Impact point prediction guidance of ballistic missile in high maneuver penetration condition

An impact point prediction(IPP) guidance based on supervised learning is proposed to address the problem of precise guidance for the ballistic missile in high maneuver penetration condition.An accurate ballistic trajectory model is applied to generate training samples,and ablation experiments are conducted to determine the mapping relationship between the flight state and the impact point.At the same time,the impact point coordinates are decoupled to improve the prediction accuracy,and the sigmoid activation function is improved to ameliorate the prediction efficiency.Therefore,an IPP neural network model,which solves the contradiction between the accuracy and the speed of the IPP,is established.In view of the performance deviation of the divert control system,the mapping relationship between the guidance parameters and the impact deviation is analysed based on the variational principle.In addition,a fast iterative model of guidance parameters is designed for reference to the Newton iteration method,which solves the nonlinear strong coupling problem of the guidance parameter solution.Monte Carlo simulation results show that the prediction accuracy of the impact point is high,with a 3 σ prediction error of 4.5 m,and the guidance method is robust,with a 3 σ error of 7.5 m.On the STM32F407 singlechip microcomputer,a single IPP takes about 2.374 ms,and a single guidance solution takes about9.936 ms,which has a good real-time performance and a certain engineering application value.

Utilizing partial least square and support vector machine for TBM penetration rate prediction in hard rock conditions

Rate of penetration(ROP) of a tunnel boring machine(TBM) in a rock environment is generally a key parameter for the successful accomplishment of a tunneling project. The objectives of this work are to compare the accuracy of prediction models employing partial least squares(PLS) regression and support vector machine(SVM) regression technique for modeling the penetration rate of TBM. To develop the proposed models, the database that is composed of intact rock properties including uniaxial compressive strength(UCS), Brazilian tensile strength(BTS), and peak slope index(PSI), and also rock mass properties including distance between planes of weakness(DPW) and the alpha angle(α) are input as dependent variables and the measured ROP is chosen as an independent variable. Two hundred sets of data are collected from Queens Water Tunnel and Karaj-Tehran water transfer tunnel TBM project. The accuracy of the prediction models is measured by the coefficient of determination(R2) and root mean squares error(RMSE) between predicted and observed yield employing 10-fold cross-validation schemes. The R2 and RMSE of prediction are 0.8183 and 0.1807 for SVMR method, and 0.9999 and 0.0011 for PLS method, respectively. Comparison between the values of statistical parameters reveals the superiority of the PLSR model over SVMR one.

Physics-data coupling-driven method to predict the penetration depth into concrete targets

The projectile penetration process into concrete target is a nonlinear complex problem.With the increase ofexperiment data,the data-driven paradigm has exhibited a new feasible method to solve such complex prob-lem.However,due to poor quality of experimental data,the traditional machine learning(ML)methods,whichare driven only by experimental data,have poor generalization capabilities and limited prediction accuracy.Therefore,this study intends to exhibit a ML method fusing the prior knowledge with experiment data.The newML method can constrain the fitting to experimental data,improve the generalization ability and the predic-tion accuracy.Experimental results show that integrating domain prior knowledge can effectively improve theperformance of the prediction model for penetration depth into concrete targets.

Predicting TBM penetration rate in hard rock condition:A comparative study among six XGB-based metaheuristic techniques

A reliable and accurate prediction of the tunnel boring machine(TBM)performance can assist in minimizing the relevant risks of high capital costs and in scheduling tunneling projects.This research aims to develop six hybrid models of extreme gradient boosting(XGB)which are optimized by gray wolf optimization(GWO),particle swarm optimization(PSO),social spider optimization(SSO),sine cosine algorithm(SCA),multi verse optimization(MVO)and moth flame optimization(MFO),for estimation of the TBM penetration rate(PR).To do this,a comprehensive database with 1286 data samples was established where seven parameters including the rock quality designation,the rock mass rating,Brazilian tensile strength(BTS),rock mass weathering,the uniaxial compressive strength(UCS),revolution per minute and trust force per cutter(TFC),were set as inputs and TBM PR was selected as model output.Together with the mentioned six hybrid models,four single models i.e.,artificial neural network,random forest regression,XGB and support vector regression were also built to estimate TBM PR for comparison purposes.These models were designed conducting several parametric studies on their most important parameters and then,their performance capacities were assessed through the use of root mean square error,coefficient of determination,mean absolute percentage error,and a10-index.Results of this study confirmed that the best predictive model of PR goes to the PSO-XGB technique with system error of(0.1453,and 0.1325),R^(2) of(0.951,and 0.951),mean absolute percentage error(4.0689,and 3.8115),and a10-index of(0.9348,and 0.9496)in training and testing phases,respectively.The developed hybrid PSO-XGB can be introduced as an accurate,powerful and applicable technique in the field of TBM performance prediction.By conducting sensitivity analysis,it was found that UCS,BTS and TFC have the deepest impacts on the TBM PR.

