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关键词： 转向系统   军用汽车   维修方法   传动比  

摘要： 军用汽车的主动式转向系统,具有更高的机动性和灵活性,克服了既往军用汽车转向比固定不变的缺点,能够在更好适应复杂多变战场环境的同时,还可以改善车辆在转向过程中的反应,降低轮胎在转向极限时的压力。在功能上为驾驶者提供行驶舒适性的同时,也满足了汽车行驶安全性的要求。基于此,本文将以某军用车为例,对其转向系统原理进行分析,并针对当前该车型较为常见的三种故障进行维修方法方面的阐述。

关键词： 振动   风电机组   频谱分析   故障诊断  

摘要： 随着海上并网风电机组越来越多,海上风电机组机舱振动问题对机组安全稳定起到决定性作用。本文选取某MW级直驱式风电机组为分析对象,通过分析风速、转速、变桨核偏航对机组振动的影响,运行状态对机组振动的影响,采集不同工况下的机舱振动加速度进行离线分析,发现风速风向的突变对机组机舱左右振动影响较大、在偏航电机动作与变桨距动作时刻对机组机舱左右振动影响明显,风速的突变对机组机舱前后振动影响显著。

关键词： 工程机械   涂装车间   工程设计  

摘要： 涂装车间设计既要满足生产使用要求,又须符合节能环保等相关要求,其设计的合理性对整个项目的建设有着重要的作用。论文根据工程机械涂装工艺发展趋势和一种典型的绿色涂装工艺,论述了涂装车间如何进行工艺设计、土建设计、公辅系统及环保设施设计。

关键词： TBM   Discrete element   Disc-type cutterhead   Load characteristics   Specific energy   CUTTING PROCESS   TBM   PREDICTION   MECHANISM  

摘要： In order to study the rock-breaking process of a 17-inch disc-type cutterhead in a full-face tunnel boring machine (TBM), we utilized the discrete element method software EDEM to analyze the in-fluence of relevant parameters (cutter spacing, penetration depth, installation radius, phase difference) on rock-breaking loads and efficiency. Firstly, validate the rationality of the discrete element model for the disc cutter rock-breaking process. Subsequently, establish a simulation of sequential rock-breaking with multiple rotating disc cutters and analyze the load characteristics as well as specific energy consumption. Simulation results indicate that within certain parameter ranges, an increase in cutter penetration depth leads to an increase in average load force, while an increase in cutter spacing and installation radius initially results in an increase in average load force followed by a decrease. An increase in phase difference results in an initial decrease in average load force followed by an increase. Based on orthogonal tests, a combination of rock-breaking parameters with values of 100 mm for cutter spacing, 8 mm for penetration depth, 650 mm for installation radius, and 45 degrees for phase difference was found to yield lower specific energy consumption. This method can provide reference for cutter arrangement and cutterhead performance prediction.

关键词： Rock Engineering Parameters   Machine Learning   TBM   Hard Rock Condition   Reversed Analysis   TUNNEL BORING MACHINES   PENETRATION RATE   GEOLOGICAL CONDITION   OPERATION   PRESSURE   MODELS   FACE  

摘要： Knowing accurate values of rock engineering parameters in hard rock condition is the key in estimating and adjusting TBM performance. In this study, TBM penetration per revolution (Pr) as driving parameter, TBM operational parameters including cutterhead torque (Tq), field penetration index (FPI), thrust force (Th), cutter load (Fn), cutterhead rotational speed revolution per minute (RPM), and also cutterhead diameter as a mechanical machine specification are used to predict geomechanical parameters including rock quality designation (RQD) and unconfined compressive strength (UCS) based on reverse analysis in three types of strong rocks (Tuff, Andesite, Diorite) along the Southern lot of Kerman water transfer tunnel; KrWCT. A soft computing method called least squares-support vector machine (Ls-SVM), is trained using operating and driving data as well as rock engineering parameters gathered from the KrWCT project which is excavated in hard rock condition using double shield TBM. Due to the lack of access to the excavation face in mechanized tunneling, the collection of geomechanical data on the rock mass is possible only through the examination of excavated material and limited inspections during cutterhead repairs. Therefore, this paper has tried to predict and classify the rock engineering factors using some indicators related to machine performance, mechanical specification, and TBM operational parameters. Among the data collected during the pre-construction and construction phases of the project, the dataset was classified into 42 zones, and the reasonable distribution intervals of the main tunneling factors corresponding to each zone were defined. New empirical equations are developed to estimate the properties of igneous rocks based on linear and non-linear multivariable regression (MR). Several loss functions were used to assess the performance and precision of the applied methods. The results of the proposed models indicate an acceptable and reliable accurac

关键词： Forecasting  

摘要： Due to the uncertainty of geological conditions during the tunneling process, advanced prediction of TBM tunneling parameters is significant for evaluating operational safety and efficiency, especially for real-time prediction of key tunneling parameters during the steady phase of TBM operation. At present, although there are studies on constructing predictive models based on machine learning algorithms, multiparameter prediction consistent with the actual tunneling process remains challenging due to the complexity of the TBM tunneling process and the numerous tunneling parameters. Therefore, this paper proposes a real-time multiple tunneling parameters prediction method of TBM steady phase based on dual recurrent neural networks. Firstly, the irregular multidimensional time series of tunneling parameters are analyzed and processed, which are divided into an idle-push phase, a rising phase, and a steady phase; secondly, the parameters of rising phase are analyzed using a recurrent neural network, and the parameters relevant for constructing a real-time prediction model are screened; then, based on the screened parameters, the Bayesian-optimized gated recurrent unit (GRU, a kind of recursive neural network) is proposed to construct a real-time prediction model for the four key tunneling parameters during the steady phase. Finally, the effectiveness and practicality of the proposed method are demonstrated by verification on real TBM tunnel datasets and comparing it with the models constructed by six commonly used machine learning algorithms. The results of this paper show that the designed prediction method is able to achieve a good combination of performance in terms of accuracy and computational time-consumption, with an average prediction accuracy of 91.1% for the four parameters for different rock grades of geology, the multiparameter prediction time for 100 samples is only 11 ms. In addition, three current similar studies using deep learning methods were compared

