CN108446417A - Severe cold area high-speed railway subgrade stability online interaction formula appraisal procedure and device - Google Patents
Severe cold area high-speed railway subgrade stability online interaction formula appraisal procedure and device Download PDFInfo
- Publication number
- CN108446417A CN108446417A CN201810012451.5A CN201810012451A CN108446417A CN 108446417 A CN108446417 A CN 108446417A CN 201810012451 A CN201810012451 A CN 201810012451A CN 108446417 A CN108446417 A CN 108446417A
- Authority
- CN
- China
- Prior art keywords
- subgrade
- monitoring
- deformation
- prediction
- temperature
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Pending
Links
- 238000000034 method Methods 0.000 title claims abstract description 25
- 230000003993 interaction Effects 0.000 title claims abstract 10
- 238000012544 monitoring process Methods 0.000 claims abstract description 140
- 238000011156 evaluation Methods 0.000 claims description 42
- 238000012545 processing Methods 0.000 claims description 27
- 239000002689 soil Substances 0.000 claims description 13
- 238000004458 analytical method Methods 0.000 claims description 11
- 238000010276 construction Methods 0.000 claims description 10
- 230000008859 change Effects 0.000 claims description 6
- 238000004088 simulation Methods 0.000 claims description 6
- 238000007405 data analysis Methods 0.000 claims description 5
- 238000007689 inspection Methods 0.000 claims description 4
- 239000000463 material Substances 0.000 claims description 3
- 238000012216 screening Methods 0.000 claims description 3
- 238000009792 diffusion process Methods 0.000 claims 1
- 239000012530 fluid Substances 0.000 claims 1
- 238000005516 engineering process Methods 0.000 abstract description 4
- 238000013097 stability assessment Methods 0.000 abstract description 4
- 230000002452 interceptive effect Effects 0.000 description 11
- 230000006870 function Effects 0.000 description 7
- 230000007774 longterm Effects 0.000 description 6
- 238000012937 correction Methods 0.000 description 5
- 238000013461 design Methods 0.000 description 5
- 230000008569 process Effects 0.000 description 5
- 238000012360 testing method Methods 0.000 description 5
- 238000004364 calculation method Methods 0.000 description 4
- 230000008014 freezing Effects 0.000 description 4
- 238000007710 freezing Methods 0.000 description 4
- 238000007781 pre-processing Methods 0.000 description 4
- 238000011161 development Methods 0.000 description 3
- 238000010586 diagram Methods 0.000 description 3
- 238000005259 measurement Methods 0.000 description 3
- 238000005070 sampling Methods 0.000 description 3
- 230000007704 transition Effects 0.000 description 3
- XLYOFNOQVPJJNP-UHFFFAOYSA-N water Substances O XLYOFNOQVPJJNP-UHFFFAOYSA-N 0.000 description 3
- 238000012986 modification Methods 0.000 description 2
- 230000004048 modification Effects 0.000 description 2
- 230000009466 transformation Effects 0.000 description 2
- 238000013519 translation Methods 0.000 description 2
- 230000009286 beneficial effect Effects 0.000 description 1
- 230000008901 benefit Effects 0.000 description 1
- 230000005540 biological transmission Effects 0.000 description 1
- 238000004422 calculation algorithm Methods 0.000 description 1
- 230000008878 coupling Effects 0.000 description 1
- 238000010168 coupling process Methods 0.000 description 1
- 238000005859 coupling reaction Methods 0.000 description 1
- 230000001186 cumulative effect Effects 0.000 description 1
- 238000013500 data storage Methods 0.000 description 1
- 230000006806 disease prevention Effects 0.000 description 1
- 238000012423 maintenance Methods 0.000 description 1
- 238000007726 management method Methods 0.000 description 1
- 230000007246 mechanism Effects 0.000 description 1
- 230000001737 promoting effect Effects 0.000 description 1
- 230000009467 reduction Effects 0.000 description 1
- 230000008054 signal transmission Effects 0.000 description 1
- 230000003068 static effect Effects 0.000 description 1
Classifications
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F30/00—Computer-aided design [CAD]
- G06F30/20—Design optimisation, verification or simulation
Landscapes
- Engineering & Computer Science (AREA)
- Physics & Mathematics (AREA)
- Theoretical Computer Science (AREA)
- Computer Hardware Design (AREA)
- Evolutionary Computation (AREA)
- Geometry (AREA)
- General Engineering & Computer Science (AREA)
- General Physics & Mathematics (AREA)
- Management, Administration, Business Operations System, And Electronic Commerce (AREA)
Abstract
Description
技术领域technical field
本发明属于路基稳定性评估技术领域，尤其涉及一种严寒地区高速铁路路基稳定性在线交互式评估方法及装置。The invention belongs to the technical field of roadbed stability evaluation, in particular to an online interactive evaluation method and device for high-speed railway roadbed stability in severe cold regions.
