为解决采用三维激光扫描仪探测隧道裂缝的识别精度低、抗干扰能力差的问题，提出基于联邦加权学习算法的裂缝探测研究新思路。基于隧道激光点云数据，首先使用优化联邦加权学习算法，并采用异步和残差测试自适应调整算法，以此整体达到精准探测隧道裂缝的目的。在临滕高速隧道进行试验，围绕裂缝探测的可靠性、准确度和测量精度等指标，将本文算法与传统算法进行对比分析，结果表明本文提出的新方法能有效提高隧道裂缝探测的可靠性及准确率，对探测裂缝宽度的精度上也有较好的性能。当探测结果中出现灰尘、钢筋裸露等干扰因素时，新算法较传统算法在可靠性上仍有明显优势，仍能达到95%以上的识别准确度和低于10%的识别误识率，这些确保了算法应用效果的鲁棒性。通过工程现场的实践，本算法识别出的裂缝宽度与人工测量值之间最小偏差仅为0.06 mm，验证了其良好的裂缝识别精度。

The three-dimensional laser scanners for detecting tunnel cracks have disadvantages such as low recognition accuracy and poor anti-interference ability. Therefore, a new research approach for crack detection based on federated weighted learning algorithm is proposed. Based on tunnel laser point cloud data, an optimized federated weighted learning algorithm is employed, and asynchronous and residual testing adaptive adjustment algorithms are adopted to achieve overall accurate detection of tunnel cracks. Experiments are conducted in the Linyi-Tengzhou expressway tunnel, focusing on several indicators such as reliability, accuracy, and measurement accuracy of crack detection. The proposed algorithm is compared with traditional ones. The results show that the proposed method can effectively improve the reliability and accuracy of tunnel crack detection, exhibiting good performance in detecting crack width accuracy. When interference factors such as dust and exposed steel bars appear in the detection results, the proposed algorithm still exhibits significant advantages in reliability compared to traditional algorithms, achieving an accuracy of over 95% and a misidentification rate of less than 10%, thus ensuring the robustness of the algorithm′s application effect. Through an on-site engineering practice, the minimum deviation between the crack width identified by the proposed algorithm and the manually measured value is only 0.06 mm, verifying its good crack recognition accuracy.