Compared with traditional feedback control, predictive control can eliminate the lag of pose control and avoid the snakelike motion of shield machines. Therefore, a shield pose prediction model was proposed based on dynamic modeling. Firstly, the dynamic equations of shield thrust system were established to clarify the relationship between force and movement of shield machine. Secondly, an analytical model was proposed to predict future multistep pose of the shield machine. Finally, a virtual prototype model was developed to simulate the dynamic behavior of the shield machine and validate the accuracy of the proposed pose prediction method. Results reveal that the model proposed can predict the shield pose with high accuracy, which can provide a decision basis whether for manual or automatic control of shield pose.
In recent years, the escalation in accidental explosions has emerged as a formidable threat to tunnel infrastructures. Therefore, it is of great significance to conduct a dynamic performance analysis of the tunnels, to improve the safety and maintain the functionality of underground transport hubs. To this end, this study proposes a dynamic performance assessment framework to assess the extent of damage of shallow buried circular tunnels under explosion hazards. First, the nonlinear dynamic finite element numerical model of soil-tunnel interaction system under explosion hazard was established and validated. Then, based on the validated numerical model, an explosion intensity (EI) considering both explosion equivalent and relative distance was used to further analyze the dynamic response characteristics under typical explosion conditions. Finally, this study further explored the influence of the integrity and strength of the surrounding soil, concrete strength, lining thickness, rebar strength, and rebar rate on the tunnel dynamic performance. Our results show that the dynamic performance assessment framework proposed for shallow circular tunnels fully integrates the coupling effects of explosion equivalent and distance, and is able to accurately measure the degree of damage sustained by these structures under different EI. This work contributes to designing and managing tunnels and underground transport networks based on dynamic performance, thereby facilitating decision-making and efficient allocation of resources by consultants, operators, and stakeholders.
Most earth-dam failures are mainly due to seepage, and an accurate assessment of the permeability coefficient provides an indication to avoid a disaster. Parametric uncertainties are encountered in the seepage analysis, and may be reduced by an inverse procedure that calibrates the simulation results to observations on the real system being simulated. This work proposes an adaptive Bayesian inversion method solved using artificial neural network (ANN) based Markov Chain Monte Carlo simulation. The optimized surrogate model achieves a coefficient of determination at 0.98 by ANN with 247 samples, whereby the computational workload can be greatly reduced. It is also significant to balance the accuracy and efficiency of the ANN model by adaptively updating the sample database. The enrichment samples are obtained from the posterior distribution after iteration, which allows a more accurate and rapid manner to the target posterior. The method was then applied to the hydraulic analysis of an earth dam. After calibrating the global permeability coefficient of the earth dam with the pore water pressure at the downstream unsaturated location, it was validated by the pore water pressure monitoring values at the upstream saturated location. In addition, the uncertainty in the permeability coefficient was reduced, from 0.5 to 0.05. It is shown that the provision of adequate prior information is valuable for improving the efficiency of the Bayesian inversion.
Conical picks are important tools for rock mechanical excavation. Mean cutting force (MCF) of conical pick determines the suitability of the target rock for mechanical excavation. Accurate evaluation of MCF is important for pick design and rock cutting. This study proposed hybrid methods composed of boosting trees and Bayesian optimization (BO) for accurate evaluation of MCF. 220 datasets including uniaxial compression strength, tensile strength, tip angle (θ), attack angle, and cutting depth, were collected. Four boosting trees were developed based on the database to predict MCF. BO optimized the hyper-parameters of these boosting trees. Model evaluation suggested that the proposed hybrid models outperformed many commonly utilized machine learning models. The hybrid model composed of BO and categorical boosting (BO-CatBoost) was the best. Its outstanding performance was attributed to its advantages in dealing with categorical features (θ included 6 types of angles and could be considered as categorical features). A graphical user interface was developed to facilitate the application of BO-CatBoost for the estimation of MCF. Moreover, the influences of the input parameters on the model and their relationship with MCF were analyzed. When θ increased from 80° to 90°, it had a significant contribution to the increase of MCF.
