Grouting intelligent control method for pile foundation in karst area

By constructing a three-dimensional geological model using multi-source heterogeneous detection data and optimizing grouting parameters using an intelligent decision-making model, the problems of insufficient detection depth and reliance on experience in pile foundation construction in karst areas have been solved, thereby improving construction accuracy and efficiency.

CN122389686APending Publication Date: 2026-07-14CHINA UNITED ENG
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Patent Information

Authority / Receiving Office
CN · China
Patent Type
Applications(China)
Current Assignee / Owner
CHINA UNITED ENG
Filing Date
2026-03-20
Publication Date
2026-07-14

AI Technical Summary

Technical Problem

Existing technologies for pile foundation construction in karst areas suffer from problems such as insufficient detection depth, reliance on manual experience for grouting parameters, and disconnect between construction processes, resulting in insufficient construction accuracy, low efficiency, and poor quality control.

Method used

A three-dimensional geological model was constructed using multi-source heterogeneous detection data to extract key features of karst caves. Optimized grouting decision parameters were generated through an intelligent decision model, and construction parameters were adjusted in real time to carry out grouting operations and conduct quality inspections.

Benefits of technology

This has improved the precision, construction safety and efficiency, and enhanced the quality control of pile foundation grouting projects in karst areas.

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    Figure CN122389686A_ABST
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Abstract

The application relates to a grouting intelligent control method for a pile foundation in a karst area, comprising the following steps: collecting multi-source heterogeneous detection data of a pile site area, and constructing a three-dimensional geological model; based on the three-dimensional geological model, extracting key features of a karst cave, and forming a current karst cave decision feature vector; through a pre-trained intelligent decision model, an optimized grouting decision parameter set is generated; in the process of performing work according to the optimized grouting decision parameter set, real-time collection of grouting pressure and flow data is performed, and the dynamic prediction result in the decision model is combined to realize real-time adjustment of the construction parameters, so that the construction process is always directed towards an optimal filling path; when the grouting body meets a preset strength, quality detection is performed on the filling state to obtain a grouting quality detection result. The method can effectively solve the core problems of insufficient precision, experience dependence, extensive control and disconnection of links in the grouting engineering in the karst area, and improve the safety, efficiency, economy and quality controllability of the construction.
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