WO2025007732A1 - 一种盾构施工参数多目标优化方法及系统 - Google Patents

一种盾构施工参数多目标优化方法及系统 Download PDF

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Publication number
WO2025007732A1
WO2025007732A1 PCT/CN2024/099553 CN2024099553W WO2025007732A1 WO 2025007732 A1 WO2025007732 A1 WO 2025007732A1 CN 2024099553 W CN2024099553 W CN 2024099553W WO 2025007732 A1 WO2025007732 A1 WO 2025007732A1
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Prior art keywords
shield
shield construction
construction parameters
objective optimization
tunneling
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English (en)
French (fr)
Inventor
李利平
周宗青
高成路
宋锦铭
杨钧岩
成帅
李景龙
巴兴之
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Shandong University
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Shandong University
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    • E—FIXED CONSTRUCTIONS
    • E21—EARTH OR ROCK DRILLING; MINING
    • E21D—SHAFTS; TUNNELS; GALLERIES; LARGE UNDERGROUND CHAMBERS
    • E21D9/00—Tunnels or galleries, with or without linings; Methods or apparatus for making thereof; Layout of tunnels or galleries
    • E21D9/06—Making by using a driving shield, i.e. advanced by pushing means bearing against the already placed lining
    • E21D9/093—Control of the driving shield, e.g. of the hydraulic advancing cylinders
    • G—PHYSICS
    • G06—COMPUTING OR CALCULATING; COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F30/00—Computer-aided design [CAD]
    • G06F30/10—Geometric CAD
    • G06F30/13—Architectural design, e.g. computer-aided architectural design [CAAD] related to design of buildings, bridges, landscapes, production plants or roads
    • G—PHYSICS
    • G06—COMPUTING OR CALCULATING; COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F30/00—Computer-aided design [CAD]
    • G06F30/20—Design optimisation, verification or simulation
    • G06F30/27—Design optimisation, verification or simulation using machine learning, e.g. artificial intelligence, neural networks, support vector machines [SVM] or training a model
    • G—PHYSICS
    • G06—COMPUTING OR CALCULATING; COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F2111/00—Details relating to CAD techniques
    • G06F2111/06—Multi-objective optimisation, e.g. Pareto optimisation using simulated annealing [SA], ant colony algorithms or genetic algorithms [GA]

Definitions

  • the invention belongs to the technical field of multi-objective optimization of shield parameters, and in particular relates to a multi-objective optimization method and system for shield construction parameters.
  • the shield method is the preferred method for building urban underground tunnels. It is environmentally friendly, safe, and efficient. This method has been widely used in urban tunnel construction in China.
  • the shield method is to advance the shield machine along the design axis in the stratum, and support the surrounding rock with the shield shell and segments to prevent tunnel collapse.
  • the mud water pressure difference between the top and bottom of the cutterhead of the large-diameter shield tunnel is further increased, and its matching with the stratum water and soil pressure is reduced, thereby reducing the effective support for the excavation face.
  • the probability of the shield passing through the composite stratum also increases significantly, which puts higher requirements on the shield's adaptability configuration and corresponding construction technology.
  • the present invention provides a multi-objective optimization method and system for shield construction parameters, which adopts a combination of a random forest algorithm and a multi-objective optimization algorithm, and uses the excavation speed predicted by the random forest as the first fitness function of the multi-objective optimization algorithm, and uses the shield excavation energy consumption ratio as the second fitness function, to achieve multi-objective optimization of shield construction parameters, while ensuring construction safety, taking into account construction efficiency and reducing construction costs.
  • the present invention adopts the following technical solution:
  • a first aspect of the present invention provides a multi-objective optimization method for shield construction parameters.
  • a multi-objective optimization method for shield construction parameters comprising:
  • a multi-objective optimization algorithm is adopted to optimize the shield construction parameters.
  • the excavation speed is obtained based on the shield construction parameters through random forest prediction.
  • the shield excavation energy consumption ratio is calculated based on the excavation speed and shield construction parameters. The maximization of the excavation speed and the minimization of the shield excavation energy consumption ratio are used as the two fitness functions of the multi-objective optimization algorithm.
  • the Spearman correlation coefficient was used to identify several shield construction parameters with the highest correlation with the target tunneling speed, and the tunneling speed was predicted by random forest based on the identified shield construction parameters.
  • the identified shield construction parameters include: total thrust, cutter head rotation speed, cutter head torque, cutter head extrusion pressure, incision pressure, penetration, tunnel burial depth, cohesion and internal friction angle.
  • the shield tunneling energy consumption ratio is related to the tunneling speed and the total thrust, cutter head rotation speed, cutter head torque and shield machine cutter head radius in the shield construction parameters.