Hybrid ensemble soft computing approach for predicting penetration rate of tunnel boring machine in a rock environment

This study implements a hybrid ensemble machine learning method for forecasting the rate of penetration(ROP) of tunnel boring machine(TBM),which is becoming a prerequisite for reliable cost assessment and project scheduling in tunnelling and underground projects in a rock environment.For this purpose,a sum of 185 datasets was collected from the literature and used to predict the ROP of TBM.Initially,the main dataset was utilised to construct and validate four conventional soft computing(CSC)models,i.e.minimax probability machine regression,relevance vector machine,extreme learning machine,and functional network.Consequently,the estimated outputs of CSC models were united and trained using an artificial neural network(ANN) to construct a hybrid ensemble model(HENSM).The outcomes of the proposed HENSM are superior to other CSC models employed in this study.Based on the experimental results(training RMSE=0.0283 and testing RMSE=0.0418),the newly proposed HENSM is potential to assist engineers in predicting ROP of TBM in the design phase of tunnelling and underground projects.

A systematic review of machine learning modeling processes and applications in ROP prediction in the past decade

Fossil fuels are undoubtedly important, and drilling technology plays an important role in realizing fossil fuel exploration;therefore, the prediction and evaluation of drilling efficiency is a key research goal in the industry. Limited by the unknown geological environment and complex operating procedures, the prediction and evaluation of drilling efficiency were very difficult before the introduction of machine learning algorithms. This review statistically analyses rate of penetration(ROP) prediction models established based on machine learning algorithms;establishes an overall framework including data collection, data preprocessing, model establishment, and accuracy evaluation;and compares the effectiveness of different algorithms in each link of the process. This review also compares the prediction accuracy of different machine learning models and traditional models commonly used in this field and demonstrates that machine learning models are the most effective technical means in current ROP prediction modeling.

Fault Zone Fluid Deep-penetrating Techniques for Potash Deposit Prediction in Lanping-Simao Basin,Yunnan,South China

In China,the strategic resource potash is suffering from severe shortages,and the ancient marine solid potash locating is still a problem of long impregnability.Till now,only the Mengyejing Potash Deposit was found

应用LSTM神经网络实现机械钻速(ROP)预测及异常值检测的方法研究

机械钻速(ROP)是反映钻井效率和工程风险的一个重要参数,对于高度非线性的钻井系统,传统的钻速方程和全连接神经网络计算的方法无法全面考虑影响ROP的工程因素,应用时间序列技术的LSTM神经网络方法构建机械钻速预测模型,通过优化LSTM网络结构和预测长度提高模型预测精确度,研究发现ROP预测范围在10m以内较为准确,以模型误差的高斯分布为参考提出机械钻速异常值检测的方法。

滇中引水工程隧洞综合超前地质预报分析及应用

针对隧洞施工过程中不同工程地质条件下何种超前地质预报方法预测更为准确、更为适用和单一方法精确度不够、适用性不强的问题,分别介绍了基于地震波法的长距离隧洞超前地质预报技术和基于电磁法的中、短距离隧洞超前地质预报技术原理及特点,依据其工作原理、工作方式、预报距离等特点,探讨了不同超前地质预报方法的优、缺点及其最适用的工作场景,总结出一套实用性强的综合超前地质预报流程。依托滇中引水工程大理Ⅱ段两个隧洞的工程实例,利用所提出的综合超前地质预报流程,分别介绍了结合长距离的隧道地震预报(TSP)法、中短距离的瞬变电磁法和探地雷达法对断层破碎带和裂隙水等不良地质体的综合预报研究。首先,通过长距离预报方法对地质灾害危险程度进行分级、识别高危地段,再通过短距离预报方法对不良地质体进行更精确的识别与定位,综合分析3种超前地质预报方法的地球物理响应特征,可以推断出不良地质体的类型及其空间分布特征;其次,通过探地雷达探测溶蚀波场特征的案例分析,可以推断出溶蚀的物性特征、规模及其空间分布特征。预报结果与隧洞掌子面开挖揭示的结果基本一致,验证了3种超前地质预报方法预报隧洞不良地质体的可靠性,也证实了所提出的综合超前地质预报流程的实用性,有助于指导物探工作者对不良地质体进行更精确的预报,确保隧洞施工安全。

