The invention discloses a bridge jacking force-displacement
dynamic prediction and counterweight optimization method based on CNN-LSTM and edge calculation, and belongs to the field of intelligent
bridge engineering monitoring and
structure control, and the method comprises the steps: S1, integrating multi-dimensional
sensing data, constructing a space-time
incidence matrix, extracting force-displacement characteristics such as curvature, gradient change rate,
hysteresis effect parameters, and the like, and carrying out the optimization of the force-displacement characteristics; multi-scale
convolution sampling is carried out to capture abrupt change and stationary features; s2, constructing a CNN-LSTM
hybrid model, extracting local features by CNN, capturing
time sequence dependence by LSTM, introducing a moment balance constraint Fc * R = Mz + Mun into a
loss function, outputting a prediction
confidence interval through Dropout, and fusing multi-
branch features to form a final
feature vector; s3, deploying an edge-cloud cooperative
system, realizing real-time reasoning and counterweight optimization by an edge end, and executing incremental training by a cloud end to update a model weight; and S4, calculating the balance
weight adjustment amount according to the predicted critical jacking force, and realizing closed-
loop control of hoisting operation by linkage with BIM
visualization. The method has the beneficial effects that the construction safety and the real-time decision-making efficiency are remarkably improved.