  • training steps of the random forest include:
  • the isolation forest is used to detect outliers, and the detected outliers are replaced with missing values.
  • the missing value data are then filled using the average method and normalized.
  • the multi-objective optimization algorithm adopts a non-dominated genetic algorithm
  • the non-dominated genetic algorithm adopts an arithmetic crossover operator when the population crosses.
  • the non-dominated genetic algorithm introduces a cumulative sorting fitness strategy during Pareto sorting.
  • a second aspect of the present invention provides a multi-objective optimization system for shield construction parameters.
  • a multi-objective optimization system for shield construction parameters comprising:
  • a data acquisition module is configured to: acquire initial shield construction parameters
  • the optimization module is configured as follows: adopting a multi-objective optimization algorithm to optimize the shield construction parameters, and in the process of optimizing the shield construction parameters, based on the shield construction parameters, obtaining the tunneling speed through random forest prediction, calculating the shield tunneling energy consumption ratio based on the tunneling speed and the shield construction parameters, and taking the maximization of the tunneling speed and the minimization of the shield tunneling energy consumption ratio as the two fitness functions of the multi-objective optimization algorithm.
  • a third aspect of the present invention provides a computer-readable storage medium.
  • a computer-readable storage medium having a computer program stored thereon, the program being executed by a processor
  • the steps in the multi-objective optimization method for shield construction parameters as described in the first aspect above are implemented at the same time.
  • a fourth aspect of the present invention provides a computer device.
  • a computer device comprises a memory, a processor and a computer program stored in the memory and executable on the processor, wherein when the processor executes the program, the steps in the multi-objective optimization method for shield construction parameters as described in the first aspect above are implemented.
  • the present invention has the following beneficial effects:
  • the present invention takes into account the multi-objective optimization algorithm of shield tunneling parameters. Because in the shield construction process, not only the construction efficiency but also the construction cost should be considered. There are many shield construction parameters.
  • the RF algorithm and NSGA-II are combined, and the tunneling speed based on RF prediction is used as the first fitness function of NSGA-II, and the shield tunneling energy consumption ratio is used as the second fitness function. Multi-objective optimization of shield construction parameters is achieved, and the optimal parameter control range is given while meeting multiple objectives. While ensuring construction safety, construction efficiency is considered and construction cost is reduced. Compared with single-objective optimization, it is more in line with the actual significance of the project.
  • the present invention improves the NSGA-II algorithm, uses an arithmetic crossover operator to replace the SBX crossover operator in NSGA-II, and improves the search performance of the algorithm; at the same time, introduces a cumulative sorting fitness strategy to improve the global search capability of the algorithm and the uniformity of the Pareto front, so as to finally obtain the optimal solution set under multiple objectives.
  • the optimal solution the optimization range of the excavation parameters is obtained to assist the shield construction, so that the shield can be excavated safely and efficiently.
  • the present invention uses the Spearman correlation coefficient to select parameters required for the model in the data, and improves the data quality and usability through data preprocessing.
  • FIG1 is a flow chart of a multi-objective optimization method for shield construction parameters shown in Embodiment 1 of the present invention
  • FIG2 is a diagram showing the prediction effect of the RF model on the tunneling speed according to the first embodiment of the present invention
  • FIG3 is a flow chart of an improved NSGA-II algorithm model according to the first embodiment of the present invention.
  • each box in the flowchart or block diagram can represent a module, a program segment, or a part of a code
  • the module, program segment, or a part of a code may include one or more executable instructions for implementing the logical functions specified in each embodiment.
  • the functions marked in the box can also occur in an order different from that marked in the accompanying drawings. For example, two boxes represented in succession can actually be executed substantially in parallel, or they can sometimes be executed in the opposite order, depending on the functions involved.
  • each box in the flowchart and/or block diagram, and the combination of boxes in the flowchart and/or block diagram can be implemented using a dedicated hardware-based system that performs a specified function or operation, or can be implemented using a combination of dedicated hardware and computer instructions.
  • This embodiment provides a multi-objective optimization method for shield construction parameters.
  • the present embodiment provides a multi-objective optimization method for shield construction parameters, comprising: obtaining initial shield construction parameters; optimizing the shield construction parameters using a multi-objective optimization algorithm, and in the process of optimizing the shield construction parameters, obtaining the tunneling speed through random forest prediction based on the shield construction parameters, calculating the shield tunneling energy consumption ratio based on the tunneling speed and the shield construction parameters, and using maximization of the tunneling speed and minimization of the shield tunneling energy consumption ratio as two fitness functions of the multi-objective optimization algorithm.
  • the initial shield construction parameters can be randomly generated or set manually.
