A soft-sensing model on hydraulic excavator's backhoe vibratory excavating resistance based on fuzzy support vector machine

In order to measure the backhoe vibratory excavating resistance of a hydraulic excavator fast and precisely,the influences of vibratory excavating depth,angle,vibratory frequency,amplitude,bucket inserting velocity and soil type on the vibratory excavating resistance were analyzed.Simulation analysis was carded out to establish the bucket inserting velocity,amplitude and vibratory frequency considered as secondary variables and excavating resistance as primary variable.A fttzzy membership function was introduced to improve the anti-noise capacity of support vector machine,which is a soft-sensing model on the hydraulic excavator＇s backhoe vibratory excavating resistance based on fuzzy support vector machine.The simulation result reveals that its maximum relative training and testing error are nearly 0.68% and-0.47%,respectively.It is concluded that the model has quite high modeling precision and generalization capacity,and it can measure the vibratory excavating resistance accurately,reliably and fast in an indirect way.

Predicting the strut forces of the steel supporting structure of deep excavation considering various factors by machine learning methods

The application of steel strut force servo systems in deep excavation engineering is not widespread,and there is a notable scarcity of in-situ measured datasets.This presents a significant research gap in the field.Addressing this,our study introduces a valuable dataset and application scenarios,serving as a reference point for future research.The main objective of this study is to use machine learning(ML)methods for accurately predicting strut forces in steel supporting structures,a crucial aspect for the safety and stability of deep excavation projects.We employed five different ML methods:radial basis function neural network(RBFNN),back propagation neural network(BPNN),K-Nearest Neighbor(KNN),support vector machine(SVM),and random forest(RF),utilizing a dataset of 2208 measured points.These points included one output parameter(strut forces)and seven input parameters(vertical position of strut,plane position of strut,time,temperature,unit weight,cohesion,and internal frictional angle).The effectiveness of these methods was assessed using root mean square error(RMSE),correlation coefficient(R),and mean absolute error(MAE).Our findings indicate that the BPNN method outperforms others,with RMSE,R,and MAE values of 72.1 kN,0.9931,and 57.4 kN,respectively,on the testing dataset.This study underscores the potential of ML methods in precisely predicting strut forces in deep excavation engineering,contributing to enhanced safety measures and project planning.

Modelling the performance of EPB shield tunnelling using machine and deep learning algorithms

This paper introduces an intelligent framework for predicting the advancing speed during earth pressure balance(EPB)shield tunnelling.Five artificial intelligence(AI)models based on machine and deep learning techniques-back-propagation neural network(BPNN),extreme learning machine(ELM),support vector machine(SVM),long-short term memory(LSTM),and gated recurrent unit(GRU)-are used.Five geological and nine operational parameters that influence the advancing speed are considered.A field case of shield tunnelling in Shenzhen City,China is analyzed using the developed models.A total of 1000 field datasets are adopted to establish intelligent models.The prediction performance of the five models is ranked as GRU>LSTM>SVM>ELM>BPNN.Moreover,the Pearson correlation coefficient(PCC)is adopted for sensitivity analysis.The results reveal that the main thrust(MT),penetration(P),foam volume(FV),and grouting volume(GV)have strong correlations with advancing speed(AS).An empirical formula is constructed based on the high-correlation influential factors and their corresponding field datasets.Finally,the prediction performances of the intelligent models and the empirical method are compared.The results reveal that all the intelligent models perform better than the empirical method.

Rock burst prediction based on genetic algorithms and extreme learning machine

Rock burst is a kind of geological disaster in rock excavation of high stress areas.To evaluate intensity of rock burst,the maximum shear stress,uniaxial compressive strength,uniaxial tensile strength and rock elastic energy index were selected as input factors,and burst pit depth as output factor.The rock burst prediction model was proposed according to the genetic algorithms and extreme learning machine.The effect of structural surface was taken into consideration.Based on the engineering examples of tunnels,the observed and collected data were divided into the training set,validation set and prediction set.The training set and validation set were used to train and optimize the model.Parameter optimization results are presented.The hidden layer node was450,and the fitness of the predictions was 0.0197 under the optimal combination of the input weight and offset vector.Then,the optimized model is tested with the prediction set.Results show that the proposed model is effective.The maximum relative error is4.71%,and the average relative error is 3.20%,which proves that the model has practical value in the relative engineering.

