Determination of the Pile Drivability Using Random Forest Optimized by Particle Swarm Optimization and Bayesian Optimizer

Driven piles are used in many geological environments as a practical and convenient structural component.Hence,the determination of the drivability of piles is actually of great importance in complex geotechnical applications.Conventional methods of predicting pile drivability often rely on simplified physicalmodels or empirical formulas,whichmay lack accuracy or applicability in complex geological conditions.Therefore,this study presents a practical machine learning approach,namely a Random Forest(RF)optimized by Bayesian Optimization(BO)and Particle Swarm Optimization(PSO),which not only enhances prediction accuracy but also better adapts to varying geological environments to predict the drivability parameters of piles(i.e.,maximumcompressive stress,maximum tensile stress,and blow per foot).In addition,support vector regression,extreme gradient boosting,k nearest neighbor,and decision tree are also used and applied for comparison purposes.In order to train and test these models,among the 4072 datasets collected with 17model inputs,3258 datasets were randomly selected for training,and the remaining 814 datasets were used for model testing.Lastly,the results of these models were compared and evaluated using two performance indices,i.e.,the root mean square error(RMSE)and the coefficient of determination(R2).The results indicate that the optimized RF model achieved lower RMSE than other prediction models in predicting the three parameters,specifically 0.044,0.438,and 0.146;and higher R^(2) values than other implemented techniques,specifically 0.966,0.884,and 0.977.In addition,the sensitivity and uncertainty of the optimized RF model were analyzed using Sobol sensitivity analysis and Monte Carlo(MC)simulation.It can be concluded that the optimized RF model could be used to predict the performance of the pile,and it may provide a useful reference for solving some problems under similar engineering conditions.

Data-driven production optimization using particle swarm algorithm based on the ensemble-learning proxy model

Production optimization is of significance for carbonate reservoirs,directly affecting the sustainability and profitability of reservoir development.Traditional physics-based numerical simulations suffer from insufficient calculation accuracy and excessive time consumption when performing production optimization.We establish an ensemble proxy-model-assisted optimization framework combining the Bayesian random forest(BRF)with the particle swarm optimization algorithm(PSO).The BRF method is implemented to construct a proxy model of the injectioneproduction system that can accurately predict the dynamic parameters of producers based on injection data and production measures.With the help of proxy model,PSO is applied to search the optimal injection pattern integrating Pareto front analysis.After experimental testing,the proxy model not only boasts higher prediction accuracy compared to deep learning,but it also requires 8 times less time for training.In addition,the injection mode adjusted by the PSO algorithm can effectively reduce the gaseoil ratio and increase the oil production by more than 10% for carbonate reservoirs.The proposed proxy-model-assisted optimization protocol brings new perspectives on the multi-objective optimization problems in the petroleum industry,which can provide more options for the project decision-makers to balance the oil production and the gaseoil ratio considering physical and operational constraints.

Multi-objective reservoir operation using particle swarm optimization with adaptive random inertia weights

Based on conventional particle swarm optimization(PSO),this paper presents an efficient and reliable heuristic approach using PSO with an adaptive random inertia weight(ARIW)strategy,referred to as the ARIW-PSO algorithm,to build a multi-objective optimization model for reservoir operation.Using the triangular probability density function,the inertia weight is randomly generated,and the probability density function is automatically adjusted to make the inertia weight generally greater in the initial stage of evolution,which is suitable for global searches.In the evolution process,the inertia weight gradually decreases,which is beneficial to local searches.The performance of the ARIWPSO algorithm was investigated with some classical test functions,and the results were compared with those of the genetic algorithm(GA),the conventional PSO,and other improved PSO methods.Then,the ARIW-PSO algorithm was applied to multi-objective optimal dispatch of the Panjiakou Reservoir and multi-objective flood control operation of a reservoir group on the Luanhe River in China,including the Panjiakou Reservoir,Daheiting Reservoir,and Taolinkou Reservoir.The validity of the multi-objective optimization model for multi-reservoir systems based on the ARIW-PSO algorithm was verified.

Optimal Control of Slurry Pressure during Shield Tunnelling Based on Random Forest and Particle Swarm Optimization

The control of slurry pressure aiming to be consistent with the external water and earth pressure during shield tunnelling has great significance for face stability,especially in urban areas or underwater where the surrounding environment is very sensitive to the fluctuation of slurry pressure.In this study,an optimal control method for slurry pressure during shield tunnelling is developed,which is composed of an identifier and a controller.The established identifier based on the random forest(RF)can describe the complex non-linear relationship between slurry pressure and its influencing factors.The proposed controller based on particle swarm optimization(PSO)can optimize the key factor to precisely control the slurry pressure at the normal state of advancement.A data set from Tsinghua Yuan Tunnel in China was used to train the RF model and several performance measures like R2,RMSE,etc.,were employed to evaluate.Then,the hybrid RF-PSO control method is adopted to optimize the control of slurry pressure.The good agreement between optimized slurry pressure and expected values demonstrates a high identifying and control precision.

