首页|基于MFO-BPNN的螺旋钻机钻速预测研究

基于MFO-BPNN的螺旋钻机钻速预测研究

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针对利用现有经验公式所建立的螺旋钻机钻速预测模型存在准确度不足的问题,提出了一种基于飞蛾扑火算法(MFO)的反向传播神经网络(BPNN)钻速预测模型.首先,对MFO算法的基本原理进行了研究,构建了MFO算法优化BPNN的具体流程;接着,采集了江苏无锡某施工现场钻探数据,并分析了钻速影响因素,运用小波阈值降噪、归一化和灰色关联度分析等系列方法对采集数据进行了预处理,得到了训练和测试集;然后,将MFO算法运用于神经网络的权值和阈值训练,以代替原有梯度下降法,建立了MFO-BPNN钻速预测模型;最后,对上述预测模型与BPNN模型、遗传算法优化反向传播神经网络(GA-BPNN)模型以及粒子群优化算法优化反向传播神经网络(PSO-BPNN)模型的预测结果和评价指标进行了详细的对比分析.研究结果表明:运用 MFO-BPNN建立的钻速预测模型,其可靠性达到了91.65%,其决定系数(R2)优于其他 3 种预测模型,3 项误差指标也是其中最低的,说明该模型的预测精度良好,适合于桩基础工程的实际应用,可为复杂因素影响下的钻速预测提供一种新思路.
Drilling speed prediction of spiral drilling rigs based on MFO-BPNN
Aiming at the issue of insufficient accuracy in the prediction model of drilling speed for spiral drilling rigs established by existing empirical formulas,a back propagation neural network(BPNN)drilling speed prediction model based on the moth-flame optimization(MFO)algorithm was proposed.Firstly,the basic principles of the MFO algorithm were studied,and the specific process for optimizing the BPNN using the MFO algorithm was constructed.Subsequently,drilling data obtained from a construction site in Wuxi,Jiangsu was collected and factors affecting drilling speed were analyzed.A series of methods such as wavelet threshold denoising,normalization,and grey correlation analysis were used to preprocess the collected data,resulting in a training and testing set.Then,the MFO algorithm was applied to train the weights and thresholds of the neural network,replacing the original gradient descent method,and an MFO-BPNN drilling speed prediction model was established.Finally,a detailed comparative analysis was conducted on the prediction results and evaluation indicators of the above prediction model,BPNN model,genetic algorithm optimization-back propagation neural network(GA-BPNN)model,andparticle swarm optimization-back propagation neural network(PSO-BPNN)model.The research results indicate that the reliability of the drilling speed prediction model established using the MFO-BPNN has reached 91.65%.Furthermore,its coefficients of determination(R2)is better than the other three prediction models.The three error indicators are also the lowest.This indicates that the prediction accuracy of the model is good,suitable for practical applications in pile foundation engineering,and provides a new idea for drilling speed prediction under the influence of complex factors.

spiral drilling rigsdrilling speed predictionmoth-flame optimization(MFO)algorithmback propagation neural network(BPNN)genetic algorithm optimization-back propagation neural network(GA-BPNN)particle swarm optimization-back propagation neural network(P

李嘉辉、王英、郑荣跃、叶军、赵京昊、陈立

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宁波大学 机械工程与力学学院,浙江 宁波 315211

宁波大学 土木工程与地理环境学院,浙江 宁波 315211

浙江工业职业技术学院 机电工程学院,浙江 绍兴 312000

浙江易通特种基础工程股份有限公司,浙江 宁波 315800

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螺旋钻机 钻速预测 飞蛾扑火算法 反向传播神经网络 遗传算法优化反向传播神经网络 粒子群优化算法优化反向传播神经网络 决定系数 桩基础工程

宁波市重大科技攻关暨揭榜挂帅项目宁波市北仑区关键核心技术攻关项目

2022Z0632022001

2024

10.3969/j.issn.1001-4551.2024.04.008

机电工程
浙江大学　浙江省机电集团有限公司

机电工程

CSTPCD北大核心
影响因子：0.785
ISSN：1001-4551
年,卷(期)：2024.41(4)
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