Underground excavation can lead to stress redistribution and result in an excavation damaged zone (EDZ), which is an important factor affecting the excavation stability and support design. Accurately estimating the thickness of EDZ is essential to ensure the safety of the underground excavation. In this study, four novel hybrid ensemble learning models were developed by optimizing the extreme gradient boosting (XGBoost) and random forest (RF) algorithms through simulated annealing (SA) and Bayesian optimization (BO) approaches, namely SA-XGBoost, SA-RF, BO-XGBoost and BO-RF models. A total of 210 cases were collected from Xiangxi Gold Mine in Hunan Province and Fankou Lead-zinc Mine in Guangdong Province, China, including seven input indicators: embedding depth, drift span, uniaxial compressive strength of rock, rock mass rating, unit weight of rock, lateral pressure coefficient of roadway and unit consumption of blasting explosive. The performance of the proposed models was evaluated by the coefficient of determination, root mean squared error, mean absolute error and variance accounted for. The results indicated that the SA-XGBoost model performed best. The Shapley additive explanations method revealed that the embedding depth was the most important indicator. Moreover, the convergence curves suggested that the SA-XGBoost model can reduce the generalization error and avoid overfitting.
Large residual stresses would be generated in the laser additive manufactured (LAMed) structures after processing rapid and intense heating and cooling cycles with bad mechanical properties. Scholars have tried many methods to decrease the residual stress to prevent the structures from being broken and improve the mechanical properties. In this study, residual stress and mechanical properties of LAMed structures are analyzed, and the advanced measuring method, laser ultrasonic technique, is used to detect the residual stresses accumulated in the samples in time. The results show that when the solution temperature is less than T β (992 °C), the residual stress increases gradually with the increase of solution temperature, and when the temperature is more than T β (992 °C), Widmanstätten structure will significantly reduce the residual stress; the mechanical properties of the specimen decrease with the increase of the solution temperature, and the different cooling methods do not have much effect on the elastic properties of the specimen. Considering the residual stress and mechanical properties, the HT1 system used in this paper is the best. This study is of great significance for the reasonable suppression of residual stress and the regulation of mechanical properties of TC4 titanium alloy fabricated by laser additive manufacturing.
Fatigue and tensile behaviors of homogenized WE54 magnesium alloy before and after immersion in simulated body fluid (SBF) were investigated. According to the tensile test, the alloy without immersion in SBF solution has the highest tensile strength of 278 MPa, which decreased to 190 MPa after 336 h of immersion.. The fatigue life of the homogenized WE54 magnesium alloy before immersion in the SBF solution under a constant stress of 15 MPa is 3598 cycles. However, the fatigue life of the alloy decreased to 453 cycles after 336 h of immersion in the SBF solution under the same stress. Examination of the fracture surface of the samples by SEM reveals that the origin of the fatigue crack before immersion is micro-pores and defects. While corrosion pits and cracks are the main reasons for forming the initial fatigue crack after immersion. Moreover, the results obtained from practical work were evaluated and compared to theoretical calculations. The area of the hysteresis loops of the samples after the fatigue test, determined using Triangles and Monte Carlo methods, decreased from 4954.5 MPa and 4842.9 MPa before immersion to 192.0 MPa and 175.8 MPa after 336 h of immersion, respectively.
Hot tensile tests were performed on Hastelloy C-276 alloy in the temperature range of 850–1150° C and strain rate range of 0.01–10 s−1 to reveal its fracture characteristics and critical fracture failure conditions during high-temperature deformation process. Short-term aging treatments were also conducted to analyze the effects of precipitation on the fracture behaviors in conjunction with the experimental results obtained from the hot tensile tests. It was observed that the main precipitates in Hastelloy C-276 alloy under hot tensile deformation and short-term aging treatment were identified as M6C carbides, around which the microscopic voids nucleate when the external forces were applied. Considering the effects of deformation temperature and strain rate, two failure criteria based on Zener-Hollomon parameter were developed to describe the fracture behaviors of Hastelloy C-276 alloy deforming at elevated temperatures. Finite element method (FEM) coupling with the proposed failure criteria was used to examine the validity by comparing the predicted values with the experimental data, and the comparison results indicate that the established failure criteria were capable of predicting the fracture behaviors of Hastelloy C-276 alloy in hot deformation process.
Aluminum alloy is used as the support of final optical assembly because of its excellent mechanical properties, which constitutes the “skeleton” of high-power laser system. Stray light reflected by weak optical elements in high power laser system will fall on the inner wall frame of aluminum alloy, which will cause damage and produce impurity particles, polluting the entire optical system. However, the research on the damage mechanism and protection technology of aluminum alloy under the action of high-power laser system is still in the initial stage. This paper introduces the interaction mechanism between laser and materials, analyzes the laser damage mechanism of aluminum alloy from the perspective of plasma nano metal particle ablation, reviews the progress of laser-induced damage protection of aluminum alloy, and prospects the future research direction of laser absorption and damage protection technology of aluminum alloy under the action of high-energy laser.