背景技术Background technique
我国严寒地区高速铁路建设正在大规模开展，高速铁路的高稳定性和高平顺性给路基稳定性提出了高标准的要求。路基稳定性的准确评估和长期趋势预测是严寒地区高速铁路运营安全的重要保证。传统的路基稳定性评估方法通常采用长期在线监测和数值计算方法，然而由于严寒地区高速铁路路基稳定性演化机理复杂，采用传统的评估方法对严寒地区高速铁路路基稳定性进行评估的准确度较差，难以准确及时评估其稳定性。The construction of high-speed railways in severe cold areas in my country is being carried out on a large scale. The high stability and smoothness of high-speed railways put forward high standards for the stability of roadbeds. Accurate assessment of subgrade stability and long-term trend prediction are important guarantees for high-speed railway operation safety in severe cold regions. Traditional subgrade stability assessment methods usually use long-term on-line monitoring and numerical calculation methods. However, due to the complex evolution mechanism of high-speed railway subgrade stability in severe cold regions, the accuracy of evaluating the stability of high-speed railway subgrades in severe cold regions using traditional assessment methods is poor. , it is difficult to accurately and timely evaluate its stability.
发明内容Contents of the invention
有鉴于此，本发明实施例提供了一种严寒地区高速铁路路基稳定性在线交互式评估方法及装置，以解决目前评估方法对严寒地区高速铁路路基稳定性进行评估的准确度较差问题。In view of this, the embodiments of the present invention provide an online interactive evaluation method and device for high-speed railway embankment stability in severe cold regions, so as to solve the problem of poor accuracy of current evaluation methods for evaluating the stability of high-speed railway embankment stability in severe cold regions.
本发明实施例的第一方面提供了一种严寒地区高速铁路路基稳定性在线交互式评估方法，包括：The first aspect of the embodiments of the present invention provides an online interactive evaluation method for high-speed railway subgrade stability in severe cold areas, including:
通过监测系统获取监测区域的监测数据，并对所述监测数据进行初步处理；Obtain the monitoring data of the monitoring area through the monitoring system, and perform preliminary processing on the monitoring data;
根据地温估算模型和经过所述初步处理的监测数据对路基地温进行不确定性预测，根据动态灰色预测模型和经过所述初步处理的监测数据对路基变形进行不确定性预测；Uncertainty prediction of subgrade temperature is carried out according to the ground temperature estimation model and the monitoring data after the preliminary processing, and uncertainty prediction of subgrade deformation is carried out according to the dynamic gray prediction model and the monitoring data after the preliminary processing;
根据地温场的控制微分方程和经过所述初步处理的监测数据对路基地温进行确定性预测，根据冻土区变形和应力的控制方程和经过所述初步处理的监测数据对路基变形进行确定性预测；其中所述冻土区变形和应力的控制方程的温度边界条件根据路基地温的不确定性预测结果进行调整；Deterministically predict the subgrade temperature according to the control differential equation of the geothermal field and the monitoring data that has undergone the preliminary processing, and deterministically predict the subgrade deformation according to the control equation of deformation and stress in the permafrost region and the monitoring data that has undergone the preliminary processing Prediction; wherein the temperature boundary condition of the governing equation of deformation and stress in the permafrost region is adjusted according to the uncertainty prediction results of the subgrade temperature;
根据路基地温与路基形变的不确定性预测结果和路基地温与路基形变的确定性预测结果，对路基稳定性进行评估。According to the uncertain prediction results of subgrade temperature and subgrade deformation and the deterministic prediction results of subgrade temperature and subgrade deformation, the subgrade stability is evaluated.
