Key Takeaways & Executive Findings
- •• The PSR–GA–SVM model achieves 94.37% accuracy in identifying AE Kaiser points, outperforming traditional methods. • The proposed PPPGS method provides a more accurate and efficient alternative for in-situ stress measurement. • Fractal dimension from phase space reconstruction is a key feature, ranking second in importance after accumulated AE count. • Field validation at a phosphate mine in Guizhou, China, confirms the practical applicability of the method.
Abstract
Large-scale underground projects need accurate in-situ stress information, and the acoustic emission (AE) Kaiser effect method currently offers lower costs and streamlined procedures. In this method, the accuracy and speed of Kaiser point identification are important. Thus, this study aims to integrate chaos theory and machine learning for accurately and quickly identifying Kaiser points. An intelligent model of the identification of AE partitioned areas was established by phase space reconstruction (PSR), genetic algorithm (GA), and support vector machine (SVM). Then, the plots of model classification results were made to identify Kaiser points. We refer to this method of identifying Kaiser points as the partitioning plot method based on PSR–GA–SVM (PPPGS). The PSR–GA–SVM model demonstrated outstanding performance, which achieved a 94.37% accuracy rate on the test set, with other evaluation metrics also indicating exceptional performance. The PPPGS identified Kaiser points similar to the tangent-intersection method with greater accuracy. Furthermore, in the feature importance score of the classification model, the fractal dimension extracted by PSR ranked second after accumulated AE count, which confirmed its importance and reliability as a classification feature. The PPPGS was applied to in-situ stress measurement at a phosphate mine in Guizhou Weng’an, China, to validate its practicability, where it demonstrated good performance.
1. Introduction
The rapid development of the mining industry and the construction of urban underground spaces have led to an increasing trend in large-scale deep underground mining and the excavation of deep tunnels [1–6]. Determining the in-situ stress information reliably is important to ensure the safety of construction and personnel in the project [7–10]. Traditionally employed methods in measuring in-situ stress mainly include the stress relief method by overcoring and the hydrofracturing method. Although traditional methods are widely used in engineering measurements, they show certain limitations. These limitations include high costs, time-consuming and labor-intensive procedures, and difficulty conducting measurements in deep underground areas [11]. Therefore, the acoustic emission (AE) Kaiser effect method has been quickly developed recently. The Kaiser effect refers to the phenomenon in materials undergoing cyclic loading, where a large amount of AE signals are detected only when stress surpasses the previously experienced maximum stress of the material. The moment when the Kaiser effect occurs is called the Kaiser point. The Kaiser effect was first discovered by Joseph Kaiser in 1950 in metal samples [12], followed by Goodman in 1963 in rocks [13].
The most critical part of the AE Kaiser effect method for in-situ stress measurement is the identification of the Kaiser point, and several scholars have already proposed various identification methods for the Kaiser point. Boyce et al. [14] first proposed the tangent-intersection method based on AE features. Hayashi et al. [15] concluded that Kaiser point can be obtained by take-off point based on AE counts, and Qin et al. [16] also used this method. Villaescusa et al. [17] combined the take-off point method with the strain of the specimen to determine the Kaiser point. Yoshikawa and Mogi [18] proposed a new method to identify the Kaiser point by loading the specimen twice cyclically and observing the difference in the AE events between the first and second cycles. Then, Srinivasan et al. [19] also employed this method to study the Kaiser effect. Bai et al. [20] suggested a Kaiser point identification method based on the slope and tilt angle of the curve of the accumulated AE counts. The aforementioned methods can identify Kaiser points and have their respective strengths. However, these methods may have some limitations, such as relatively subjective discriminative conditions or difficulty in identifying the Kaiser point in the case of more noise.
Chaos refers to a phenomenon in nonlinear dynamical systems characterized by initial value sensitivity, intrinsic randomness, and ergodicity, which show the complexity and unpredictability of system behavior. It has been studied to different degrees by scholars in many fields. Li and Liu [21] investigated the law of AE activity of rock mass through chaotic dynamics theory and extracted the nonlinear features of AE activity.
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Zhangwei Chen, Zhixiang Liu, Jiangzhan Chen, Xibing Li, Linqi Huang (2025). Intelligent identification of acoustic emission Kaiser effect points and its application in efficiently acquiring in-situ stress. Int. Journal of Minerals, Metallurgy and Materials (矿物冶金与材料学报). https://doi.org/10.1007/s12613-024-2977-6
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Frequently Asked Questions
What is the Kaiser effect in acoustic emission?
The Kaiser effect is a phenomenon in materials under cyclic loading where significant acoustic emission signals occur only when the applied stress exceeds the maximum previously experienced stress. The point at which this occurs is called the Kaiser point.
How does the proposed PPPGS method improve Kaiser point identification?
The PPPGS method integrates phase space reconstruction, genetic algorithm, and support vector machine to classify AE partitioned areas, achieving 94.37% accuracy and providing a more objective and efficient identification compared to traditional methods.
What are the advantages of using the AE Kaiser effect method for in-situ stress measurement?
The AE Kaiser effect method offers lower costs, streamlined procedures, and the ability to measure in-situ stress in deep underground areas where traditional methods are difficult to apply.
What role does fractal dimension play in the classification model?
Fractal dimension extracted via phase space reconstruction is a significant feature in the classification model, ranking second in importance after accumulated AE count, confirming its reliability for identifying Kaiser points.
Where was the PPPGS method validated?
The PPPGS method was applied to in-situ stress measurement at a phosphate mine in Guizhou Weng’an, China, where it demonstrated good performance, validating its practical applicability.
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