  • the present embodiment provides a multi-objective optimization method for shield construction parameters, which selects the parameters required by the model from the data, and improves the data quality and availability through data preprocessing; uses random forest (RF) for prediction, and improves the prediction accuracy by adjusting its hyperparameters, which can be used as the fitness function of multi-objective optimization; uses RF-based predicted tunneling speed as the first fitness function of NSGA-II (Non-dominated Sorting Genetic Algorithms, NSGA); uses the shield tunneling energy consumption ratio as the second fitness function to obtain a multi-objective solution set; introduces the arithmetic crossover operator strategy and the cumulative sorting fitness strategy into the NSGA-II algorithm, so as to converge faster and maintain population diversity, and finally obtains the optimal solution set under multiple objectives based on the Pareto frontier, and gives a reasonable range of parameter control, thereby assisting shield construction.
  • RF random forest
  • This embodiment provides a multi-objective optimization method for shield construction parameters, which specifically includes the following steps:
  • Step 1 Obtain shield construction parameters and excavation speed, and perform data segmentation:
  • the sensor obtains each ring of data mainly in four parts, consisting of the empty push section, the rising section, the stable section, and the ring construction section. Most of the values of the empty push section and the ring construction section are 0, and their analysis is meaningless. 10 rings of rising section and stable section data are selected for analysis, that is, the shield construction parameters and excavation speed of the stable section and the ring construction section are selected.
  • the shield excavation process involves many parameters, including shield excavation parameters, tunnel geometry parameters, and geological parameters. Sensors acquire data and record it all the time; it is meaningless to select the data recorded when the shield is running and analyze the data recorded when it is stopped.
  • Step 2 Outlier processing: For the selected data, use the isolation forest method to detect outliers, replace the outliers detected by the isolation forest with missing values, and then use the average method to fill the missing value data.
  • Isolation Forest is an unsupervised learning algorithm based on decision trees.
  • the idea of the algorithm is to judge samples distributed outside the high-density group as outliers: extract a part of the samples and put them into the root node of a single tree, set the cut value to split the tree, until the tree can no longer be split or the tree grows to the set height, and judge the first batch of isolated samples as outliers; perform the above operation on each tree in the forest, and then collect the segmentation results of the entire forest, and calculate the outlier score of each sample according to the following formula:
  • h(x) is the depth of the sample in the tree
  • n is the number of samples
  • H is the harmonic constant
  • samples with high scores are judged as outliers.
  • Step 3 Data normalization calculation formula:
  • Step 4 Use the Spearman correlation coefficient to identify the correlation between the shield parameters in the data set and the target tunneling speed.
  • the Spearman correlation coefficient is calculated as follows:
  • Ri , Si are the levels of observation value i, are the actual values of variables x and y, and n is the total number of parameters.
  • Step 5 Through correlation analysis, the variables related to the tunneling speed target are obtained and used as the main parameters for rationally controlling shield construction.
  • the total thrust, cutter head speed, cutter head torque, cutter head extrusion pressure, incision pressure, penetration, tunnel burial depth, cohesion, internal friction angle, etc. are used as input parameters, and the tunneling speed output parameters are used to predict.
  • Step 6 Input the data processed in steps 1-3 into the RF regression model.
  • the RF algorithm is training samples, it is necessary to adjust the important hyperparameters of the model.
  • the number of decision trees, the maximum number of features, and the maximum depth the regression fitting performance of the model can be improved.
  • Adjust the settings of RF hyperparameters which directly affect the prediction accuracy of the model, including the number of decision trees n_estimators, the maximum number of features max_features, and the maximum depth max_depth; its prediction effect on the test set is shown in Figure 2.
  • the determination coefficient R 2 of the training set is 0.9057 and the test set R 2 is 0.8991 through its model quantitative indicators, which proves that the model has a high generalization ability.
  • a quantitative indicator determination coefficient R 2 is selected.
  • the calculation formula of the performance indicator is as follows:
  • Step 7 The shield tunneling specific energy is the energy consumed by the shield machine per unit volume. One part is the consumption of the cutter excavating the soil, and the other part is the consumption of the friction between the shield shell and the soil.
  • the formula is as follows:
  • F is the total thrust (kN)
  • v is the tunneling speed (mm/min)
  • n is the cutterhead speed (r/min)
  • T is the cutterhead torque (MN ⁇ m)
  • R is the cutterhead radius of the shield machine.
  • Step 8 After RF training, the relationship between shield tunneling parameters and tunneling speed is obtained, and the first objective function is established to determine the objective function maxF 2 of tunneling speed; then, the second objective function is established to determine the objective function minF 1 of shield tunneling specific energy.
  • RF is used to accurately predict the tunneling speed
  • the nonlinear relationship between the shield parameters and this target is used to construct the first fitness function of NSGA-II.
