Soil disturbance evaluation of soft clay based on stress-normalized smallstrain stiffness

Soil disturbance includes the change of stress state and the damage of soil structure.The field testing indices reflect the combined effect of both changes and it is difficult to identify the soil structure disturbance directly from these indices.In the present study,the small-strain shear modulus is used to characterize soil structure disturbance by normalizing the effective stress and void ratio based on Hardin equation.The procedure for evaluating soil sampling disturbance in the field and the further disturbance during the subsequent consolidation process in laboratory test is proposed,and then validated by a case study of soft clay ground.Downhole seismic testing in the field,portable piezoelectric bender elements for the drilled sample and bender elements in triaxial apparatus for the consolidated sample were used to monitor the shear wave velocity of the soil from intact to disturbed and even remolded states.It is found that soil sampling disturbance degree by conventional thin-wall sampler is about 30%according to the proposed procedure,which is slightly higher than that from the modified volume compression method proposed by Hong and Onitsuka(1998).And the additional soil disturbance induced by consolidation in laboratory could reach about 50%when the consolidation pressure is far beyond the structural yield stress,and it follows the plastic volumetric strain quite well.

Adaptive mutation sparrow search algorithm-Elman-AdaBoost model for predicting the deformation of subway tunnels

A novel coupled model integrating Elman-AdaBoost with adaptive mutation sparrow search algorithm(AM-SSA),called AMSSAElman-AdaBoost,is proposed for predicting the existing metro tunnel deformation induced by adjacent deep excavations in soft ground.The novelty is that the modified SSA proposes adaptive adjustment strategy to create a balance between the capacity of exploitation and exploration.In AM-SSA,firstly,the population is initialized by cat mapping chaotic sequences to improve the ergodicity and randomness of the individual sparrow,enhancing the global search ability.Then the individuals are adjusted by Tent chaotic disturbance and Cauchy mutation to avoid the population being too concentrated or scattered,expanding the local search ability.Finally,the adaptive producer-scrounger number adjustment formula is introduced to balance the ability to seek the global and local optimal.In addition,it leads to the improved algorithm achieving a better accuracy level and convergence speed compared with the original SSA.To demonstrate the effectiveness and reliability of AM-SSA,23 classical benchmark functions and 25 IEEE Congress on Evolutionary Computation benchmark test functions(CEC2005),are employed as the numerical examples and investigated in comparison with some wellknown optimization algorithms.The statistical results indicate the promising performance of AM-SSA in a variety of optimization with constrained and unknown search spaces.By utilizing the AdaBoost algorithm,multiple sets of weak AMSSA-Elman predictor functions are restructured into one strong predictor by successive iterations for the tunnel deformation prediction output.Additionally,the on-site monitoring data acquired from a deep excavation project in Ningbo,China,were selected as the training and testing sample.Meanwhile,the predictive outcomes are compared with those of other different optimization and machine learning techniques.In the end,the obtained results in this real-world geotechnical engineering field reveal the feasibility of the proposed hybrid algorithm model,illustrating its power and superiority in terms of computational efficiency,accuracy,stability,and robustness.More critically,by observing data in real time on daily basis,the structural safety associated with metro tunnels could be supervised,which enables decision-makers to take concrete control and protection measures.