基于探地雷达的沥青路面压实度检测研究综述及展望

沥青路面施工过程中,压实质量是影响其使用性能的重要因素,压实不足或压实过度均会使得沥青路面过早出现损坏。为推动探地雷达(GPR)在沥青路面压实度检测领域的发展,更全面地评价沥青路面的压实质量,系统阐述了探地雷达检测原理、系统组成及数据处理方法;对探地雷达在沥青路面压实度检测领域的研究现状进行了归纳和总结。研究结果表明:沥青混合料的介电特性受其组成成分体积比、雷达频率及路面温度等因素的影响;通过建立复合介电模型可以预测沥青混合料的压实度,但其精确性和适用范围仍有待提高;对于含水沥青混合料,水的存在会使得介电常数测量值偏大,从而影响密度预测的准确性。未来可优化探地雷达信号处理手段,提升探地雷达对沥青混合料介电常数的检测精度;进一步建立具有广泛适用范围的复合介电模型,提高密度预测的精确性和适用范围;将探地雷达与压路机进行结合,建立完善的沥青路面压实度实时监测系统。

基于LSTM神经网络深度序列机械钻速实时预测

机械钻速是钻井优化、缩短钻井周期的关键因素,传统的机械钻速预测大多是在钻井后进行钻井分析,预测效率和精度低、地层适用性不广。为了以更高效的方法预测得到高精度机械钻速,提出基于长短期记忆(LSTM)神经网络的深度序列机械钻速预测方法。采集实时钻井数据集,使用皮尔逊相关系数衡量各特征之间的相关性,筛选出井深、伽玛射线、地层密度、孔隙压力、井径、钻时、排量、钻井液密度等8个参数。构建LSTM神经网络模型,训练LSTM模型并预测ROP,对预测结果进行分析,并用决定系数(R^(2))、均方根误差(RMSE)、平均绝对百分比误差(MAPE)等指标对LSTM模型、BP模型和SVM模型性能进行对比分析。结果表明:LSTM模型其R^(2)、RMSE和MAPE的值分别为0.948、1.151和17.075,相较于BP模型和SVM模型,其R^(2)更大,RMSE和MAPE较小,说明LSTM模型预测性能更好。该方法有助于钻井工程师和决策者提前获得钻井信息,从而更好地规划钻井作业,缩短钻井周期,同时为钻井参数预测提供新的途径,能改善以往预测方法在处理复杂地层问题时效率不高、预测精度低等问题。

基于机器学习-网格搜索优化的砂土液化预测

砂土液化是一种破坏力较强的地震次生灾害,传统的砂土液化判别方法存在一定的局限性。提出两种液化判别方法,第一种是基于新西兰岩土数据库(New Zealand Geotechnical Database,NZGD)中519组静力触探试验数据,建立具有砂土液化预测功能的机器学习模型。首先建立支持向量机(support vector machine,SVM)、随机森林(random forest,RF)、XGboost(eXtreme gradient boosting,XGB)三种机器学习分类模型,运用网格搜索(GridSearchCV)法进行超参数优化后,采用整体精度(overall accuracy,OA)、精确率(P)、召回率(R)、F_(1)值对模型进行性能评估,对历史液化数据进行模型验证并与国内外方法进行结果对比。第二种是基于历史震害数据,采用经验判断法确定的静力触探初判条件。结果表明:随机森林可作为一种具有较强的预测能力的液化判别模型,通过与国内《岩土工程勘察规范》以及国际Olsen方法进行对比,选取要素简便且计算迅速的随机森林能够达到与上述两种权威方法接近的准确性,是一种可实行的液化判别模型;此外,基于历史液化数据库归结出不同烈度下,具备液化埋深限制的锥尖阻力阈值,经数据验证在7烈度区、8烈度区、9烈度区的准确率良好,与《岩土工程勘察规范》进行对比发现有可操作性好、可解释性强、可适用性广等优点。所建立的模型对砂土液化预测具有较强的适用性,静力触探初判条件亦可作为快速液化判别的参考值,两种方法结合可对科学研究和工程建设提供较好的参考价值。