  • the empirical formula shield tunneling consumption ratio is used as the second fitness function.
  • the second fitness function In order to ensure that the generated plan can be reasonably implemented, it is necessary to set restrictions on each variable when generating the plan and form variable constraints. In order to prevent a large difference between the optimized shield machine parameters and the actual shield machine parameters, resulting in a large adjustment range and potential safety hazards.
  • Step 9 When optimizing construction parameters, the decision range of the population must be set and its variables constrained to ensure that the initial population has practical significance.
  • Step 10 Run the NSGA-II algorithm.
  • the arithmetic crossover operator is used instead of the SBX crossover operator in NSGA-II when the population crosses, so as to retain the excellent traits of the parent individuals, improve the quality of the individuals after the crossover, and improve the search performance of the algorithm; the cumulative sorting fitness strategy is introduced in the Pareto sorting to retain the fitness values of the excellent individuals without dilution, so as to converge faster and maintain the diversity of the population.
  • the optimal solution set of the Pareto frontier under multiple objectives is obtained.
  • the algorithm flow chart is shown in Figure 3. According to the complexity of the objective, the population size, tournament size, crossover probability, and evolutionary generation are set to obtain the Pareto frontier.
  • Step 11 The optimal solution is obtained using the NSGA-II algorithm. While meeting the two objectives, the shield construction parameters are controlled within the optimization range, making shield tunneling safer and more efficient.
  • This embodiment provides a multi-objective optimization system for shield construction parameters.
  • a multi-objective optimization system for shield construction parameters comprising:
  • a data acquisition module is configured to: acquire initial shield construction parameters
