CN110119568B - A method for evaluating the influencing factors of riprap effect for riprap bank protection - Google Patents
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Abstract
本发明公开了抛石护岸工程技术领域的一种抛石护岸抛石效果影响因子评价方法，旨在解决现有技术中评价抛石效果仅限于定性分析，而缺乏定量分析方法以准确测定各影响因素对河势变化贡献率的技术问题。所述方法包括如下步骤：获取目标河段评价数据，所述目标河段评价数据包括抛石效果影响因子实测值和抛石效果评价指标实测值；根据目标河段评价数据建立增强回归树模型；根据增强回归树模型评价抛石效果影响因子对抛石效果的贡献率。
The invention discloses a method for evaluating the influencing factors of the riprap effect of riprap and bank protection engineering in the technical field of riprap and bank protection, aiming at solving the problem that the evaluation of riprap effects in the prior art is limited to qualitative analysis, and there is a lack of quantitative analysis methods to accurately measure each impact. The technical problem of factors contributing to the change of river regime. The method includes the following steps: obtaining evaluation data of a target river section, the target river section evaluation data including the measured value of the impact factor of the riprap effect and the measured value of the riprap effect evaluation index; establishing an enhanced regression tree model according to the evaluation data of the target river section; According to the enhanced regression tree model, the contribution rate of the influencing factors of the riprap effect to the riprap effect was evaluated.
Description
技术领域technical field
本发明涉及一种抛石护岸抛石效果影响因子评价方法，属于抛石护岸工程技术领域。The invention relates to a method for evaluating influence factors of riprap effects for riprap bank protection, and belongs to the technical field of riprap bank protection engineering.
背景技术Background technique
抛石护岸工程为水下隐蔽工程，鉴于水下复杂地形，为确保抛石抛石效果，需首先确定施工河段内不同区域的抛石厚度。此外，抛石抛石效果还受其他因素影响，包括：抛石体受水流泥沙冲击在水下发生剧烈运移；抛石结束后抛石体作为河床一部分，由于长期浸泡、冲刷而发生损毁和老化，导致抛石效果与工程预期不符，严重的甚至发生崩岸溃堤。因此，抛石施工后评价抛石效果，以及研究抛石体水下运移的影响因素，对进一步改进施工方式、提高抛石利用率及维护岸坡安全有重要意义。The riprap bank protection project is an underwater concealed project. In view of the complex underwater terrain, in order to ensure the effect of riprap and riprap, it is necessary to first determine the thickness of riprap in different areas of the construction river. In addition, the riprap effect is also affected by other factors, including: the riprap body is violently migrated underwater due to the impact of water flow and sediment; after the riprap is completed, the riprap body, as part of the river bed, is damaged due to long-term immersion and erosion and aging, resulting in the effect of riprap that is not in line with the project expectations, and even the collapse of the embankment in serious cases. Therefore, it is of great significance to further improve the construction method, improve the utilization rate of the riprap and maintain the safety of the bank slope by evaluating the riprap effect after the riprap construction and studying the influencing factors of the underwater migration of the riprap.
现有技术中对于抛石效果的评价尚处在定性分析阶段，缺乏定量分析方法以准确测定各影响因素对河势变化的贡献率。The evaluation of the riprap effect in the prior art is still in the stage of qualitative analysis, and there is no quantitative analysis method to accurately determine the contribution rate of each influencing factor to the change of the river regime.
发明内容SUMMARY OF THE INVENTION
本发明的目的在于克服现有技术中的不足，提供了一种抛石护岸抛石效果影响因子评价方法，包括如下步骤：获取目标河段评价数据，所述目标河段评价数据包括抛石效果影响因子实测值和抛石效果评价指标实测值；根据目标河段评价数据建立增强回归树模型；根据增强回归树模型评价抛石效果影响因子对抛石效果的贡献率。The purpose of the present invention is to overcome the deficiencies in the prior art, and provides a method for evaluating the impact factor of the riprap effect of riprap bank protection, comprising the following steps: obtaining evaluation data of a target river section, and the target river section evaluation data includes the riprap effect The measured value of the impact factor and the actual measured value of the riprap effect evaluation index; the enhanced regression tree model is established according to the evaluation data of the target river reach; the contribution rate of the impact factor of the riprap effect to the riprap effect is evaluated according to the enhanced regression tree model.
