Intelligent prediction method for intelligent jacking construction progress of bent cap
By constructing a construction entropy model and a causal state evolution model, the problem of delayed risk identification in construction progress prediction was solved, enabling forward-looking identification of construction risks and scientific corrective decision-making, thereby improving the accuracy and safety of construction progress prediction.
CN121094243AActive Publication Date: 2025-12-09SUZHOU TRAFFIC ENG GRP CO LTD
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Patent Information
- Application Number
- CN202511622674.X
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2025-11-07
- Publication Date
- 2025-12-09
- Estimated Expiration
- 2045-11-07
AI Technical Summary
Technical Problem
Existing technologies struggle to glean insights into construction risks from multi-source data and cannot effectively predict construction progress, leading to delays in the identification and response to construction risks.
Method used
By collecting multi-source real-time data during the girder lifting construction process, a construction entropy model is constructed. A causal state evolution model is used for prediction and counterfactual inference to generate an adaptive correction strategy.
Benefits of technology
It enables proactive identification of construction risks and scientific corrective decision-making, improving the accuracy and safety of construction progress prediction.
✦ Generated by Eureka AI based on patent content.
Abstract
The invention relates to the technical field of intelligent construction, and discloses a bent cap intelligent jacking construction progress intelligent prediction method, which comprises the following steps: collecting multi-source real-time data such as equipment, structure, personnel and environment in a construction process, and calculating a construction entropy for measuring system uncertainty; establishing a causal state evolution model, and predicting the evolution trend of the construction entropy in a future time period; and when it is predicted that a construction progress disturbance risk exists, performing anti-fact deduction based on the model or the dynamically trimmed sparse model thereof, and generating and recommending an optimal adaptive correction strategy. According to the method, the overall uncertainty of the construction system is quantified into the construction entropy, and the prospective deduction capability of the causal state evolution model is combined, so that the conversion from the risk passive response depending on experience to the data-driven beforehand active early warning and optimal decision recommendation is realized; and improvement of scientificity, foresight and timeliness of construction risk management is facilitated.
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