  • the optimization module is configured as follows: adopting a multi-objective optimization algorithm to optimize the shield construction parameters, and in the process of optimizing the shield construction parameters, based on the shield construction parameters, obtaining the tunneling speed through random forest prediction, calculating the shield tunneling energy consumption ratio based on the tunneling speed and the shield construction parameters, and taking the maximization of the tunneling speed and the minimization of the shield tunneling energy consumption ratio as the two fitness functions of the multi-objective optimization algorithm.
  • This embodiment provides a computer-readable storage medium on which a computer program is stored.
  • the program is executed by the processor, the steps in the multi-objective optimization method for shield construction parameters as described in the above embodiment 1 are implemented.
  • This embodiment provides a computer device, including a memory, a processor, and a computer program stored in the memory and executable on the processor.
  • the processor executes the program, the steps in the multi-objective optimization method for shield construction parameters as described in the first embodiment are implemented.

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Abstract

一种盾构施工参数多目标优化方法及系统，包括：获取初始盾构施工参数；采用多目标优化算法，对盾构施工参数进行优化，且在对盾构施工参数进行优化的过程中，基于盾构施工参数，通过随机森林预测得到掘进速度，基于掘进速度和盾构施工参数计算盾构掘进耗能比，并将掘进速度最大化和盾构掘进耗能比最小化作为多目标优化算法的两个适应度函数。该盾构施工参数多目标优化方法及系统在保证施工安全的同时考虑了施工效率及减少了施工成本。

Description

一种盾构施工参数多目标优化方法及系统
相关申请的交叉引用
本发明要求于2023年7月6日提交中国国家知识产权局、申请号为202310826020.3、发明名称为“一种盾构施工参数多目标优化方法及系统”的中国专利申请的优先权，其全部内容通过引用结合在本发明中并构成本发明的一部分，用于所有目的。
技术领域
本发明属于盾构参数多目标优化技术领域，尤其涉及一种盾构施工参数多目标优化方法及系统。
背景技术
本部分的陈述仅仅是提供了与本发明相关的背景技术信息，不必然构成在先技术。
盾构法是建设城市地下隧道的首选工法，它具有环保、安全、高效等特点，这种方法在中国城市隧道施工中得到了广泛的应用。盾构法是将盾构机械在地层中沿着设计轴线推进，通过盾构外壳和管片支撑四周围岩防止隧道坍塌。与常规的6米左右的隧道相比，大直径盾构隧道的刀盘顶部与底部的泥水压力差进一步增大，其与地层水土压力的匹配性降低，从而降低了对开挖面的有效支护。同时，随着盾构直径的增大，盾构穿越复合地层的概率也大幅提高，从而对盾构适应性配置和相应地施工技术提出更高的要求。
盾构的安全、高效掘进是当前地下空间开发的迫切需求，为了能够使盾构掘进过程中实时维持掌子面的稳定，在泥浆舱内提供足够的支护压力，但是不同的掘进参数组合将产生不同的掘进效果，其中值得关注的一个问题就是盾构机的掘进耗能，它决定了盾构施工的成本消耗。此外，盾构机掘进速度是盾构 安全、高效掘进重要参数，明确指导工程进度规划、成本控制。过快的掘进速度，盾构姿态越不易控制，地表起伏偏大，而过慢的掘进速度，则会影响施工效率。
当前研究发现，盾构施工参数的优化目标较为单一，而在实际工程项目中，无法在保证施工安全的同时考虑施工效率及减少施工成本。
发明内容
为了解决上述背景技术中存在的技术问题，本发明提供一种盾构施工参数多目标优化方法及系统，采用随机森林算法和多目标优化算法相结合，将基于随机森林预测的掘进速度作为多目标优化算法第一个适应度函数，将盾构掘进耗能比作为第二个适应度函数，对盾构施工参数实现多目标优化，在保证施工安全的同时考虑了施工效率及减少施工成本。