In this study, specific warm rolling was carried out to process the Fe50Mn30Co10Cr10 high-entropy alloy. The aim was to investigate the effect of warm rolling temperature on the microstructure and mechanical properties. The results indicated that serious transverse cracks appeared in the 25 °C rolled sheet with reduction of 60%, which was significantly improved through 100–300 °C warm rolling. In addition, the increase of rolling temperature promoted dislocation slip and inhibited martensitic transformation and twinning deformation. A single face centered cubic (FCC) matrix with abundant dislocations and stacking faults was developed in the 300 °C rolled microstructure. Meanwhile, the deformation stored energy gradually increased, and the copper-type deformation texture was gradually enhanced. After annealing, the recrystallized microstructure of 25–200 °C rolled sheets was composed of FCC and a small amount of HCP phase. However, the hexagonal close packed (HCP) content in the annealed sheet rolled at 300 °C was as high as 20%–23% after annealing for 2–4 min and decreased to 4.5% after annealing for 8 min. All recrystallized microstructure contained a large number of annealing twins, and the average grain size increased with the increase of rolling temperature. Moreover, the mechanical properties of the annealed sheet were significantly improved after warm rolling.
This study investigated the effect of pre-friction surfacing heat treatment of consumable rods and heat input during friction surfacing on the microstructure, mechanical properties, and wear resistance of hypereutectic Al-Si alloy deposited on a commercially pure aluminum substrate. The results show that regardless of the consumable rod’s heat treatment conditions, the coating’s efficiency has increased with the increase in heat input, so the coating efficiency increases by 20% and 30% in the solid solution-treated rod and the artificially aged rod, respectively. By increasing the heat input, the average grain size in the coating fabricated by solid solution-treated rod and artificially aged rod increased from 0.1 to 0.9 µm and from 0.2 to 1.3 µm, respectively. At constant heat input, the average hardness and wear resistance of the coating created in the solid solution-treated rod are lower than those of the artificially aged rod. By decreasing heat input, the wear loss in the coating fabricated by solid solution-treated rod and artificially aged rod decreased by 10% and 20%, respectively, reaching 0.1 and 0.03 µg/m.
The excessive demand for phosphorus-based fertilizers is contributing to the undesired byproduct of phosphogypsum (PG), typically found in large quantities in phosphoric acid industry. Without proper management, this industrial waste poses a significant environmental pollution risk. Current technologies are struggle to effectively handle the volume of PG produced, but one promising solution is its conversion into hemihydrate gypsum (CaSO4·0.5H2O, HH). HH can exist in two phases, α-HH and β-HH, with α-hemihydrate gypsum (α-HH) being preferred for its complete crystal structure and lower water requirement for hydration. The morphology of α-HH gypsum is crucial for its material applications, and controlling crystal morphology is possible through the use of suitable crystal modifiers. This review explores various aspects of crystal modifiers and highlights their role in the preparation of α-HH from PG. It suggests that leveraging the interfacial properties of PG could lead to innovative applications. Additionally, the review outlines future directions for PG development and identifies challenges to be addressed in the next steps.
This study developed a direct reduction route to smelt refractory high-phosphorus iron ores by using hydrogen-rich gas. The effects of temperature, gas composition, and gangue on the reduction behavior of iron ore pellets were investigated. Additionally, the migration behavior of phosphorus throughout the reduction-smelting process was examined. The apparent activation energy of the reduction process increased from 64.2 to 194.2 kJ/mol. Increasing the basicity from 0.5 to 0.9 increased the metallization rate from 85.9% to 89.2%. During the reduction process, phosphorus remained in the gangue phase. Carbon deposition and phosphorus removal behaviors of the pellets were investigated and correlated with the gas composition, temperature, pressure, metallization rate, and basicity. Increasing the FeO and CaO contents led to an increase in the liquidus temperature. A high metallization rate of the pellets reduced the phosphorus removal rate but increased the carbon content of the final iron product. Increasing basicity restricted the migration of phosphorus and improved the rate of phosphorus removal. The optimum dephosphorization parameters were separation temperature of 1823 K, basicity of 2.0, and metallization rate of 82.3%. This study presents a high-efficiency and low-carbon method for smelting high-phosphorus iron ores.