Identification on rock and soil parameters for vibration drilling rock in metal mine based on fuzzy least square support vector machine

A single freedom degree model of drilling bit-rock was established according to the vibration mechanism and its dynamic characteristics. Moreover, a novel identification method of rock and soil parameters for vibration drilling based on the fuzzy least squares(FLS)-support vector machine(SVM) was developed, in which the fuzzy membership function was set by using linear distance, and its parameters, such as penalty factor and kernel parameter, were optimized by using adaptive genetic algorithm. And FLS-SVM identification on rock and soil parameters for vibration drilling was made by changing the input/output data from single freedom degree model of drilling bit-rock. The results of identification simulation and resonance column experiment show that relative error of natural frequency for some hard sand from identification simulation and resonance column experiment is 1.1% and the identification precision based on the fuzzy least squares-support vector machine is high.

小断面土石组合地质条件下TBM施工围岩可掘性分级识别

围岩可掘性分级以及识别研究对隧道掘进机(TBM)高效率施工及智能化控制意义重大。依托南水北调安阳市西部调水工程TBM施工实际数据,利用掘进性能综合指标单位贯入度推力(FPI)、单位贯入度扭矩(TPI)建立了小断面土石组合地质条件下TBM施工围岩可掘性分级标准;提出了PCA-RF模型对围岩可掘性分级进行识别,并与BP、SVR和RF模型进行了比较讨论。结果表明:①建立的小断面土石组合围岩TBM施工可掘性分级标准是适用的,克服了土石组合围岩下传统围岩分类方法的局限性;②小断面土石组合围岩TBM施工可掘性分级PCA-RF识别模型的识别准确率达到了98.3%,高于BP、SVR和RF模型,可以满足工程施工需要。

竖井巷道掘进超前地质探测研究进展与展望

以掘进机为代表的机械破岩是未来竖井巷道掘进技术发展的方向,为保障竖井巷道快速机械智能化掘进的安全,超前地质探测是不可或缺的工序。从探测范围、适用条件以及优势不足等方面对常规超前地质探测技术和随掘超前探测技术的发展现状与特点进行了分类总结,常规超前探测技术各有一定的适用范围,在钻爆法施工环境中得到了较好地应用。在面对掘进机复杂的施工环境时,常规超前探测技术难以适用。而随掘超前探测可以实现掘进与超前地质探测同步进行,实时预测工作面前方的不良地质,是井巷机械智能化掘进超前地质探测技术研究的重点。其中,竖井全断面掘进机是综合机械化凿井的发展方向和趋势,但其施工环境非常复杂,基于掘进机破岩震源地震波的超前地质探测是有效的预测方法。针对全断面竖井掘进机破岩震源超前探测方法的难点,即施工环境和破岩震源地震波场的双重复杂性,从多个方面提出了解决思路:针对震源先导信号,采用多种方法联合去噪,压制破岩震源干扰波;对于地震记录信号,采用以互相关为核心的地震记录重构方法恢复有效波场;开展竖井全空间三维立体探测和高精度成像的研究等。此外,开展多种随掘物探方法联合反演能够提高地质判识的可靠性和解释精度。研发竖井掘进机的掘探一体化装备是未来深入研究的方向。

开挖补偿法防控深部地下岩爆灾害——引汉济渭工程秦岭输水隧洞案例分析

Rockburst disasters occur frequently during deep underground excavation,yet traditional concepts and methods can hardly meet the requirements for support under high geo-stress conditions.Consequently,rockburst control remains challenging in the engineering field.In this study,the mechanism of excavation-induced rockburst was briefly described,and it was proposed to apply the excavation compensation method(ECM)to rockburst control.Moreover,a field test was carried out on the Qinling Water Conveyance Tunnel.The following beneficial findings were obtained:Excavation leads to changes in the engineering stress state of surrounding rock and results in the generation of excess energy DE,which is the fundamental cause of rockburst.The ECM,which aims to offset the deep excavation effect and lower the risk of rockburst,is an active support strategy based on high pre-stress compensation.The new negative Poisson’s ratio(NPR)bolt developed has the mechanical characteristics of high strength,high toughness,and impact resistance,serving as the material basis for the ECM.The field test results reveal that the ECM and the NPR bolt succeed in controlling rockburst disasters effectively.The research results are expected to provide guidance for rockburst support in deep underground projects such as Sichuan-Xizang Railway.