Prediction of maximum upward displacement of shield tunnel linings during construction using particle swarm optimization-random forest algorithm

During construction,the shield linings of tunnels often face the problem of local or overall upward movement after leaving the shield tail in soft soil areas or during some large diameter shield projects.Differential floating will increase the initial stress on the segments and bolts which is harmful to the service performance of the tunnel.In this study we used a random forest(RF)algorithm combined particle swarm optimization(PSO)and 5-fold cross-validation(5-fold CV)to predict the maximum upward displacement of tunnel linings induced by shield tunnel excavation.The mechanism and factors causing upward movement of the tunnel lining are comprehensively summarized.Twelve input variables were selected according to results from analysis of influencing factors.The prediction performance of two models,PSO-RF and RF(default)were compared.The Gini value was obtained to represent the relative importance of the influencing factors to the upward displacement of linings.The PSO-RF model successfully predicted the maximum upward displacement of the tunnel linings with a low error(mean absolute error(MAE)=4.04 mm,root mean square error(RMSE)=5.67 mm)and high correlation(R^(2)=0.915).The thrust and depth of the tunnel were the most important factors in the prediction model influencing the upward displacement of the tunnel linings.

Structural Damage Identification System Suitable for Old Arch Bridge in Rural Regions: Random Forest Approach

A huge number of old arch bridges located in rural regions are at the peak of maintenance.The health monitoring technology of the long-span bridge is hardly applicable to the small-span bridge,owing to the absence of technical resources and sufficient funds in rural regions.There is an urgent need for an economical,fast,and accurate damage identification solution.The authors proposed a damage identification system of an old arch bridge implemented with amachine learning algorithm,which took the vehicle-induced response as the excitation.A damage index was defined based on wavelet packet theory,and a machine learning sample database collecting the denoised response was constructed.Through comparing three machine learning algorithms:Back-Propagation Neural Network(BPNN),Support Vector Machine(SVM),and Random Forest(R.F.),the R.F.damage identification model were found to have a better recognition ability.Finally,the Particle Swarm Optimization(PSO)algorithm was used to optimize the number of subtrees and split features of the R.F.model.The PSO optimized R.F.model was capable of the identification of different damage levels of old arch bridges with sensitive damage index.The proposed framework is practical and promising for the old bridge’s structural damage identification in rural regions.

基于人工智能方法的隧道塌方风险预测研究

为了对隧道塌方风险展开研究,整理246起隧道塌方事故案例,通过建立塌方风险评估指标体系,基于人工智能预测方法,分别采用随机森林算法、径向基函数神经网络、BP神经网络模型、粒子群算法优化BP神经网络模型,对塌方风险进行预测。结果表明,随机森林算法、径向基函数神经网络、BP神经网络模型、粒子群算法优化BP神经网络模型的塌方预测准确率分别为81.67%、83.33%、86.67%、93.33%,F_(1)值分别为0.645、0.642、0.5、0.833。粒子群算法优化BP神经网络模型预测准确率和F_(1)值均大幅提高,预测效果最好,大大减少了评估结果的主观性,为隧道塌方风险研究提供了新的研究思路。

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。

气动调节阀粘滞故障检测与参数辨识方法研究

为了研究气动调节阀的粘滞特性问题,以CHEN粘滞模型作为阀门粘滞特性问题的基础模型,提出一种阀门粘滞故障的检测与改进的参数辨识方法。通过搭建粘滞故障试验台,模拟实际情况中发生不同程度粘滞时的阀杆运动状态,揭示阀门粘滞发生的机理。使用随机森林算法对振荡源进行分类,实现对粘滞故障的检测。使用基于Hammerstein模型的粒子群优化算法求解粘滞参数最优解,提出粘滞双参数取值范围的确定方法,实现欠补偿、无补偿和过补偿3种状态下的粘滞参数辨识。结果表明:阀门粘滞程度与阀杆所受动静摩擦力有关;在不考虑外界因素的影响下,提出的粘滞检测方法对4种振荡源的分类准确率达到99.0268%;提出的改进的粘滞参数辨识方法对不同大小粘滞参数的辨识结果误差达到7%以内。研究成果为阀门粘滞故障的检测和参数辨识提供了理论方法,对粘滞模型的改进具有实际参考价值。

