Advances in Civil Engineering

Biomasses are environmentally friendly additives that lower pollution in pavement engineering because of their biodegradability. On the other hand, to build a safe, long-lasting pavement, rutting prevention is crucial. This study provides a comprehensive review of the efficacy of biomass as recyclable materials in reducing rutting and enhancing characteristics of asphalt mixtures. According to findings, the hydrocarbon polymer properties of lignin and biomass ash improve asphalt binder consistency, hardness, and function at high temperatures. The results showed that biochar, due to its solid shape, enhances the stiffness and viscosity of the mixtures. The high-temperature performance of asphalt binder is improved by bioshell waste, which increases rutting parameters. Thus, biomass like ash, lignin, and biochar can increase asphalt binder rheology and rutting resistance due to chemical forces such as Van der Waals and hydrogen ions. The macroscopic and microscopic investigation also shows higher interaction and better adhesion in bioasphalt. However, asphalt binders containing bio-oil exhibited no unique behaviors due to their lubricant impact. Based on the estimation of the life cycle assessment (LCA), it was determined that biomass utilization has the potential to decrease the cost and CO₂ emissions of pavement engineering by as much as 10% and more than three times, respectively. An examination of recyclability revealed that biomass utilization can decrease the requirement for additional stabilizers by as much as 20%.

As the weakest link in the shield segment, the reinforcement and repair technology of shield segment joint has received widespread attention. In this study, an finite element model utilizing a cohesive zone model (CZM) was constructed to simulate the mechanical behavior of the shield segment joint during the whole fracture process. The proposed modeling method of joint allows multiple layers of steel bars to be stacked without interference by applying cohesive elements. Cohesive elements were employed to represent the mechanical response of potential fracture surfaces in concrete, as well as the interfaces between steel–concrete and fiber-reinforced polymer (FRP)–concrete, by utilizing various constitutive models tailored for mixed-mode loading conditions. A group of experiments was chosen to assess the precision of the proposed model by comparing the mechanical response and the fracture patterns. Finally, parameter analyses were conducted to study the reinforcement effect of the FRP bonding length and width on the shield segment joint. The results indicate that external bonding of FRP can effectively enhance the bearing capacity and stiffness of shield segment joints. However, insufficient bonding length or width may significantly reduce the strengthening effect and potentially decrease the ductility of the joint.

This paper introduced a new nonexplosive roadway excavation method, combining the reserved free space technology and the static expansion mechanical fracturing technology, where the former is implemented by the gasbag, while the piston splitter is for the latter. The numerical model of roadway excavation was set up via PFC3D to investigate the mechanical fracturing performance, including the single-hole fracturing and the hole network fracturing. The results show that the reasonable hole margin is about 1.0–1.5 m, and the optimal column spacing of the hole network pattern is 1.0 m, after comprehensively analyzing the fracturing performance and the splitting force evolution. Moreover, the mechanical fracturing excavation method was applied to construct a parking chamber in the Kalatongke Mine, to preliminarily verify the feasibility of the static expansion mechanical fracturing technology. The in situ investigation results indicate that the excavation footage is about 0.8 m with the piston splitter when adopting a hole margin of 1.0–1.5 m. To sum up, the preliminary field application and the numerical simulation result both support the feasibility of mechanical fracturing, and the reasonable fracturing hole margin is about 1.0 m.

In order to accurately and efficiently assess the impact of tunnel excavation on overlying existing pipeline, an analytical method is proposed to solve this problem. First, the vertical free displacement of the surrounding soil due to tunnel excavation can be derived by the Loganathan formula. Next, the overlying existing pipeline can be treated as a Timoshenko beam resting on the Vlasov foundation model, and the influence of the surrounding soil on the both sides of the existing pipeline is taken into consideration. Finally, an analytical solution for the longitudinal deformation of the existing pipeline can be obtained by using the integral method. Case analysis results demonstrate that the calculated results of this method closely in line with measured data. Compared to the degenerate analytical solution given by this method, the result from this method is more consistent with the measured data. Further parameter studies show that the volume loss rate, diameter of new tunnel, skew angle, and vertical distance between tunnel and pipeline are significant factors affecting the existing pipeline response due to tunneling underlying.

Predicting the status of train delays, a complex and dynamic problem, is crucial for railway enterprises and passengers. This paper proposes a novel hybrid deep learning model composed of convolutional neural networks (CNN) and temporal convolutional networks (TCN), named the CNN + TCN model, for predicting train delays in railway systems. First, we construct 3D data containing the spatiotemporal characteristics of real-world train data. Then, the CNN + TCN model employs a 3D CNN component, which is fed into the constructed 3D data to mine the spatiotemporal characteristics, and a TCN component that captures the temporal characteristics in railway operation data. Furthermore, the characteristic variables corresponding to the two components are selected. Finally, the model is evaluated by leveraging data from two railway lines in the United Kingdom. Numerical results show that the CNN + TCN model has greater accuracy and convergence performance in train delay prediction.

Seismic site response is inevitably influenced by natural variability of soil properties and anticipated earthquake intensity. This study presents the influence of variability in shear wave velocity (V_s) and amplitude of input rock motion on seismic site response analysis. Monte Carlo simulations were employed to randomize the V_s profile for different scenarios. A series of 1-D equivalent linear (EQL) seismic site response analyses were conducted by combining the randomized V_s profile with different levels of rock motion intensities. The results of the analyses are presented in terms of surface spectral acceleration, amplification factors (AFs), and peak ground acceleration (PGA). The mean and standard deviation of these parameters are thoroughly discussed for a wide range of randomized V_s profile, number of V_s randomizations, and intensities of input rock motions. The results demonstrate that both the median PGA and its standard deviations across different number of V_s profile realization exhibit a slight variation. As few as twenty V_s profile realizations are sufficient to compute reliable response parameters. Both rock motion intensity and standard deviation of V_s variability cause significant variation in computed surface parameters. However, the variability in the number of records used to conduct site response has no significant impact on ground response if the records closely match the target spectrum. Incorporating the multiple sources of variabilities can reduce uncertainty when conducting ground response simulations.