基于HSP⁃GPR法的隧道三维超前地质预报技术及应用

隧道施工中,岩溶致灾构造容易引发岩溶涌突水、坍方、地表塌陷等地质灾害,严重威胁隧道施工安全。因此,进行超前地质预报对确保岩溶区域隧道施工安全至关重要。从岩溶隧道施工地质灾害、致灾构造及致灾模式为出发点,基于地震波法与电法联合探测技术进行岩溶隧道超前预报研究,并将其应用于某新建铁路隧道。研究结果表明,地震反射法(HSP)与电磁波反射法(GPR)可互为补充,相互验证。地震联合反演在电阻率反演的帮助下能有效还原溶洞的大致形态与位置,同时低速界面内部波速形态更加均匀。对HSP探测结果中存在异常的里程范围,GPR法可用于进一步确定异常的规模和性质。通过对比验证和综合分析地下介质中多种物探参数(速度、介电/电阻率)的差异性,可获取隧道前方不良地质体的详细规模和可靠性质,从而识别和预测岩溶致灾构造和致灾模式。基于HSP-GPR联合探测技术的岩溶隧道超前预报研究为制定防治措施和建立监测系统提供了重要的支持和指导,可为类似岩溶隧道工程提供参考。

基于Stacking集成学习的机械钻速预测方法

机械钻速是评估石油天然气钻井作业效率的重要指标。为准确预测新疆工区某油田钻井机械钻速,基于该工区的历史钻井数据,利用局部离群因子检测算法对数据进行预处理,建立了基于Stacking集成学习的机械钻速预测模型,该模型通过Stacking集成策略融合K近邻算法(KNN)、支持向量机算法(SVM)和随机森林算法(RF)进行预测验证。预测验证结果显示,分类准确度不高。运用遗传算法进行各基础模型参数优化。优化后,基于KNN、SVM、RF及Stacking集成4种算法,预测机械钻速准确率分别为73.7%、78.9%、81.6%及97.4%,其中Stacking集成模型预测准确率最高。基于Stacking集成学习的机械钻速预测方法开发了机械钻速预测软件,运用软件预测其他2套施工参数下的机械钻速,结果表明,预测机械钻速与实际机械钻速一致,且性能稳定,表明该模型拥有较强的泛化性和较高的预测精度。该智能算法可为新疆工区的该油田机械钻速预测与钻井施工参数优化提供一种新手段。

基于注意力机制的卷积神经网络机械钻速预测方法

传统机器学习方法在进行机械钻速(rate of penetration,ROP)预测时,受复杂特征提取和人为认知局限性的影响,难以满足现场预测精度要求。基于此,提出一种特征提取和回归预测相结合的机械钻速预测方法。首先,采用箱型图和独热编码对钻井实测数据进行预处理,清除异常数据并将离散特征连续化。其次,应用卷积神经网络(convolutional neural network,CNN)挖掘数据特征,并在网络中引入通道注意力机制(squeeze-and-excitation network,SENet),实现对CNN特征通道重要性程度的合理分配,建立SE-CNN机械钻速预测模型。最后,将SE-CNN模型与CNN模型进行对比分析,结果表明:SE-CNN模型的拟合优度提高了2.1%,平均绝对误差和均方根误差分别降低了1.1%和1.5%。SE-CNN模型具有更高的预测精度,可以用于现场机械钻速预测,为钻井提速提供科学参考。

煤矿井下钻进速度影响因素及其智能预测方法研究

在煤矿井下钻探领域,钻进速度(DR)是评估钻探作业最有效的指标之一,钻速预测是实现煤矿钻进智能化的前提条件,对于优化钻机钻进参数、降低作业成本、实现安全高效钻探具有重要意义。为此,提出煤矿井下钻进速度影响因素及其智能预测方法研究,探索基于钻压、转速、扭矩以及钻进深度等少量钻机参数采用机器学习算法实现钻进速度精准预测。首先通过实验室微钻试验,深入分析煤岩力学性能、钻压、转速和钻进深度对扭矩、钻进速度影响规律。研究结果显示,在煤矿井下钻进过程中,随着钻进压力增大,钻进速度呈逐渐升高趋势,在较高的转速条件下钻进压力对钻进速度影响更加明显,转速增加有利于提高钻进速度,但转速对硬度较低的煤层钻进速度影响更为显著;然后,根据煤矿井下防冲钻孔现场数据,采用K–近邻(KNN)、支持向量回归(SVR)和随机森林回归(RFR)3种不同的机器学习算法建立钻进速度预测模型,并结合粒子群算法(PSO)对3种模型超参数进行优化,最后对比分析PSO–KNN,PSO–SVR和PSO–RFR三种钻进速度预测模型预测结果。研究结果表明,PSO–RFR模型准确性最好,决定系数R2高达0.963,均方误差MSE仅有29.742,而PSO–SVR模型鲁棒性最好,在对抗攻击后评价指标变化率最小。本文研究有助于实现煤矿井下钻进速度的精准预测,为煤矿井下智能钻进参数优化提供理论支撑。

