Modification of Peck Formula to Predict Surface Settlement of Tunnel Construction in Water-Rich Sandy Cobble Strata and Its Program Implementation

Abstract

There are few studies on the land subsidence induced by shield tunneling in the water-rich sandy gravel stratum, which is of high research value. Linear regression and measured data were employed in this study to investigate the land subsidence induced by shield tunneling when crossing the water-rich sandy gravel stratum from Mudan Dadao Station to Longmen Dadao station of Luoyang Metro Line 2. The maximum land subsidence correction coefficient, α, and the settlement trough width correction coefficient, β, were introduced to modify the peck formula to predict land subsidence induced by shield tunneling in Luoyang’s water-rich sandy gravel stratum. It was discovered that the original Peck formula needs to be modified because its prediction result was significantly larger than the actual value. When the value ranges of α and β in the modified Peck formula were 0.379~0.690 and 0.455~0.508, respectively, the modified Peck formula presented a minor error, in terms of the prediction curve, compared with the original formula, and the prediction result was more reliable. The best prediction result could be obtained when α = 0.535 and β = 0.482. In addition, Python could effectively improve the calculation efficiency of the Peck formula modification.

Prediction method of surface settlement of rectangular pipe jacking tunnel based on improved PSO-BP neural network

“…The number of intermediate layers and the number of neurons in each layer can be set arbitrarily accord- www.nature.com/scientificreports/ ing to the specific situation, and the operation is relatively simple and convenient, but the learning rate is slow, the convergence time is long, and the random initial weights and thresholds will have a greater impact on the results, thereby reducing the prediction performance of the network as a whole. To reduce the impact, this paper adopts the improved particle swarm algorithm and introduces the linear decreasing inertia weight ω i , which better balances the global search ability and local search ability of the algorithm, and the ω i update formula is shown in (4). Through the global search of particles, when the later particle search falls into the local optimum, the mutation jumps out of the local point and studies until the optimal individual in Eq.…”

“…The existing surface settlement prediction methods mainly include empirical methods, analytical methods, numerical simulation methods, model test methods and artificial intelligence algorithm predictions. The well-known Peck formula in the empirical method [1][2][3][4] can describe the general morphology of surface settlement and roughly believes that the lateral distribution of surface settlement generated by tunnel construction is approximately the normal distribution curve, but due to the large difference in the values of different strata empirical parameters, the two key parameters surface settlement trough width i and maximum settlement S max are related to many factors. Therefore, the empirical method expects settlement to have a wide distribution and cannot guarantee the accuracy and consistency of the prediction results.…”

Evaluation of the Ground Settlement in an Urban Area Resulting from a Small Curvature Tunneling Construction

“…Peck [5] reviewed a series of in-situ settlement datasets and proposed a Gaussian-distribution-form equation to represent the tunnel-excavation-caused settlement. Later, many scholars continuously improved Peck's equation based on engineering data to widen its applicability [7][8][9]. One can observe that Peck's equation has been blindly used in tunneling construction regardless of the assumptions adopted in the equation.…”

Improved Machine Learning Model for Urban Tunnel Settlement Prediction Using Sparse Data

“…The structural settlement changes of urban tunnels may seriously affect the structure, endangering the safety, stability, and lifespan of the tunnel [1][2][3]. Accurately predicting settlement is vital for monitoring changes and implementing preventive maintenance, making it a critical concern in academic and industrial domains [4][5][6]. A preventive maintenance strategy can be made based on estimating future tunnel structural settlement.…”