基于光谱分解和PSOBP组合模型的光谱重构研究

针对R矩阵光谱重构法面临的问题,提出了一种基于相机响应特性的光谱分解方法,对分解出的同色异谱黑的反演建立粒子群优化BP神经网络模型(PSOBP)以实现网络训练权重的优化,并利用全局训练样本和局部训练样本的二次光谱重构方式进行了仿真实验。结果表明,在D65光源下,利用所提出的方法, RGB相机观测下重构两种测试集均方误差平均值分别至少降低了1.71%和0.51%,色差最大值分别为3.5579和2.3776,满足人眼辨别颜色阈值要求;WorldView3观测下光谱重构精度均方误差在410~510、555~565、590~685、705~740 nm波段内不超过2%,适应度系数表示的可接受样本占比均为91.667%,色差最大值分别为1.6002和1.1177,其光谱重构精度以及色度精度较其他方法均有所提高,且6通道多光谱相机已能满足较高精度光谱重构的要求。

基于RF-PSO的塔吊事故可能发生阶段预测与分析

塔吊在高层建筑的施工现场较为常见,为了探索塔吊事故的影响因素,对塔吊事故可能发生阶段进行预测,建立了塔吊事故的人为因素分析分类系统(HFACS)模型框架,明确了塔吊事故的影响因素;利用卡方检验对事故致因进行特征选择及量化分析,提出了一种基于粒子群优化算法优化随机森林预测模型(RF-PSO)。结果表明:RF-PSO模型的预测是可靠的,训练后的RF-PSO模型可以预测塔吊事故可能发生的阶段,影响因素塔吊安装不符合设计要求、缺乏沟通和个人防护设备缺失发生的概率越大,塔吊事故分别在安拆阶段、吊装阶段和攀爬阶段发生事故的可能性越大。研究结果可为塔吊施工现场管理提供理论依据。

基于改进PSO算法的移动机器人最优路径规划

针对机器人全局移动路径上出现动态障碍物,影响其安全运动问题,提出了一种随机障碍物环境下的改进粒子群(PSO)最优路径规划方法。目的是保证机器人沿全局路径移动,并能躲避随机障碍物。通过Dijkstra算法规划全局路径,并利用改进的PSO算法进行全局路径优化,获得最短运动路径;进一步,利用动态窗口方法避开随机障碍物,并使机器人返回规划的全局路径,降低重新规划路径的计算成本;仿真对比分析和实验研究结果表明,所提出的路径规划方法能保障移动机器人避开随机障碍物并在规划的全局路径上安全运动。

改进融合指标的新型盲解卷积算法在轴承故障诊断中的应用

为解决现有盲解卷积算法易受随机脉冲影响的问题,综合时域特征和频域特征,提出一个新的故障敏感指标,即包络谱峭度-包络基尼系数融合指标(Envelope Spectral Kurtosis-envelope Gini Index,ESKEG)。该指标对周期性脉冲更敏感,不易受随机脉冲的影响。基于该指标,提出一个新的解卷积算法,即基于最大ESKEG的盲解卷积,并采用粒子群算法(Particle Swarm Optimization,PSO)求解滤波器系数。通过仿真振动信号和实验仿真信号进行验证,结果表明相比于其他盲解卷积算法,所提出的PSO-ESKEG算法在故障先验知识未知的情况下,能更有效避免受到随机脉冲信号的影响。

考虑源荷储协同的主动配电网多时间尺度优化调度

随着分布式能源、柔性负荷和储能装置等资源的不断扩增,配电网逐渐转变成主动参与电网运行的调节与控制的主动配电网。针对储能装置、柔性负荷的利用效率较低等问题,提出一种荷源储协同的优化调度方法。首先,建立一个多时间尺度的主动配电网调度模型,然后以配电网电能质量最佳和最小运行成本为目标函数,以储能约束、网络平衡约束以及柔性负荷约束等作为约束条件。最后通过IEEE-33系统构建主动配电网,建立日前-日内的多时间尺度调度策略,利用随机权重粒子群算法求解多目标优化问题。验证了所提方法的可行性。