The effect of freeze-thaw (F-T) cycles on the mechanical behaviors and internal mechanism of rock mass is a critical research topic. In permafrost or seasonally frozen regions, F-T cycles have adverse effects on the mechanical properties of rock mass, leading to many serious disasters in mining and geotechnical operations. In this paper, uniaxial compression tests are carried out on cyan sandstone after different F-T cycles. The failure modes and damage evolution of cyan sandstone under F-T cycles are studied. In addition, from the perspective of fracture and pore volume, the calculation equations of rock strain under frost heaving pressure and F-T cycles are established and verified with the corresponding laboratory tests. Subsequently, based on the classical damage theory, the F-T damage variables of cyan sandstone under different F-T cycles are calculated, and the meso-damage calculation model of cyan sandstone under F-T-loading coupling conditions is derived. Furthermore, through the discrete element numerical simulation software (PFC3D), the microscopic damage evolution process of cyan sandstone under uniaxial compression after F-T cycles is studied, including the change of microcracks number, distribution of microcracks, and the acoustic emission (AE) count. The goal of this study is to investigate the damage evolution mechanism of rock from the mesoscopic and microscopic aspects, which has certain guiding value for accurately understanding the damage characteristics of rock in cold regions.
Controlled laboratory experiments are proved to be a valuable tool for investigating changes in underground physical properties and the related response of surface geophysical signals. The self-potential (SP) method is widely used in mineral resource exploration due to its direct correlation with underground electrochemical gradients. This paper presented the design and construction of an experimental platform based on a multi-channel SP monitoring system. The proposed platform was used to monitor the anodizing corrosion process of different metal blocks from a laboratory perspective, record the real-time SP signal generated by the redox reaction, as well as investigate the geobattery mechanism associated with the natural polarization process of metal mineral resources. The experimental results demonstrate that the constructed SP monitoring platform effectively captures time-series SP signals and provides direct laboratory evidence for the geobattery model. The measured SP data were quantitatively interpreted using the simulated annealing algorithm, and the inversion results closely match the real model. This finding highlights the potential of the SP method as a promising tool for determining the location and spatial distribution of underground polarizers. The study holds reference value for the exploration and exploitation of mineral resources in both terrestrial and marine environments.
The seismic damage to ancillary facilities on high-speed railway (HSR) bridges can affect the normal movement of trains. To propose the bridge deck acceleration response spectra of the typical HSR simply-supported girder bridge for simplifying the seismic responses analysis of the facilities on bridges, the finite element models of the HSR multi-span simply-supported girder bridges with CRTSII track were established, and the numerical model was validated by tests. Besides, the effects of the span number, peak ground acceleration (PGA), pier height on the seismic acceleration and response spectra of the bridge deck were investigated. Afterward, the bridge acceleration amplification factor curves and bridge deck response spectra with different PGAs and pier heights were obtained. The formula for bridge deck acceleration amplification factor, with a 95% guarantee rate, was fitted. Moreover, the finite element models of the overhead contact lines (OCL) mounted on rigid base and bridges were established to validate the fitted formula. The results indicated that the maximum seismic acceleration response is in the midspan of the beam. The proposed formula for the bridge deck acceleration response spectra can be used to analyze the earthquake response of the OCL and other ancillary facilities on HSR simply-supported girder bridges. The bridge deck acceleration response spectra are conservative in terms of structural safety and can significantly improving the analysis efficiency.
Due to rainfall infiltration, groundwater activity, geological processes, and natural erosion, soil often exhibits heterogeneity and unsaturation. Additionally, seismic events can compromise slope stability. Existing analytical solutions typically consider a single failure mode, leading to inaccurate slope stability assessments. This study analyzes the impact of matric suction through three nonlinear shear strength models and adopts a heterogeneous soil model where cohesion linearly increases with depth. An improved pseudo-dynamic method is used to account for seismic effects. Based on a three-dimensional (3D) trumpet-shaped rotational failure mechanism, a new framework is established to analyze the stability of 3D two-bench slopes in heterogeneous unsaturated soil under seismic effects. The internal energy dissipation rate and external power at failure are calculated, and the gravity increase method is introduced to derive an explicit expression for the safety factor (F s). The results are compared with previously published results, demonstrating the effectiveness of the proposed method. Sensitivity analyses on different parameters are conducted, discussing the influence of various factors on F s. This study proposes a new formula for calculating the F s of 3D two-bench slopes in heterogeneous unsaturated soil under seismic effects, providing a practical application for slope engineering.
