龙溪口航电工程防护堤碾压过程监控与压实质量分析

龙溪口航电防护堤工程是保护沿岸人民生命财产安全和维护龙溪口枢纽工程安全正常运行的重要工程，其施工质量至关重要。传统航电工程防护堤碾压过程采用试坑试验法检测压实度来进行质量检查与控制，难以实现全过程的实时控制。为保证施工质量，采用GNSS(全球导航卫星系统)和人工智能技术，研发龙溪口防护堤工程碾压过程监控系统，实现了对碾压轨迹、速度、遍数以及激振力状态的实时监控，基于灰狼优化的核极限学习机算法，提出压实质量评估模型，实现压实质量的实时分析。结合实际工程，通过对比平均绝对误差、均方根误差、相关系数，新型压实质量评估模型精度高于传统的BP神经网络、支持向量机(SVR)、极限学习机(ELM)模型。

Monitoring of rolling process and analysis of compaction quality of protective embankment in Longxikou Navigation-power project

Longxikou Navigation-power Protection Embankment project protects the lives and property of the people along the coast and maintains the safe and normal operation of the Longxikou Junction project.Therefore,its construction quality is important.The traditional quality inspection and control of the rolling process of the navigation power protection embankment project uses the test pit experiment to detect the degree of compaction,and it is difficult to achieve real-time control of the whole process.In order to ensure the construction quality,this paper uses global navigation satellite systems (GNSS) and artificial intelligence technology to develop a rolling process monitoring system for the Longxikou Protection Embankment Project,which realizes real-time monitoring of rolling trajectory,rolling speed,exciting force state,and number of roller passes.This paper uses the kernel extreme learning machine (ELM) algorithm optimized by the gray wolf to propose a compaction quality evaluation model and analyze the compaction quality in real time.Combined with the actual engineering,by comparing the average absolute error,root mean square error,and correlation coefficient,the accuracy of the new compaction quality evaluation model is higher than that of the traditional back propagation (BP) neural network,support vector machine (SVR),and extreme learning machine (ELM) models.