为了实现上述目的，本发明采用如下技术方案：
本发明的第一个方面提供一种盾构施工参数多目标优化方法。
一种盾构施工参数多目标优化方法，包括：
获取初始盾构施工参数；
采用多目标优化算法，对盾构施工参数进行优化，且在对盾构施工参数进行优化的过程中，基于盾构施工参数，通过随机森林预测得到掘进速度，基于掘进速度和盾构施工参数计算盾构掘进耗能比，并将掘进速度最大化和盾构掘进耗能比最小化作为多目标优化算法的两个适应度函数。
进一步地，通过斯皮尔曼相关系数识别与目标掘进速度的相关性最高的若干个盾构施工参数，基于识别出的盾构施工参数，通过随机森林预测得到掘进速度。
进一步地，所述识别出的盾构施工参数包括：总推力、刀盘转速、刀盘扭矩、刀盘挤压力、切口压力、贯入度、隧道埋深、粘聚力和内摩擦角。
进一步地，所述盾构掘进耗能比与所述掘进速度以及盾构施工参数中的总推力、刀盘转速、刀盘扭矩和盾构机刀盘半径相关。
进一步地，所述随机森林的训练步骤包括：
获取盾构施工参数及掘进速度，并分割为空推段、上升段、稳定段和建环段后，选取稳定段和建环段的盾构施工参数及掘进速度；
对于选取的数据，利用孤立森林进行异常值的检测，将检测出来的异常值替换为缺失值，再使用平均值法进行缺失值数据填充后，进行归一化处理；
利用归一化处理后的数据，训练随机森林。
进一步地，所述多目标优化算法采用非支配遗传算法，且所述非支配遗传算法在种群交叉时采用算数交叉算子。
进一步地，所述非支配遗传算法在帕累托排序时引入累积排序适应度策略。
本发明的第二个方面提供一种盾构施工参数多目标优化系统。
一种盾构施工参数多目标优化系统，包括：
数据获取模块，其被配置为：获取初始盾构施工参数；
优化模块，其被配置为：采用多目标优化算法，对盾构施工参数进行优化，且在对盾构施工参数进行优化的过程中，基于盾构施工参数，通过随机森林预测得到掘进速度，基于掘进速度和盾构施工参数计算盾构掘进耗能比，并将掘进速度最大化和盾构掘进耗能比最小化作为多目标优化算法的两个适应度函数。
本发明的第三个方面提供一种计算机可读存储介质。
一种计算机可读存储介质，其上存储有计算机程序，该程序被处理器执行 时实现如上述第一个方面所述的盾构施工参数多目标优化方法中的步骤。
本发明的第四个方面提供一种计算机设备。
一种计算机设备，包括存储器、处理器及存储在存储器上并可在处理器上运行的计算机程序，所述处理器执行所述程序时实现如上述第一个方面所述的盾构施工参数多目标优化方法中的步骤。
与现有技术相比，本发明的有益效果是：
本发明考虑了盾构掘进参数的多目标优化算法，因为盾构施工过程中，不仅要考虑施工效率还要考虑施工成本，盾构的施工参数众多，采用RF算法和NSGA-II相结合，将基于RF预测的掘进速度作为NSGA-II第一个适应度函数，将盾构掘进耗能比作为第二个适应度函数，对盾构施工参数实现多目标优化，在满足多个目标的同时给出最佳参数控制范围，在保证施工安全的同时考虑了施工效率及减少施工成本，相较于单一的目标优化更加符合工程实际意义。
本发明改进了NSGA-II算法，使用算术交叉算子代替NSGA-II中的SBX交叉算子，提高该算法的搜索性能；同时，引入累积排序适应度策略，提高算法的全局搜索能力和帕累托前沿的均匀性，从而最后得到多目标下的最优解集。根据最优解得到掘进参数的优化范围，辅助盾构施工，从而使盾构安全高效掘进。
本发明使用斯皮尔曼相关系数在数据中选取模型所需参数，通过数据预处理提高数据质量和可用性。
附图说明
构成本发明的一部分的说明书附图用来提供对本发明的进一步理解，本发明的示意性实施例及其说明用于解释本发明，并不构成对本发明的不当限定。
图1是本发明实施例一示出的一种盾构施工参数多目标优化方法的流程图；
图2是本发明实施例一示出的RF模型对掘进速度的预测效果图；
图3是本发明实施例一示出的改进NSGA-II算法模型流程图。
具体实施方式
下面结合附图与实施例对本发明作进一步说明。
应该指出，以下详细说明都是例示性的，旨在对本发明提供进一步的说明。除非另有指明，本文使用的所有技术和科学术语具有与本发明所属技术领域的普通技术人员通常理解的相同含义。
需要注意的是，这里所使用的术语仅是为了描述具体实施方式，而非意图限制根据本发明的示例性实施方式。如在这里所使用的，除非上下文另外明确指出，否则单数形式也意图包括复数形式，此外，还应当理解的是，当在本说明书中使用术语“包含”和/或“包括”时，其指明存在特征、步骤、操作、器件、组件和/或它们的组合。
需要注意的是，附图中的流程图和框图示出了根据本公开的各种实施例的方法和系统的可能实现的体系架构、功能和操作。应当注意，流程图或框图中的每个方框可以代表一个模块、程序段、或代码的一部分，所述模块、程序段、或代码的一部分可以包括一个或多个用于实现各个实施例中所规定的逻辑功能的可执行指令。也应当注意，在有些作为备选的实现中，方框中所标注的功能也可以按照不同于附图中所标注的顺序发生。例如，两个接连地表示的方框实际上可以基本并行地执行，或者它们有时也可以按照相反的顺序执行，这取决于所涉及的功能。同样应当注意的是，流程图和/或框图中的每个方框、以及流程图和/或框图中的方框的组合，可以使用执行规定的功能或操作的专用的基于硬件的系统来实现，或者可以使用专用硬件与计算机指令的组合来实现。
实施例一
本实施例提供了一种盾构施工参数多目标优化方法。