煤矿复杂地层TBM掘进巷道新型装配式支护结构工程实践

TBM(Tunnel Boring Machine)全断面掘进机已在煤矿深井巷道掘进工程中成功应用,取得了显著的社会经济效益。但仍存在影响其进一步推广应用的技术瓶颈。针对当前支护结构与支护技术难以同时满足支护效率、支护强度和施工成本方面的要求,TBM难以充分发挥其速度优势的技术瓶颈,研究团队研发了新型钢管片型装配式支护结构。开展了新型装配式支护结构大比尺模型试验,获得新型支护结构在受载条件下变形破坏规律,根据试验结果优化新型支护结构型式。基于新型钢管片支护下TBM掘进巷道数值模拟评估新型支护结构可靠性。最后开展工业性试验,现场验证新型钢管片式支护结构用于煤矿TBM掘进巷道的可行性。试验结果表明,巷道围岩最大拉应变为803×10-6,钢管片支护结构变形量小于1 mm。新型支护结构安装速度快,可在90 min内掘进1.5 m并完成一环管片的安装工作,显著提高了TBM掘进巷道的支护强度和TBM掘进作业线的地层适应性。为未来进一步提高煤矿巷道掘进速度、保障煤矿采掘接替提供技术参考。

矿用正铲液压挖掘机人机协同控制

针对矿用正铲液压挖掘机操纵复杂度高的问题,提出了一种通用型人机协同控制方法。对比分析了3种能够降低正铲液压挖掘机操纵复杂度的新型工作装置在动臂提升和水平推压作业模式下的作业性能,结果表明,工作装置创新设计存在机构复杂和作业精度低的问题。基于运动学逆解和切换控制思想,所提方法采用驾驶员手动操纵主要执行机构,其他辅助执行机构根据作业要求约束进行自主协同的策略,在保证作业精度的同时,减小了驾驶员操纵难度。结果表明,所提方法能够降低正铲液压挖掘机在动臂提升和水平推压作业模式下的操纵复杂度,提高作业精度和效率,具有模式切换灵活、通用性强等优点。

煤矿岩巷TBM掘进随掘地震信号特征及其应用

随掘地震超前探测技术可实现探掘平行,为巷道快速智能掘进场景下实时、精准地质保障提供了可能。随掘震源产生的是复杂、变频、连续信号,信号特征认知直接影响数据处理与成像精度,而目前针对岩巷全断面掘进机(TBM)随掘地震信号特征的认知仍不清晰,且暂时还没有针对性开展过信号处理与成像研究工作。针对上述问题,以谢桥煤矿瓦斯治理巷TBM随掘地震超前探测试验为例,分析了刀盘先导信号与岩壁接收信号的时间域、频率域及时频域特征:岩巷TBM随掘地震信号中不同振幅能量成分比例呈现金字塔形,但分布随机,不对称程度较高;机械运转信号能量较大,刀盘先导信号强度是岩壁接收信号的200倍左右;频率域变频特征明显;机械运转信号基础频率较低,刀盘先导信号频率成分主要集中在10~80 Hz与150~200 Hz,主频为36.99 Hz,岩壁接收信号频率成分主要集中在50~200 Hz,主频为137.97 Hz;刀盘先导信号较岩壁接收信号时频域能量团分布更为规则,多次震源激发现象明显,能量团之间的差异性特征表明了多次震源激发时振幅能量与持续时间的随机性。利用脉冲化算法与绕射叠加偏移成像方法对岩巷TBM随掘地震信号进行数据处理与成像试验,结果表明:(1)脉冲化等效单炮记录与利用常规震源得到的超前探测单炮记录特征一致性较强,同相轴清晰且连续性较好,可满足现场探测分析需要。(2)对探测范围内岩体情况的超前预报结果与实际揭露情况一致,说明岩巷TBM随掘地震超前探测可提供有效地质保障。

不同岩体条件下双护盾TBM施工性能分析

针对复杂地质条件下城市地铁隧道双护盾TBM的掘进性能评价,依托青岛地铁6号线创石区间左、右线TBM施工段,对全线施工数据进行收集与分析,研究不同围岩类别下双护盾TBM掘进参数与施工性能参数的变化规律。不同围岩类别下TBM利用率分析表明,双护盾TBM对岩体质量较差的围岩段具有良好适应性。分析了不同围岩类别下各掘进参数与工程破岩比能SE间关系,贯入度与SE的相关性最好,更适合作为评价TBM掘进过程中能量消耗的指标,且在不同围岩类别下,贯入度与工程破岩比能SE呈较好的负幂指数关系,在Ⅱ,Ⅲ,Ⅳ,Ⅴ类围岩类别下,当贯入度分别>2,5,8,10mm/r时,其对应的工程破岩比能SE在较低水平。

掘锚机外喷雾系统的改进设计

外喷雾系统是掘锚机的重要部分,位于截割部上方,对改善工作环境有着非常重要的作用。针对现有外喷雾系统的不足,通过增大系统的压力和流量、改变喷水座、喷嘴的形状和布置方式等方法,设计出一种新型的全断面外喷雾系统,并对其结构和具体实施方案进行详细的阐述。