微分光谱变换方法对土壤重金属含量反演精度的影响研究

随着我国工农业的日益发展,土壤中以镍(Ni)、铁(Fe)、铜(Cu)、铬(Cr)、铅(Pb)等为代表的重金属污染对人类生活产生了严重影响。高光谱遥感技术具有实时、无损、快速等优点,为高效准确地获取土壤重金属含量提供了科学手段。而在利用高光谱数据反演土壤重金属含量时,微分光谱变换方法的选择对遥感反演土壤重金属含量的精度有显著影响。为明确二者关系,基于研究区采集的60个土壤样品,测定其Ni、Fe、Cr、Cu、Pb等含量以及350~2500 nm波段范围的光谱反射率。在相关系数(CC)分析法的基础上通过改进离散粒子群算法(MDBPSO)优选遥感探测土壤重金属含量的特征波段。最终以优选出的特征波段作为自变量利用随机森林(RF)算法构建了Ni、Fe、Cr、Cu、Pb等重金属含量的估测模型。在对原始反射率数据进行高斯平滑的基础上,对比分析了一阶微分(R′)、对数倒数的一阶微分(1/lgR)′、倒数的一阶微分(1/R)′、指数的一阶微分(e^(R))′四种微分光谱变换方法对土壤重金属反演精度的影响。结果表明,在CC分析法的基础上,MDBPSO算法可以有效地降低光谱数据的冗余度,提高模型的运行效率。其中R′、(1/lgR)′、(1/R)′、(e^(R))′中对Ni、Fe、Cr、Cu、Pb敏感的特征波段个数分别至少减少了154、363、135、744和889个。(1/lgR)′、R′、R′、(1/R)′、R′光谱变换方法分别应用到Ni、Fe、Cr、Cu、Pb特征波段的组合运算中,得到的估测模型的精度优于其他微分变换方法;模型检验集的决定系数分别为0.913、0.906、0.872、0.912、0.876,均方根误差分别为0.743、0.095、2.588、1.541、1.453。本研究为利用遥感数据反演土壤重金属含量微分光谱变换方法的选择提供了科学的参考,为进一步实现土壤重金属含量的大面积高精度遥感监测提供新的思路。

Study on site selection planning of urban electric vehicle charging station

The large-scale development of electric vehicles(EVs)requires numerous charging stations to serve them,and the charging stations should be reasonably laid out and planned according to the charging demand of electric vehicles.Considering the costs of both operators and users,a site selection model for optimal layout planning of charging stations is constructed,and a queuing theory approach is used to determine the charging pile configuration to meet the charging demand in the planning area.To solve the difficulties of particle swarm global optimization search,the improved random drift particle swarm optimization(IRDPSO)and Voronoi diagram are used to jointly solve for the optimal layout of electric vehicles.The final arithmetic analysis verifies the feasibility and practicality of the model and algorithm,and the results show that the total social cost is minimized when the charging station is 9,the location of the charging station is close to the center of gravity and the layout is reasonable.

基于PSO-RF的FRP筋与混凝土间黏结强度预测模型

与钢筋相比,FRP筋具有抗疲劳、低磁性和耐腐蚀等优势,在不利工程环境中的应用越来越普遍。然而,现有FRP筋与混凝土间黏结性能预测模型的适用范围较小,预测精度较低。为此,本文收集了170组铰接梁试验数据,并在此基础上利用粒子群算法优化随机森林算法(PSO-RF)建立了FRP筋与混凝土间的黏结强度模型。将PSO-RF模型与RF模型和五个现有模型进行对比,各模型的预测结果通过决定系数R2和平均绝对值误差MAE等统计指标进行评价。PSO-RF模型的R2和MAE值分别为0.9396和1.0143,可以为FRP筋在混凝土中的应用提供有价值的参考。与现有模型相比,PSO-RF模型的R2和MAE值分别提高了141.9%和81.3%,预测性能提升明显。模型的参数重要性分析结果表明,黏结长度和混凝土抗压强度对模型的预测结果影响较大,重要性系数分别为22.35%和18.3%。

融合差分进化和Sine混沌的改进粒子群算法

将差分进化与Sine混沌相结合,提出一种改进的粒子群算法。利用Sine混沌映射对初始种群进行优化,提高了收敛速度;该算法通过引入非同步变化的学习因子的速度更新公式,引入随机惯性权重,使算法能够更好地兼顾全局搜索与局部优化;借鉴差分进化算法中的交叉操作,采用淘汰机制随机搜索策略,提高算法的全局搜索能力,提高算法收敛速度。为了验证融合差分进化和Sine混沌的改进粒子群算法(improved particle swarm optimization algorithm,IPSO)的性能,与基于压缩学习因子的粒子群算法(yield-based particle swarm optimization,YPSO)、自适应加权粒子群算法(self-adaptive particle swarm optimization,SPSO)等PSO相关算法以及蜘蛛蜂优化算法(spider wasp optimization,SWO)、能量谷算法(energy valley algorithm,EVA)等2023年最新算法相比较,验证融合差分进化和Sine混沌的改进粒子群算法(IPSO)的有效性。在不同维度下解决12个常用基准函数,对12个测试函数进行实验,并与其他的几种算法进行比较,实验结果表明,改进后的PSO算法收敛速度快,收敛精度高。