本实施例提供的一种盾构施工参数多目标优化方法，包括：获取初始盾构施工参数；采用多目标优化算法，对盾构施工参数进行优化，且在对盾构施工参数进行优化的过程中，基于盾构施工参数，通过随机森林预测得到掘进速度，基于掘进速度和盾构施工参数计算盾构掘进耗能比，并将掘进速度最大化和盾构掘进耗能比最小化作为多目标优化算法的两个适应度函数。
其中，初始盾构施工参数可以随机生成，也可以人为设定。
本实施例提供的一种盾构施工参数多目标优化方法，在数据中选取模型所需参数，通过数据预处理提高数据质量和可用性；采用随机森林(RF)进行预测，通过调整其超参数，提高预测精度可以作为多目标优化的适应度函数；基于RF预测掘进速度作为NSGA-II(非支配遗传算法，Non-dominated Sorting Genetic Algorithms，NSGA)第一个适应度函数；将盾构掘进耗能比作为第二个适应度函数，从而获取多目标的解集；将NSGA-II算法引入算术交叉算子策略和累积排序适应度策略，从而更快收敛和保持种群多样性，最后基于pareto前沿得到多目标下的最优解集，给出参数控制的合理范围，从而辅助盾构施工。
本实施例提供的一种盾构施工参数多目标优化方法，具体包括以下步骤：
步骤1：获取盾构施工参数及掘进速度，并进行数据分割：传感器获取每环数据主要是四部分，由空推段、上升段、稳定段、建环段四部分组成，空推段与建环段大部分数值为0，其分析无意义，选取10环上升段与稳定段数据进行分析，即选取稳定段和建环段的盾构施工参数及掘进速度。
其中，盾构挖掘过程中涉及较多参数，有盾构掘进参数、隧道几何参数、地质参数。传感器获取数据每时每刻都在记录；选取盾构运行时所记录数据，停机时所记录数据分析是无意义的。
步骤2：异常值处理：对于选取的数据，利用孤立森林进行异常值的检测，将孤立森林检测出来的异常点替换为缺失值，再使用平均值法进行缺失值数据填充。
孤立森林是基于决策树的无监督学习算法，算法的思想是将分布在高密度群体之外的样本判为异常点：抽取一部分样本放入单颗树的根结点，设置切割值进行裂树，直到树已无法分割或树长到了所设定的高度，将最先被孤立的一批样本判为异常样本；对森林的每棵树都执行上述操作，再将整个森林的切分结果进行集合，根据如下公式计算每个样本的异常得分:
其中，h(x)为样本在树的深度，n为样本个数，H为谐波常数，将得分高的样本判为异常值。
由于突然停机或传感器监测数据异常会获取到一些异常值，将其剔除以免影响后续模型预测。
步骤3：数据的归一化计算公式：
对数据进行归一化，消除数据之间的量纲，能更好的放入到模型中进行预测。将处理好的数据中的70％作为训练集，30％作为测试集。
步骤4：使用斯皮尔曼相关系数来识别所述数据集中的盾构参数与目标掘进速度的相关性，斯皮尔曼相关系数的计算如下：
式中，Ri,Si是观测值i取值的等级，是变量x,y的实际值，n是参数总数。
步骤5：通过相关性分析得到与掘进速度目标相关变量将其作为合理控制盾构施工的主要参数，总推力、刀盘转速、刀盘扭矩、刀盘挤压力、切口压力、贯入度、隧道埋深、粘聚力、内摩擦角等作为输入参数，将掘进速度输出参数来预测。
步骤6：将步骤1-3处理好的数据输入到RF回归模型中，RF算法训练样本时，需要调整模型重要超参数。通过调整决策树的数量、最大特征数及最大深度，从而提高模型的回归拟合性能。
调整RF超参数的设置，其设置直接影响模型的预测精度，包括决策树数量n_estimators、最大特征数量max_features、最大深度max_depth；其在测试集上的预测效果如图2所示。通过其模型量化指标得出训练集的决定系数R2为0.9057，测试集R2为0.8991，证明该模型具有很高的泛化能力。
为了验证RF模型的预测性能，选择一个量化指标决定系数R2。性能指标的计算公式如下：
式中，yi是目标值的实际值，是目标值的预测值，是目标值的实际平均值。
步骤7：盾构掘进比能是盾构机掘进单位体积所消耗的能量，一部分是刀具开挖土体的消耗WT，另一部分是盾壳与土体之间摩擦力的消耗，其公式如下：
其中F为总推力(kN),v为掘进速度(mm/min),n为刀盘转速(r/min)，T为刀盘扭矩(MN·m)，R为盾构机刀盘半径。
从RF-NSGA-II优化的角度来看，特定工程项目的土壤参数和几何参数是固定的。为了实现盾构施工中掘进比能最小和掘进速度最大的目标，应优化施工参数。
步骤8：通过RF训练后得到盾构掘进参数和掘进速度的关系，并建立第一个目标函数，决定掘进速度的目标函数maxF2；然后，建立第二个目标函数，决定盾构掘进比能的目标函数minF1。
具体地，用RF对掘进速度进行精准预测，将盾构参数与该目标之间的非线性关系构造NSGA-II的第一个适应度函数。
具体地，将经验公式盾构掘进耗比能作为第二个适应度函数，为了确保生成方案能够合理实施，需要在生成方案时设置每个变量的进行限制，并形成变量的约束条件。为了防止优化的盾构机参数与实际盾构参数之间存在较大差异，从而导致较大的调整范围和潜在的安全隐患。
步骤9：当施工参数优化时，必须设定种群的决策范围，对其变量进行约束，以确保初始种群具有实际意义。
步骤10：运行NSGA-II算法。
在其他领域研究中多目标优化中，有用到各种算法如MODE、MOGA等，相对较多的是NSGA-II算法，其可生成最优解，帮助决策者进行决策，然而其采用的交叉算子搜索性能较弱，该算法在种群多样性和收敛速度上也有一定的局限。