From tunnel boring machine to tunnel boring robot: perspectives on intelligent shield machine and its smart operation

Advances in intelligent shield machines reflect an evolving trend from traditional tunnel boring machines(TBMs)to tunnel boring robots(TBRs).This shift aims to address the challenges encountered by the conventional shield machine industry arising from construction environment and manual operations.This study presents a systematic review of intelligent shield machine technology,with a particular emphasis on its smart operation.Firstly,the definition,meaning,contents,and development modes of intelligent shield machines are proposed.The development status of the intelligent shield machine and its smart operation are then presented.After analyzing the operation process of the shield machine,an autonomous operation framework considering both stand-alone and fleet levels is proposed.Challenges and recommendations are given for achieving autonomous operation.This study offers insights into the essence and developmental framework of intelligent shield machines to propel the advancement of this technology.

黄陵二矿辅运巷快速掘进系统装备研究

巷道的快速掘进有助于回采工作面早日开采,缓和矿井采掘交替的紧张状况。黄陵煤矿215辅运巷掘进范围内地质条件复杂,局部地段煤岩体破碎、围岩支护困难、费时、危险度高,从而提出采用基于智能控制的掘锚一体化快速掘进系统来实现回采巷道的快速、安全掘进。系统配套设备包括掘锚机、跨骑式锚杆钻车、可弯曲皮带转载机、迈步自移机尾、干式除尘系统(选配)、智能控制系统。现场验证表明,黄陵二号煤矿215辅运巷每月进尺能达到300 m以上,比一般综掘机掘进效率高1倍多,实现巷道的快速掘进,降低工人工作量,提高掘进安全性。

钩掘机支重轮漏油故障研究分析

我司供货OEM客户75T挖掘机用支重轮,终端客户改动工作装置,将常规铲斗改为金刚臂,即将挖掘机改装成钩掘机。在终端客户的使用过程中,出现整台车18个支重轮不同程度的高温漏油情况,导致整机无法正常工作。通过理论计算,结合实验室支重轮磨合试验,得出结论。同时将成果应用于支重轮磨损研究之中,为售后与客户谈判时提供了理论依据。

掘锚机用可升降帮锚杆钻机

介绍了一种可升降的帮锚杆钻机,该钻机包括联接底座、主架体、升降滑块、帮钻机主体、油缸等,主架体设于联接底座上,并与联接底座滑动联接;升降滑块与主架体滑动联接,钻机主体与升降滑块相联。该钻机回转间隙小,摆动精度高,且整体机构更紧凑、占用空间小,灵活性和效率更高。

煤矿双巷连掘工作面智能化锚杆钻车研究与应用

基于现有四臂顶锚杆钻车、六臂帮锚杆顶锚索钻车,对其进行智能化升级改造,研制智能化四臂顶锚杆钻车、智能化六臂帮锚杆顶锚索钻车,并在双巷连掘示范工作面开展试验应用。结果表明:2种钻车实现了顶/帮锚杆、顶锚索支护钻孔施工的电液智能化控制,在局部少量人工辅助下完成顶/帮锚杆支护钻孔、锚固全流程自动化施工,起到了减员、提效作用,为煤炭行业智能化转型提供技术支撑和有益参考。

复合地层沉井式竖井机械法掘进施工技术

竖井建井技术快速发展,但随之也带来了一系列的技术难题,尤其是施工地点在中心城区,建筑密度大,空间有限。针对于该问题,本文介绍了一种新型竖井施工工艺,复合地层下采用沉井式竖井机械法掘进,采用预制管片拼装成井的方法,重点探讨机械法竖井的施工工艺和原理,预制管片的设计和拼装方法等。通过收集施工过程中的各种记录,进行总结分析,找出影响复合地层中施工效率的因素,进行设备改进和工艺改良,有效地提升了施工效率,在投资节约、工期缩短、质量控制等方面都得到了提升,为今后该技术的应用和发展奠定了基础。

含砾砂岩地层下盾构隧道掌子面工作洞开挖技术

盾构机刀盘发生磨损会导致盾构掘进困难,严重影响了施工效率。刀盘修复作业需要在盾构隧道掌子面位置开挖工作洞,为人工修复刀盘提供站立行走的操作空间。为此,以深江铁路SJSG-1标段盾构项目为例,提出一种盾构隧道掌子面工作洞开挖技术,重点阐述了作业人员进仓流程、掌子面工作洞开挖技术、洞门凿除施工注意事项等技术要点,为后续盾构机施工提供借鉴。