Intelligent prediction of rockburst in tunnels based on back propagation neural network integrated beetle antennae search algorithm.

Rockburst is one of the major engineering geological disasters of underground engineering. Accurate rockburst intensity level prediction is vital for disaster control during underground tunnel construction. In this work, a hybrid model integrating the back propagation neural network (BPNN) with beetle antennae search algorithm (BAS) has been developed for rockburst prediction.

Stability classification probability model of loess deposits based on MCS-Cloud.

The stability classification of loess deposits around tunnels is a vital prerequisite for safe construction in underground environment. Due to the fuzziness and randomness of loess physical and mechanical parameters, the stability prediction of loess deposits shows uncertainty. Existing loess deposit stability classification models rarely consider the uncertainty of influencing factors.

A new method for subsea tunnel site selection based on environmental bearing capacity.

Engineering site selection is an essential and systematic work in the early engineering construction stage. At present, the subsea tunnel site selection mainly depends on manual experience. There is still a lack of subsea tunnel site selection systems based on environmental impact.