进一步地，抛石效果影响因子，包括：坡度、坡向、流速、流向和底床高程中至少任一项；抛石效果评价指标，包括：底床高程增量。Further, the impact factor of the riprap effect includes at least any one of slope, slope aspect, flow velocity, flow direction and bed elevation; and the riprap effect evaluation index includes: the bed elevation increment.
进一步地，获取目标河段评价数据，包括如下步骤：抛石施工后监测获取目标河段评价数据；抛石体水下运移稳定后监测获取目标河段评价数据。Further, obtaining the evaluation data of the target river reach includes the following steps: monitoring and obtaining the evaluation data of the target river reach after the riprap construction; monitoring and obtaining the evaluation data of the target river reach after the underwater migration of the riprap body is stable.
进一步地，根据目标河段评价数据建立增强回归树模型，包括如下步骤：建立增强回归树过程模型，所述增强回归树过程模型包括损失函数；从目标河段评价数据中提取训练样本；将训练样本输入增强回归树过程模型训练以降低损失函数；基于降低后的损失函数建立新的增强回归树过程模型；按预设的学习速率和分型次数重复增强回归树过程模型建立过程，输出最后建立的增强回归树过程模型作为增强回归树模型。Further, establishing an enhanced regression tree model according to the target river reach evaluation data includes the following steps: establishing an enhanced regression tree process model, where the enhanced regression tree process model includes a loss function; extracting training samples from the target river reach evaluation data; The sample input enhanced regression tree process model is trained to reduce the loss function; a new enhanced regression tree process model is established based on the reduced loss function; the enhanced regression tree process model building process is repeated according to the preset learning rate and typing times, and the output is finally established The boosted regression tree process model is used as the boosted regression tree model.
进一步地，训练样本占目标河段评价数据90％，预设学习速率为0.1，预设分型次数为300。Further, the training samples account for 90% of the target river reach evaluation data, the preset learning rate is 0.1, and the preset number of classifications is 300.
进一步地，根据增强回归树模型评价抛石效果影响因子对抛石效果的贡献率，包括如下步骤：校核增强回归树模型是否具有可靠性；如果增强回归树模型具有可靠性，将抛石效果影响因子实测值输入增强回归树模型，获取抛石效果影响因子对于抛石效果评价指标的贡献率；如果增强回归树模型不具可靠性，按调整后的学习速率和分型次数重新建立增强回归树模型。Further, according to the enhanced regression tree model, evaluating the contribution rate of the impact factor of the rock dumping effect to the rock dumping effect includes the following steps: checking whether the enhanced regression tree model is reliable; if the enhanced regression tree model is reliable, the rock dumping effect Input the measured value of the impact factor into the enhanced regression tree model to obtain the contribution rate of the impact factor of the rock dumping effect to the evaluation index of the rock dumping effect; if the enhanced regression tree model is not reliable, rebuild the enhanced regression tree according to the adjusted learning rate and the number of typing times Model.
进一步地，校核增强回归树模型是否具有可靠性，包括如下步骤：从目标河段评价数据中提取校核样本，所述校核样本为目标河段评价数据经提取训练样本后的剩余数据；将校核样本输入增强回归树模型，输出抛石效果评价指标模拟值；根据抛石效果评价指标模拟值和抛石效果评价指标实测值判定增强回归树模型是否具有可靠性。Further, checking whether the enhanced regression tree model has reliability includes the following steps: extracting a check sample from the evaluation data of the target river section, and the check sample is the remaining data after the training sample is extracted from the evaluation data of the target river section; Input the check sample into the enhanced regression tree model, and output the simulated value of the riprap effect evaluation index; determine whether the enhanced regression tree model is reliable according to the simulated value of the riprap effect evaluation index and the measured value of the rock riprap effect evaluation index.
进一步地，增强回归树模型具有可靠性的判定标准，包括：抛石效果评价指标模拟值的均方根误差≤0.5，且；抛石效果评价指标模拟值的标准化平均误差＜50％，且；抛石效果评价指标模拟值的拟合优度＞0.8。Further, the enhanced regression tree model has reliability judgment criteria, including: the root mean square error of the simulated value of the evaluation index of the rock dumping effect is less than or equal to 0.5, and; the standardized average error of the simulated value of the evaluation index of the rock dumping effect is less than 50%, and; The goodness of fit of the simulated value of the evaluation index of the riprap effect is greater than 0.8.
