Defa Wang

Predicting the tunnel deformation caused by overlying excavation is a crucial catch in current rail transit construction. Most theoretical studies overlook the effect of dewatering on tunnel response. This study utilized Mindlin's stress solution and an improved incremental method to calculate the additional load resulting from excavation. The load increment due to dewatering was determined based on the effective stress principle and a modified Dupuit infiltration curve. Treating the tunnel as a series of segment rings on a Vlazov foundation, the energy variance method and a collaborative deformation model were employed to develop a tunnel deformation prediction model. The proposed model's validity was confirmed by examining two traceable cases. Based on this, further discussion addressed the impact of dewatering depth, tunnel burial depth, tunnel‐pit relative distance, and intersection angle on existing tunnel deformation. Finally, combining the proposed model with multiple regression analysis, a semi‐empirical method was established to determine the tunnel's maximum vertical displacement. The study found that dewatering extends the tunnel force range, and the tunnel subsidence was positively correlated with groundwater level drop depth. The increase in the tunnel burial depth and distance from the excavation center led to a longer soil unloading transfer path, resulting in reduced disturbance to the tunnel. A smaller intersection angle between the excavation's short edge and the tunnel led to less disturbance of the underlying tunnel, which should be avoided in practical engineering. The developed semi‐empirical formulae have sufficient engineering accuracy, guiding the reinforcement of the underlying tunnel.

As a new type of green inorganic material, geopolymers have excellent mechanical properties, durability, and less environmental pollution. It is considered a new building material that can replace cement, but it also has some disadvantages such as high viscosity, poor fluidity, and more apparent pores after hardening. In this study, the uniaxial compressive strength test of geopolymer mortar was carried out, and the effects of alkali equivalent, alkali activator modulus, bone glue ratio, and silica fume content on the mechanical properties of geopolymer mortar were analyzed. The test results show that when the alkali equivalent is 13%, the alkali activator modulus is 1.4, the bone glue ratio is 2.0, the silicon powder content is 8%, and the metakaolin-based geopolymer mortar has higher uniaxial compressive strength. Through the comparative test of adding different kinds of defoamers and dosage, the effect of defoamers on the compressive strength, fluidity, density, and water-absorption of geopolymer mortar was further studied. The fluidity, density, and water-absorption were improved, and the uniaxial compressive strength was reduced. The formation of cementitious material in the mortar was confirmed by scanning electron microscope (SEM) observation. It was found that the pore structure and pore distribution changed with the content of different defoaming agents, and the microstructure of mortar after defoaming agent material treatment was shown. The proportion and distribution of Na, Al, and Si atoms were analyzed by energy dispersive spectroscopy (EDS). This experimental study shows that the defoamer can be an effective additive for geopolymer mortar.

In structural optimization design, obtaining the optimal solution of the objective function is the key to optimal design, and one‐dimensional search is one of the important methods for function optimization. The Golden Section method is the main method of one‐dimensional search, which has better convergence and stability. Based on the solution of the Golden Section method, this paper proposes an efficient one‐dimensional search algorithm, which has the advantages of fast convergence and good stability. An objective function calculation formula is introduced to compare and analyse this method with the Golden Section method, Newton method, and Fibonacci method. It is concluded that when the accuracy is set to 0.1, the new algorithm needs 3 iterations to obtain the target value. The Golden Section method takes 11 iterations, and the Fibonacci method requires 11 iterations. The Newton method cannot obtain the target value. When the accuracy is set to 0.01, the number of iterations of the new method is still the least. The optimized design of the T‐section beam is introduced for engineering application research. When the accuracy is set to 0.1, the new method needs 3 iterations to obtain the target value and the Golden Section method requires 13 iterations. When the accuracy is set to 0.01, the new method requires 4 iterations and the Golden Section method requires 18 iterations. The new method has significant advantages in the one‐dimensional search optimization problem.