关键词： Tunnel   Electrical resistivity   Geological risks   Numerical analysis   Machine learning algorithm   GROUND-PENETRATING RADAR   RESISTIVITY TOMOGRAPHY   CASE-HISTORY   FAULT ZONES   TBM   PERFORMANCE   TREE   SOIL  

摘要： In order to ensure construction efficiency and stability during tunnel excavation, it is essential to predict geological risks ahead of tunnel faces. In this study, a geological risk prediction model was developed based on a machine learning (ML) algorithm. The database used to implement the ML model was synthetically acquired from a series of finite-element (FE) numerical analyses, which could simulate electrical resistivity surveys during tunnel excavation. The developed FE model helped obtain resistivity data representing various risky ground conditions (such as typical fault zones, water intrusion, mixed ground, geological transitions, and cavities) encountered during tunnel advancement. Four ML algorithms (support vector machine, k-nearest neighbors, random forest, and extreme gradient boosting) were used to develop the prediction model. The evaluation results showed that the proposed ML prediction models produced highly accurate results. Among the ML algorithms, the prediction model based on the random forest (RF) algorithm exhibited superior performance, with an accuracy of 97.33%. Given the feasibility and efficiency of recognizing hazardous ground conditions, the proposed model is expected to serve as a reliable approach for risk management. Finally, an engineering flowchart was proposed to assist in the application of the study results to actual tunneling sites.

关键词： Earth pressure balance shield   Face pressure   Contraction   Soil settlement   Pile tunnel interaction  

摘要： With the increasing demand to alleviate traffic congestion, subway tunnels are becoming a prominent transportation solution. However, tunnel construction induces soil movement, potentially impacting the stability of nearby structures. In the current study, the impact of tunnel construction on nearby deep foundations has investigated through numerical modeling (PLAXIS 3D). First, surface settlements reported in a case history of tunneling boring machine (TBM) movement during the construction of Metro line 2 in China has used to validate the developed numerical model. Second, analytical methods to estimate the required parameters for the simulation of TBM movement in the finite element program were investigated and a sensitivity analysis was conducted to study the effect of varying each parameter on the evaluated surface settlements. Thus, the recommended methods to be followed in selecting the TBM simulation parameters were concluded. Finally, the effect of the TBM movement on deep foundation behavior (a group of piles) in sandy soil has been investigated. By varying the location of the TBM under the tip and beside the pile group, the variation of bearing reaction, total axial load, settlement of the piles, and pile cap deformations have been evaluated during the TBM movement.

关键词： Coal mine tunnel   Tunnel boring machine   Steel segment   Experimental tests   Numerical tests   FAILURE-MECHANISM   CONCRETE   ROADWAY   ROCK  

摘要： In order to solve the problem of surrounding rock support difficulties encountered during TBM excavation in weak strata in coal mines, a steel segment support structure for TBM-excavated tunnels in coal mines was proposed. Full-scale model tests of the steel segment support structure were conducted under different lateral stress coefficients. The deformation and failure modes, bearing capacity, and strain distribution characteristics of the proposed roadway support structure were studied, and the factors influencing the bearing capacity of the support structure were determined, revealing the mechanism of the instability and failure of the support structure. Subsequently, numerical tests of the support structure were conducted to obtain parameters such as the axial force and bending moment, which were difficult to monitor in the full-scale model tests. The results showed that the support structure first experienced local instability at the joints and mid-span, leading to overall instability of the structure. The ultimate bearing capacities of the support structure were 3,972.2 kN and 2,763.2 kN for lateral stress coefficients of lambda = 1.0 and 1.5, respectively. The ultimate bearing capacity decreases exponentially with increasing lateral stress coefficient, highlighting the importance of considering tectonic stress in design. Design parameters analysis reveals that rib plate thickness significantly influences bearing capacity, followed by rib plate width, while ring plate thickness has a minimal impact.

关键词： Tunnel look ahead   Velocity structure mapping   In-tunnel passive seismic   Continuous passive seismic inversion   WAVE-FORM INVERSION   EXPLORATION   PREDICTION   FRONT  

摘要： During the excavation of Tunnel Boring Machines (TBMs), potential hazards such as abrupt geological changes, obstacles like boulders and building foundations, fault zones, and water -bearing regions significantly increase the chances of cutter damage and tunneling accidents. To reduce the risks, a tunnel look ahead is necessary during the tunneling progress. Due to the inhomogeneous nature of the underground medium, geophysical investigation based on simplified homogeneity assumptions may lead to a inaccurate prediction results. We propose an inversion scheme using multiple passive seismic observations to invert for the velocity structure ahead of the TBM cutterhead, and hence to improve the accuracy and mitigate the ill-posedness of the inverse problem. We first lay out the theoretical foundation of the inverse problem and demonstrate the inversion on a simulated seismic dataset. Afterward, we present a field application of our inversion scheme where geological interfaces are predicted as the excavation progresses. The simulation test and field case study show that our inversion method can significantly improve the robustness of the prediction and provide accurate velocity map ahead of the TBM cutterhead.