In the realm of high-speed railway bridge engineering, managing the intricacies of the track-bridge system model (TBSM) during seismic events remains a formidable challenge. This study pioneers an innovative approach by presenting a simplified bridge model (SBM) optimized for both computational efficiency and precise representation, a seminal contribution to the engineering design landscape. Central to this innovation is a novel model-updating methodology that synergistically melds artificial neural networks with an augmented particle swarm optimization. The neural networks adeptly map update parameters to seismic responses, while enhancements to the particle swarm algorithm’s inertial and learning weights lead to superior SBM parameter updates. Verification via a 4-span high-speed railway bridge revealed that the optimized SBM and TBSM exhibit a highly consistent structural natural period and seismic response, with errors controlled within 7%. Additionally, the computational efficiency improved by over 100%. Leveraging the peak displacement and shear force residuals from the seismic TBSM and SBM as optimization objectives, SBM parameters are adeptly revised. Furthermore, the incorporation of elastoplastic springs at the beam ends of the simplified model effectively captures the additional mass, stiffness, and constraint effects exerted by the track system on the bridge structure.
The residual elastic energy index is a scientific evaluation index for rockburst proneness. In laboratory test, it is sometimes difficult to obtain the post-peak curve or to test the rock sample several times, which makes it impossible to calculate the residual elastic energy index accurately. Based on 241 sets of experimental data and four input indexes of density, elastic modulus, peak intensity and peak input strain energy, this study proposed a machine learning model combining k-means clustering algorithm and random forest regression model: cluster forest (CF) model. The research employed a stratified sampling method on the dataset to ensure the representativeness and balance of the samples. Subsequently, grid search and five-fold cross-validation were utilized to optimize the model’s hyperparameters, aiming to enhance its generalization capability and prediction accuracy. Finally, the performance of the optimal model was evaluated using a test set and compared with five other commonly used models. The results indicate that the CF model outperformed the other models on the testing set, with a mean absolute error of 6.6%, and an accuracy of 93.9%. The results of sensitivity analyses reveal the degree of influence of each variable on rockburst proneness and the applicability of the CF model when the input parameters are missing. The robustness and generalization ability of the model were verified by introducing experimental data from other studies, and the results confirmed the reliability and applicability of the model. Therefore, the model not only effectively simplifies the acquisition of the residual elastic energy index, but also shows excellent performance and wide applicability.
In this paper, Brazilian test was performed on disk samples of analogue materials with defined structural planes. The surface strain evolution process of the disk samples during loading was analyzed via digital image correlation. The damage evolution process was explored from a microscopic perspective by combining discrete element numerical simulation technology. The criterion of the failure mode of the disc specimen in the split state was theoretically deduced. The influence of structural surface roughness and loading inclination angle on the stress state at the center of the specimen was explored. The results showed that the failure modes of the samples could be divided into three typical modes as matrix failure, structural plane failure and combination failure. The rough structural plane improves the failure strength of the specimen by limiting its lateral deformation, and the degree of improvement weakens continuously with the increase of the inclination angle of the structural plane. As the inclination angle of the structural plane increases, the main type of microcracks in the structural plane changes from shear microcracks to tensile microcracks. This study contributes to a better understanding of macro- and meso-failure characteristics of rock masses with structural planes under a splitting state.
Traditional track dynamic geometric state (TDGS) simulation incurs substantial computational burdens, posing challenges for developing reliability assessment approach that accounts for TDGS. To overcome these, firstly, a simulation-based TDGS model is established, and a surrogate-based model, grid search algorithm-particle swarm optimization-genetic algorithm-multi-output least squares support vector regression, is established. Among them, hyperparameter optimization algorithm’s effectiveness is confirmed through test functions. Subsequently, an adaptive surrogate-based probability density evolution method (PDEM) considering random track geometry irregularity (TGI) is developed. Finally, taking curved train-steel spring floating slab track-U beam as case study, the surrogate-based model trained on simulation datasets not only shows accuracy in both time and frequency domains, but also surpasses existing models. Additionally, the adaptive surrogate-based PDEM shows high accuracy and efficiency, outperforming Monte Carlo simulation and simulation-based PDEM. The reliability assessment shows that the TDGS part peak management indexes, left/right vertical dynamic irregularity, right alignment dynamic irregularity, and track twist, have reliability values of 0.9648, 0.9918, 0.9978, and 0.9901, respectively. The TDGS mean management index, i.e., track quality index, has reliability value of 0.9950. These findings show that the proposed framework can accurately and efficiently assess the reliability of curved low-stiffness track-viaducts, providing a theoretical basis for the TGI maintenance.