本发明实施例的第二方面提供了一种严寒地区高速铁路路基稳定性在线交互式评估装置，包括：The second aspect of the embodiment of the present invention provides an online interactive evaluation device for high-speed railway embankment stability in severe cold areas, including:
获取模块，用于通过监测系统获取监测区域的监测数据，并对所述监测数据进行初步处理；An acquisition module, configured to acquire monitoring data in the monitoring area through the monitoring system, and perform preliminary processing on the monitoring data;
不确定性评估模块，用于根据地温估算模型和经过所述初步处理的监测数据对路基地温进行不确定性预测，根据动态灰色预测模型和经过所述初步处理的监测数据对路基变形进行不确定性预测；The uncertainty evaluation module is used to predict the uncertainty of the roadbed temperature according to the ground temperature estimation model and the monitoring data that has undergone the preliminary processing, and to perform different calculations on the deformation of the roadbed according to the dynamic gray prediction model and the monitoring data that has undergone the preliminary processing. deterministic forecasting;
确定性评估模块，用于根据地温场的控制微分方程和经过所述初步处理的监测数据对路基地温进行确定性预测，根据冻土区变形和应力的控制方程和经过所述初步处理的监测数据对路基变形进行确定性预测；其中所述冻土区变形和应力的控制方程的温度边界条件根据路基地温的不确定性预测结果进行调整；The deterministic evaluation module is used for deterministically predicting the subgrade temperature according to the control differential equation of the geothermal field and the monitoring data that has undergone the preliminary processing, and according to the control equation of deformation and stress in the permafrost region and the monitoring data that has undergone the preliminary processing Deterministic prediction of subgrade deformation by data; wherein the temperature boundary condition of the control equation of deformation and stress in the permafrost region is adjusted according to the uncertainty prediction result of subgrade temperature;
综合评估模块，用于根据路基地温与路基形变的不确定性预测结果和路基地温与路基形变的确定性预测结果，对路基稳定性进行评估。The comprehensive evaluation module is used to evaluate the stability of the subgrade according to the uncertain prediction results of subgrade temperature and subgrade deformation and the deterministic prediction results of subgrade temperature and subgrade deformation.
本发明实施例与现有技术相比存在的有益效果是：通过根据地温估算模型和经过初步处理的监测数据对路基地温进行不确定性预测，根据动态灰色预测模型和经过初步处理的监测数据对路基变形进行不确定性预测，能够实现不确定评估方法对路基稳定性进行评估；通过根据地温场的控制微分方程和经过初步处理的监测数据对路基地温进行确定性预测，根据冻土区变形和应力的控制方程和经过初步处理的监测数据对路基变形进行确定性预测，能够实现确定性评估方法对路基稳定性进行评估。本发明实施例采用确定性和不确定相结合的路基稳定性评估方法，能够全面评估严寒地区高铁路基的稳定性，提高路基稳定性评估的准确度。Compared with the existing technology, the beneficial effect of the embodiment of the present invention is that the uncertainty prediction of the subgrade temperature is carried out according to the ground temperature estimation model and the preliminary processed monitoring data, and according to the dynamic gray prediction model and the preliminary processed monitoring data Uncertainty prediction of subgrade deformation can realize the uncertainty evaluation method to evaluate subgrade stability; through the deterministic prediction of subgrade temperature based on the control differential equation of the geothermal field and the preliminarily processed monitoring data, according to the permafrost region The governing equations of deformation and stress and the preliminary processed monitoring data can deterministically predict the deformation of the subgrade, and the deterministic evaluation method can be used to evaluate the stability of the subgrade. The embodiments of the present invention adopt a roadbed stability evaluation method combining certainty and uncertainty, which can comprehensively evaluate the stability of high-speed railway foundations in severe cold regions, and improve the accuracy of roadbed stability evaluation.
