Dynamic risk early warning system for tunnel construction based on two-dimensional cloud model

Abstract

Due to the uncertainty of geological conditions and construction status during tunnel construction, the risk of tunnel construction will vary with updates to environmental information. The current risk management practice for tunnel construction projects in China is static and relies on the subjective judgment of experts and practitioners, so it is necessary to propose a risk assessment methodology that takes into account dynamically updated risk information and uncertainty. This paper proposes a dynamic risk warning system for tunnel construction that integrates knowledge-driven consequence datasets and data-driven likelihood datasets of risk events, using a probability algorithm based on a two-dimensional cloud model for risk decision-making. The algorithm quantifies the risk probabilities by randomly simulating the risk discrimination results under the influence of uncertainty multiple times and statistically analyzing the distribution pattern of a large number of simulation results within the constructed quantitative risk matrix. Decisions on risk level and warning state are made based on risk probabilities. Moreover, this study conducts extensive simulation tests to determine the optimal number of simulated cloud droplets and develops a software toolbox to facilitate rapid feedback on tunnel construction risk. The proposed algorithm is applied to the construction of Guiyang Metro Line 3. The results show that the standard deviation of the simulation results is less than 0.025 and the algorithm has good stability. The two-dimensional cloud model offers a superior reflection of risk uncertainty compared with its one-dimensional counterpart, making it more appropriate for assessing the risks associated with tunnel construction—a complex scenario characterized by intricate index relationships and significant uncertainty.