testing; Knowledge; Maximizing; Information; Obtained

Current fire testing procedures for various materials are targeted primarily at compliance. This however introduces a risk of inadequate performance when the actual fire exposure differs from the test situation, be it in duration, maximum temperature, the HRR gradient or other. The proposed research project aims to provide a more complete characterization of the performance of materials or configurations by providing a procedure for targeted testing.As a PhD student, I will be working under the supervision of Dr. Grunde Jomaas from the University of Edinburgh and with the guidance of Dr. Ruben Van Coile from the University of Ghent to touch upon the engineering aspects of the problem as well as the main aspect which is fire safety.The project plan consists of a phased approach, working from specific applications towards a generalized framework.In phase 1 advanced regression techniques are applied to develop surrogate models for established fire tests, i.e. the Cone Calorimeter test and the Corner Burner test. After training the surrogate model with available test data (training set), and determining model architecture through cross validation data (cross validation set), the surrogate model is applied to predict fire test results of unseen samples (test set).In phase 2 the surrogate models are applied to generalize the knowledge obtained from the fire tests. This expansion of the field of application is associated with a loss of confidence in the simulation results. This loss of confidence can, naturally, be reduced through testing. By investigating the underlying mathematics of the surrogate models, test setups can be identified whose result is expected to result in the greatest benefit in confidence for the surrogate model. The surrogate models will be updated iteratively in function of additional test data which are specifically performed for their 'knowledge benefit'. Validation of the approach will be done through the execution of the specific identified tests.Phase 3 derives a general procedure for 'Testing for Knowledge' to be applied in conjunction with any series of fire tests, including the testing of innovative materials or designs. This is considered of great importance as it allows to obtain a comprehensive understanding of fire performance using the least possible number of tests. By iteratively testing set ups which are considered to result in the greatest 'knowledge benefit', a comprehensive fire engineering understanding is obtained at minimal cost.In phase 4 the proposed procedure is validated by application to innovative fire testing (new material, new configuration or other). The specifics of the tests will be determined with respect to testing requirements at the time (i.e. specific material or configuration whose performance needs to be better understood for commercial purposes at the time).

目前针对各种材料的防火测试程序主要针对合规性。然而，当实际的火灾暴露与测试情况不同时，无论是在持续时间、最高温度、HRR梯度还是其他方面，都会带来性能不足的风险。拟议的研究项目旨在通过提供有针对性的测试程序，对材料或结构的性能提供更完整的表征。作为一名博士生，我将在爱丁堡大学的Grunde Jomaas博士的指导下工作，并在根特大学的Ruben Van Coile博士的指导下接触问题的工程方面以及主要方面是消防安全。项目计划由分阶段的方法组成，从特定的应用程序到一般化的框架。在第一阶段，采用先进的回归技术为已建立的火灾测试开发替代模型，即锥形量热计测试和角燃烧器测试。用可用的测试数据（训练集）训练代理模型，并通过交叉验证数据（交叉验证集）确定模型架构后，将代理模型应用于预测未见样本（测试集）的火焰测试结果。在阶段2中，代理模型被用于推广从五个测试中获得的知识。这种应用领域的扩大与对模拟结果失去信心有关。当然，这种信心的丧失可以通过测试来减少。通过研究代理模型的基础数学，可以确定哪些测试设置的结果有望为代理模型带来最大的信心收益。代理模型将根据额外的测试数据进行迭代更新，这些测试数据是专门为其“知识效益”而执行的。方法的验证将通过执行特定的已确定的测试来完成。第3阶段制定了“知识测试”的一般程序，将与任何系列防火测试一起应用，包括对创新材料或设计的测试。这被认为是非常重要的，因为它允许使用尽可能少的测试次数获得对防火性能的全面了解。通过迭代测试被认为会产生最大的“知识效益”的设置，以最小的成本获得全面的消防工程理解。在第4阶段，通过应用于创新的防火测试（新材料，新配置或其他）来验证所提议的程序。测试的具体内容将根据当时的测试要求确定（即，当时出于商业目的需要更好地了解其性能的特定材料或配置）。