附图说明Description of drawings
为了更清楚地说明本发明实施例中的技术方案，下面将对实施例或现有技术描述中所需要使用的附图作简单地介绍，显而易见地，下面描述中的附图仅仅是本发明的一些实施例，对于本领域普通技术人员来讲，在不付出创造性劳动性的前提下，还可以根据这些附图获得其他的附图。In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the following will briefly introduce the accompanying drawings that need to be used in the descriptions of the embodiments or the prior art. Obviously, the accompanying drawings in the following description are only of the present invention. For some embodiments, those of ordinary skill in the art can also obtain other drawings based on these drawings without paying creative efforts.
图1是本发明实施例提供的严寒地区高速铁路路基稳定性在线交互式评估方法的实现流程图；Fig. 1 is the implementation flow diagram of the online interactive evaluation method for high-speed railway subgrade stability in severe cold regions provided by the embodiment of the present invention;
图2是本发明实施例提供的严寒地区高速铁路路基稳定性在线交互式评估方法中通过监测系统获取监测区域的监测数据的实现流程图；Fig. 2 is the implementation flowchart of obtaining the monitoring data of the monitoring area through the monitoring system in the online interactive evaluation method for the stability of the high-speed railway subgrade in severe cold regions provided by the embodiment of the present invention;
图3是本发明实施例提供的高速铁路路基稳定性在线评估装置的示意图。Fig. 3 is a schematic diagram of an online assessment device for high-speed railway subgrade stability provided by an embodiment of the present invention.
具体实施方式Detailed ways
以下描述中，为了说明而不是为了限定，提出了诸如特定系统结构、技术之类的具体细节，以便透彻理解本发明实施例。然而，本领域的技术人员应当清楚，在没有这些具体细节的其它实施例中也可以实现本发明。在其它情况中，省略对众所周知的系统、装置、电路以及方法的详细说明，以免不必要的细节妨碍本发明的描述。In the following description, specific details such as specific system structures and technologies are presented for the purpose of illustration rather than limitation, so as to thoroughly understand the embodiments of the present invention. It will be apparent, however, to one skilled in the art that the invention may be practiced in other embodiments without these specific details. In other instances, detailed descriptions of well-known systems, devices, circuits, and methods are omitted so as not to obscure the description of the present invention with unnecessary detail.
为了说明本发明所述的技术方案，下面通过具体实施例来进行说明。In order to illustrate the technical solutions of the present invention, specific examples are used below to illustrate.
图1为本发明实施例提供的严寒地区高速铁路路基稳定性在线交互式评估方法的实现流程图，详述如下：Fig. 1 is the implementation flowchart of the online interactive evaluation method for the stability of the high-speed railway embankment in severe cold regions provided by the embodiment of the present invention, which is described in detail as follows:
在S101中，通过监测系统获取监测区域的监测数据，并对所述监测数据进行初步处理。In S101, the monitoring data of the monitoring area is acquired through the monitoring system, and preliminary processing is performed on the monitoring data.
在本实施例中，可以通过数值模拟确定监测区域。可选地，所述初步处理包括预处理、查询和初步分析；预处理包括对所述监测数据的检核、筛选和奇异值检验与插补；查询包括数据查询和曲线绘制；初步分析包括报警判断和数据分析。In this embodiment, the monitoring area can be determined through numerical simulation. Optionally, the preliminary processing includes preprocessing, query and preliminary analysis; preprocessing includes checking, screening and singular value inspection and interpolation of the monitoring data; query includes data query and curve drawing; preliminary analysis includes alarming judgment and data analysis.