与现有技术相比，本发明所达到的有益效果：能够获取影响抛石体水下运移结果影响因素间的相对贡献率，为数值模拟提供指导；运用模型训练抛石运移结果的精确度较高，以此模型为依据，在抛石施工前对采集的地形数据进行预测，对淤积严重处减少抛石量，同时在冲刷严重地区域加大抛石，为提高抛石效率提供了科学依据。Compared with the prior art, the present invention has the beneficial effects of obtaining the relative contribution rate among the influencing factors affecting the underwater migration result of the rock riprap, providing guidance for numerical simulation; using the model to train the accurate rock riprap migration result. Based on this model, the collected terrain data is predicted before the construction of rock dumping, the amount of rock dumping is reduced in the areas with severe siltation, and the amount of rock dumping is increased in the areas with severe erosion, which provides a new method for improving the efficiency of rock dumping. scientific basis.
附图说明Description of drawings
图1是本发明流程图；Fig. 1 is the flow chart of the present invention;
图2是本发明实施实例中预测值与真实值数据；Fig. 2 is predicted value and actual value data in the embodiment of the present invention;
图3是本发明实施实例中计算获取的影响因子贡献率。FIG. 3 is an impact factor contribution rate obtained by calculation in an embodiment of the present invention.
具体实施方式Detailed ways
下面结合附图对本发明作进一步描述。The present invention will be further described below in conjunction with the accompanying drawings.
如图1所示，是本发明流程图，所述方法包括如下步骤：As shown in Figure 1, it is a flow chart of the present invention, and the method includes the following steps:
步骤一，采集目标河段河床地形数据，根据不同流速监测位点分别提取抛石效果影响因子和抛石效果评价指标的实测数据；Step 1: Collect the topographic data of the riverbed of the target river reach, and extract the measured data of the impact factor of the riprap effect and the evaluation index of the riprap effect according to different flow velocity monitoring sites;
根据不同流速监测位点提取相关数据时，所有数据应处于同一坐投影坐标系统下；When extracting relevant data from different flow velocity monitoring sites, all data should be in the same projection coordinate system;
用于模拟抛石护岸工程效果的数据应当至少采集于两个不同时段，即抛石施工后立即监测的地形数据和抛石体水下运移稳定后监测的地形数据，且两次收集的河段相同，提取地形信息的监测点相同；The data used for simulating the effect of the rock riprap revetment project should be collected at least in two different time periods, namely the terrain data monitored immediately after the rock riprap construction and the topographic data monitored after the rock riprap underwater migrated and stabilized. The segments are the same, and the monitoring points for extracting terrain information are the same;
所述目标河段河床地形数据包括：坡度、坡向、流速、流向、底床高程数据，该地形数据为抛石效果影响因子(即自变量)；选择从抛石施工到抛石体水下运移稳定之间的底床高程增量，作为抛石效果评价指标(即因变量)。The topographical data of the riverbed of the target river reach includes: slope, slope aspect, flow velocity, flow direction, and bed elevation data, and the topographical data is the impact factor (ie, independent variable) of the rock dumping effect; The bed elevation increment between the migration and stability is used as the evaluation index (ie dependent variable) of the riprap effect.
步骤二，建立增强回归树模型，随机抽取90％目标河段河床地形数据作为训练样本用于训练该模型，运用集成学习方法，始终保证新模型在原来模型损失函数下降的基础上建立。学习过程中，损失函数持续下降，模型不断改进。预设学习速率为0.1、分型次数为300次，即控制每次损失函数下降的偏导数为0.1，运行300次后停止训练，输出结果为300次训练的共同结果。Step 2: Build an enhanced regression tree model, randomly select 90% of the target river bed terrain data as training samples for training the model, and use the ensemble learning method to always ensure that the new model is established on the basis of the loss function of the original model. During the learning process, the loss function continues to decrease and the model continues to improve. The preset learning rate is 0.1 and the number of typing is 300 times, that is, the partial derivative that controls the decrease of each loss function is 0.1. After running for 300 times, the training is stopped, and the output result is the common result of the 300 times of training.