基于VMD与加权RF的TBM掘进速度预测SHAP解释模型

为较准确地实现TBM掘进速度(PR)的预测,构建一套基于加权随机森林(RF)结合变分模态分解(VMD)的集成学习预测模型。模型建立过程中,通过收集来自KS隧洞与兰州水源地输水隧洞中涵盖不同岩性下的数据,利用VMD对数据进行4次模态分解,在保留数据特性的同时去除最高频噪音;采用SHAP对未加权传统RF从模型贡献角度进行特征度量,以此实现未加权传统RF加权,并使用RFECV与网格搜索对加权RF进行特征遴选、超参数优化;通过实际工程对模型的性能进行验证,基于SHAP理论对模型从全局与局部进行解释。结果表明:1)所建模型预测精度较高,其在测试集上的均方根误差(MSE)、平均绝对误差(MAE)与决定系数(R^(2))分别为0.0649(m/h)^(2)、0.1875 m/h、0.9254。2)在实际工程的验证中,模型取得了MSE=0.0503(m/h)^(2)、MAE=0.1613 m/h、R^(2)=0.9505的性能表现,精度理想,且性能均高于常用的深度神经网络、支持向量回归、未加权传统RF。3)经过VMD处理可有效提升PR的预测精度,处理后的模型在测试集上MSE、MAE、R^(2)分别提升了82.50%、59.00%、33.25%。4)岩石单轴抗压强度是精准预测PR时最重要的因素,地质参数在预测中的交互性明显优于掘进参数。预测分析重要洞段的PR时,需结合全局与局部2个角度进行综合分析。

地质雷达在城市雨污分流顶管施工中的应用

分析现行主要地质超前预报技术的优缺点和地质雷达探测原理。以某市雨污分流改造工程为背景,介绍RIS-K2型地质雷达测试仪对掌子面前方地质情况进行探测的方法步骤。分析复杂地质区顶管施工地质条件与相应雷达图像的关联关系,对复杂地质区情况进行预报,并针对具体地质情况为顶管安全施工提出相应解决办法。

基于变速GTAW焊接温度场解析模型的熔深预测研究

焊接热过程解析模型是一种能够直观反映焊接过程中工件内部的热行为、明确焊接工艺参数与熔池形貌间的物理关系、快速计算得到熔深的方法.目前常用的焊接温度场解析模型通常将电弧近似为一个功率恒定的匀速运动热源进行计算,而非参考实际焊接过程,即通过实时调整焊接热输入(焊接电流或焊接速度)来保证熔透.因此,目前的焊接温度场解析模型并不适用于焊接熔透控制.为了使解析模型能够计算时变速度下的熔深,本文针对钨极氩弧焊(GTAW)工艺建立了适用于计算时变焊接速度的焊接温度场解析模型.首先以匀速移动点热源的温度场解析模型为基础,推导出了时变焊接速度移动点热源在热源作用时间内引起的温度变化公式,通过连续性证明验证了公式的连续可积性并进一步推导出了适用于时变焊接速度运动单椭球热源的焊接温度场解析模型.为了提高计算速度,减少计算量,采用坐标变化方法实现了热源及其附近温度场的快速求解.基于数值积分法得到了解析模型求解方法,实现了熔池温度场的准确计算,并通过提取等温线实现了熔深和熔宽的预测.最后,通过工艺试验验证了解析模型的精度.结果表明:在0~4 s阶段,计算熔深基本与实际测得的熔深相同,误差控制在6%以内;在速度发生改变的4~16 s阶段,由于热源系数不再适用,计算误差增大,但熔深计算误差仍控制在17%以内,熔宽计算误差在12%以内.因此,建立的焊接温度场解析模型基本可以满足时变速度下的熔深预测.

大洋钻探过程钻速在线区间预测方法——以微型钻探船室内模拟实验为例

大洋钻探是从事海洋能源资源勘探开发和地壳构造演化研究的主要手段,常面临复杂海况扰动大、海底地层不确定性强等问题。本文提出一种大洋钻探过程钻速在线区间预测方法,以微型钻探船室内模拟实验为例开展方法的验证工作,为工程化应用奠定重要基础。首先,运用数据重采样、数据时深匹配、数据滤波等方法对多源大洋钻探数据进行预处理。其次,引入极限学习机、粒子群优化等方法建立钻速点预测模型。再者,利用非参数估计方法构造置信区间,建立钻速区间预测模型,并开展钻速区间预测。最后,通过滑动窗口方法在线更新区间预测模型参数,实现模型的在线学习和优化。微型钻探船室内模拟实验的对比结果验证了所提方法具有很强的钻速预测能力和鲁棒性,为大洋钻探过程钻速优化控制提供了新的工程解决方案。