Abstract Detecting sudden changes in geological conditions (e.g., karst cavern and fault zone) during tunnelling is a complex task. These changes can cause shield machines to jam or even induce geo-hazards such as water ingress and surface subsidence. Tunnelling parameters that relate closely to the surrounding geology have proliferated in recent years and present a substantial opportunity for the application of data-driven artificial intelligent (AI) techniques that can infer patterns from data without reference to known, or labelled, outcomes. This study explores the potential for support vector machines (SVM) to identify changes in soil type during tunnelling towards reducing the possibility of jamming and geo-hazard development. All tunnelling data were pre-processed to convert time series data into feature-based sub-series. A selection of the most popular parameter optimisation algorithms was explored to improve the accuracy of the AI predictions. Their relative merits were evaluated through comparisons with a recent pipejacking case history undertaken in gravel and clayey gravel soils. The results highlight an exciting potential for the use of optimisation algorithm-based SVMs to identify changes in soil conditions during pipejacking.

中文翻译：

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使用基于优化算法的支持向量机识别顶管参数特征以评估地质条件

摘要 在隧道掘进过程中检测地质条件（例如，岩溶洞穴和断层带）的突然变化是一项复杂的任务。这些变化可能导致盾构机堵塞，甚至引发地质灾害，例如进水和地表下沉。近年来，与周围地质密切相关的隧道参数激增，为数据驱动的人工智能 (AI) 技术的应用提供了巨大的机会，该技术可以在不参考已知或标记的结果的情况下从数据中推断出模式。本研究探讨了支持向量机 (SVM) 在挖掘隧道过程中识别土壤类型变化的潜力，以减少堵塞和地质灾害发展的可能性。所有隧道数据都经过预处理，以将时间序列数据转换为基于特征的子序列。探索了一些最流行的参数优化算法，以提高 AI 预测的准确性。通过与最近在砾石和粘土砾石土壤中进行的管道劫持案例历史进行比较，评估了它们的相对优点。结果突出了使用基于优化算法的 SVM 来识别顶管期间土壤条件变化的令人兴奋的潜力。