所述增强回归树，是迭代训练的多棵回归树之和，包括两部分：第一部分为训练误差，第二部分为每棵树复杂度之和，公式如下：The enhanced regression tree is the sum of multiple regression trees trained iteratively, and includes two parts: the first part is the training error, and the second part is the sum of the complexity of each tree. The formula is as follows:
其中，Obj(θ)为增强回归树函数，l为损失函数，Ω为正则函数，n为树的数目，T为每棵树的叶的数目，yi为第i次训练时训练样本每组数据因变量实测值，
(1)单棵回归树模型为：(1) The single regression tree model is:
式中，xi为自变量，F为回归树的集合空间。目的在于将输入的数据按属性分配到各叶节点，每个叶节点上均对应一个实数；In the formula, x i is the independent variable, and F is the set space of the regression tree. The purpose is to assign the input data to each leaf node by attributes, and each leaf node corresponds to a real number;
(2)训练增强回归树函数时，每次在原有增强回归树函数基础上加入一个新函数，使得目标函数尽量最大地降低，即：(2) When training the enhanced regression tree function, each time a new function is added on the basis of the original enhanced regression tree function, so that the objective function is reduced as much as possible, namely:
式中，Obj(t)为加入新函数后的增强回归树函数，
(3)树的复杂度：复杂度包含了一棵树里面节点的个数，以及每个树叶子节点上面输出分数的模平方，即：(3) The complexity of the tree: The complexity includes the number of nodes in a tree, and the modulo square of the output score on the leaf node of each tree, namely:
式中，ωj为叶子的向量。In the formula, ω j is the vector of leaves.
学习过程中损失函数持续下降，模型不断改进，为防止模型过度拟合，设置学习速率，即：During the learning process, the loss function continues to decrease and the model continues to improve. To prevent the model from overfitting, the learning rate is set, namely:
式中，v为学习速率。where v is the learning rate.
步骤三，输出模拟结果校核模型。
在训练所得模型中输入剩余10％数据的自变量以模拟底床高程增量，与实测值进行验证，并计算模型训练数据的均方根误差、标准化平均误差、拟合优度，验证模型的可靠性。其中，Input the independent variables of the remaining 10% of the data in the training model to simulate the bed elevation increment, verify with the measured value, and calculate the root mean square error, standardized average error, and goodness of fit of the model training data to verify the model's reliability. in,
均方根误差
标准化平均误差
拟合优度
若满足RMES≤0.5m、NME＜50％、R2＞0.8，则模型达到预期，进行步骤四；否则，调整参数使模型准确性满足要求。调整参数时，需同时调整模型的学习速率和分型次数(即树的数量)，当提高学习速率时，降低分型次数；降低学习速率时，提高分型次数。If RMES≤0.5m, NME<50%, and R 2 >0.8 are satisfied, the model meets expectations, and the fourth step is performed; otherwise, the parameters are adjusted to make the model accuracy meet the requirements. When adjusting the parameters, it is necessary to adjust the learning rate of the model and the number of classifications (that is, the number of trees).
如图2所示，是本发明实施实例中预测值与真实值数据，经校核，训练数据的均方根误差为0.35m，标准化平均误差为11.88％，拟合优度达到0.91，模型具有可靠性。As shown in Figure 2, it is the predicted value and the actual value data in the implementation example of the present invention. After checking, the root mean square error of the training data is 0.35m, the standardized average error is 11.88%, and the goodness of fit reaches 0.91. The model has reliability.
步骤四，评价影响抛石体水下运移因素贡献率：通过模型运算，计算不同影响因素对抛石效果的贡献率，评价各因素对抛石护岸实施工程效果影响的重要性，输出坡度、坡向、流速、流向、底床高程对底床高程增量影响效果。如图3所示，是本发明实施实例中计算获取的影响因子贡献率。Step 4: Evaluate the contribution rate of the factors affecting the underwater migration of the riprap: Calculate the contribution rate of different influencing factors to the riprap effect through model operation, evaluate the importance of each factor on the effect of the riprap revetment implementation, and output the slope, The effect of slope aspect, flow velocity, flow direction, and bottom bed elevation on the incremental bed elevation. As shown in FIG. 3 , it is the contribution rate of the influence factor obtained by calculation in the embodiment of the present invention.
以上所述仅是本发明的优选实施方式，应当指出，对于本技术领域的普通技术人员来说，在不脱离本发明技术原理的前提下，还可以做出若干改进和变形，这些改进和变形也应视为本发明的保护范围。The above are only the preferred embodiments of the present invention. It should be pointed out that for those skilled in the art, without departing from the technical principle of the present invention, several improvements and modifications can also be made. These improvements and modifications It should also be regarded as the protection scope of the present invention.
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