本实施例中，为使NSGA-II算法获得解更加均匀，在种群交叉时采用算数交叉算子代替NSGA-II中的SBX交叉算子，保留父代个体优良性状，提高交叉后个体的质量，提高该算法的搜索性能；在帕累托排序时引入累积排序适应度策略，保留优秀个体的适应度值不被稀释，从而更快收敛和保持种群多样性， 最后得到多目标下的帕累托前沿最优解集。算法流程图如图3所示，根据该目标的复杂程度设置种群规模、锦标赛规模、交叉概率、进化代数获取帕累托前沿。
其中，算数交叉算子是将两个父代个体的对应基因进行加权平均得到的子代个体对应基因。具体地，两个父代个体P1和P2，假设它们的某个基因分别为g(1)和g(2)，则子代个体的对应基因为g(c)＝λg(1)+(1-λ)g(2)，式中λ为随机生成权重因子，通常在[0,1]之间取值。其优势是保留父代个体信息，同时引入新的变异信息，增加遗传算法的搜索空间。
步骤11：以NSGA-II算法获得最优解，在满足两个目标的同时，将盾构施工参数控制在优化范围内，从而使盾构掘进更加安全、高效。
实施例二
本实施例提供了一种盾构施工参数多目标优化系统。
一种盾构施工参数多目标优化系统，包括：
数据获取模块，其被配置为：获取初始盾构施工参数；
优化模块，其被配置为：采用多目标优化算法，对盾构施工参数进行优化，且在对盾构施工参数进行优化的过程中，基于盾构施工参数，通过随机森林预测得到掘进速度，基于掘进速度和盾构施工参数计算盾构掘进耗能比，并将掘进速度最大化和盾构掘进耗能比最小化作为多目标优化算法的两个适应度函数。
此处需要说明的是，上述模块与实施例一中的步骤所实现的示例和应用场景相同，但不限于上述实施例一所公开的内容。需要说明的是，上述模块作为系统的一部分可以在诸如一组计算机可执行指令的计算机系统中执行。
实施例三
本实施例提供了一种计算机可读存储介质，其上存储有计算机程序，该程 序被处理器执行时实现如上述实施例一所述的盾构施工参数多目标优化方法中的步骤。
实施例四
本实施例提供了一种计算机设备，包括存储器、处理器及存储在存储器上并可在处理器上运行的计算机程序，所述处理器执行所述程序时实现如上述实施例一所述的盾构施工参数多目标优化方法中的步骤。
以上所述仅为本发明的优选实施例而已，并不用于限制本发明，对于本领域的技术人员来说，本发明可以有各种更改和变化。凡在本发明的精神和原则之内，所作的任何修改、等同替换、改进等，均应包含在本发明的保护范围之内。

Claims (10)

  1. 一种盾构施工参数多目标优化方法，其特征在于，包括：
    获取初始盾构施工参数；
    采用多目标优化算法，对盾构施工参数进行优化，且在对盾构施工参数进行优化的过程中，基于盾构施工参数，通过随机森林预测得到掘进速度，基于掘进速度和盾构施工参数计算盾构掘进耗能比，并将掘进速度最大化和盾构掘进耗能比最小化作为多目标优化算法的两个适应度函数。
  2. 根据权利要求1所述的盾构施工参数多目标优化方法，其特征在于，通过斯皮尔曼相关系数识别与目标掘进速度的相关性最高的若干个盾构施工参数，基于识别出的盾构施工参数，通过随机森林预测得到掘进速度。
  3. 根据权利要求2所述的盾构施工参数多目标优化方法，其特征在于，所述识别出的盾构施工参数包括：总推力、刀盘转速、刀盘扭矩、刀盘挤压力、切口压力、贯入度、隧道埋深、粘聚力和内摩擦角。
  4. 根据权利要求1所述的盾构施工参数多目标优化方法，其特征在于，所述盾构掘进耗能比与所述掘进速度以及盾构施工参数中的总推力、刀盘转速、刀盘扭矩和盾构机刀盘半径相关。
  5. 根据权利要求1所述的盾构施工参数多目标优化方法，其特征在于，所述随机森林的训练步骤包括：
    获取盾构施工参数及掘进速度，并分割为空推段、上升段、稳定段和建环段后，选取稳定段和建环段的盾构施工参数及掘进速度；
    对于选取的数据，利用孤立森林进行异常值的检测，将检测出来的异常值替换为缺失值，再使用平均值法进行缺失值数据填充后，进行归一化处理；
    利用归一化处理后的数据，训练随机森林。
  6. 根据权利要求1所述的盾构施工参数多目标优化方法，其特征在于，所述多目标优化算法采用非支配遗传算法，且所述非支配遗传算法在种群交叉时采用算数交叉算子。
  7. 根据权利要求6所述的盾构施工参数多目标优化方法，其特征在于，所述非支配遗传算法在帕累托排序时引入累积排序适应度策略。
  8. 一种盾构施工参数多目标优化系统，其特征在于，包括：
    数据获取模块，其被配置为：获取初始盾构施工参数；
    优化模块，其被配置为：采用多目标优化算法，对盾构施工参数进行优化，且在对盾构施工参数进行优化的过程中，基于盾构施工参数，通过随机森林预测得到掘进速度，基于掘进速度和盾构施工参数计算盾构掘进耗能比，并将掘进速度最大化和盾构掘进耗能比最小化作为多目标优化算法的两个适应度函数。
  9. 一种计算机可读存储介质，其上存储有计算机程序，其特征在于，该程序被处理器执行时实现如权利要求1-7中任一项所述的盾构施工参数多目标优化方法中的步骤。
  10. 一种计算机设备，包括存储器、处理器及存储在存储器上并可在处理器上运行的计算机程序，其特征在于，所述处理器执行所述程序时实现如权利要求1-7中任一项所述的盾构施工参数多目标优化方法中的步骤。
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