具体地，数据查询是指能够查询指定断面、指定时间段、指定测孔、指定深度等对应的监测数据。曲线绘制是指可以按指定查询范围绘制时程曲线和深度-数据曲线。曲线绘制主要是利用数据库中存储的监测数据自动绘制各种曲线图表，可以包括但不限于路基沉降时程曲线、剖面沉降曲线、地温时程曲线、地温随深度变化曲线、土体静压力时程曲线、土体动应力时程曲线和含水量变化曲线等。报警判断主要是根据监测数据判断是否需要报警，如通过路基沉降变形或动应力与预警指标的对比进行判断，数据分析是指监测数据的基本处理工作，如分析各时间段沉降速率的变化等。Specifically, data query refers to the ability to query monitoring data corresponding to specified cross-sections, specified time periods, specified boreholes, and specified depths. Curve drawing means that the time history curve and depth-data curve can be drawn according to the specified query range. Curve drawing is mainly to use the monitoring data stored in the database to automatically draw various curves and charts, which can include but not limited to subgrade settlement time history curves, profile settlement curves, ground temperature time history curves, ground temperature change curves with depth, and soil static pressure time history curves Curves, soil dynamic stress time history curves and water content change curves, etc. The alarm judgment is mainly based on the monitoring data to determine whether an alarm is necessary, such as through the comparison of subgrade settlement deformation or dynamic stress with early warning indicators. Data analysis refers to the basic processing of monitoring data, such as analyzing the change of settlement rate in each time period.
作为本发明的一个实施例，如图2所示，S101中通过监测系统获取监测区域的监测数据可以包括：As an embodiment of the present invention, as shown in FIG. 2, obtaining monitoring data of the monitoring area through the monitoring system in S101 may include:
在S201中，确定稳定性监测指标和测点布设方案，并通过数值模拟确定所述监测区域。In S201, the stability monitoring index and the arrangement scheme of the measuring points are determined, and the monitoring area is determined through numerical simulation.
在本实施例中，针对严寒地区高铁路基稳定性监测指标缺失的问题，通过分析寒区高铁路基工作特性，根据寒区高铁路基稳定性的评价指标及影响因素，可以确定寒区高铁路基稳定性监测指标如表1所示。In this example, aiming at the lack of monitoring indicators for high-speed railway foundation stability in severe cold regions, by analyzing the working characteristics of high-speed railway foundations in cold regions, the stability of high-speed railway foundations in cold regions can be determined according to the evaluation indicators and influencing factors of high-speed railway foundation stability in cold regions The monitoring indicators are shown in Table 1.
表1寒区高铁路基稳定性监测指标Table 1 Monitoring indicators of high-speed railway foundation stability in cold regions
根据寒区高铁工程几何环境特点和路基防冻胀设计措施，通过数值模拟选定监测区域，并设计路基横向断面及纵向连续相结合的多场空间测点布设方案。其中，路基断面的监测数据可以包括但不限于分层沉降变形、地温、土体应力、含水量四类监测项目。在不同结构型式的过渡段区域，自桥(涵)框架边缘处沿线路方向在过渡段范围内的路肩表面每隔预设距离(如5m)布置一个测点，构成纵向沉降监测线布设，实现多参数的立体监测。According to the geometric environment characteristics of the high-speed railway project in the cold region and the anti-frost heaving design measures of the roadbed, the monitoring area is selected through numerical simulation, and the multi-field spatial measuring point layout plan combining the transverse section of the roadbed and the vertical continuity is designed. Among them, the monitoring data of the subgrade section may include but not limited to four types of monitoring items: layered settlement deformation, ground temperature, soil stress, and water content. In the transition section area of different structural types, a measuring point is arranged every preset distance (such as 5m) from the edge of the bridge (culvert) frame along the road shoulder surface within the range of the transition section to form the layout of the longitudinal settlement monitoring line. Stereoscopic monitoring of multiple parameters.
在S202中，根据所述稳定性监测指标和所述测点布设方案建立所述监测系统。In S202, the monitoring system is established according to the stability monitoring index and the measuring point arrangement scheme.
在本实施例中，根据稳定性监测指标和测点布设方案，选择监测传感器和数据传输方案，构建监测系统。监测系统可以包括但不限于现场监测站和监测中心。现场监测站自动采集各种监测数据，并通过多模式无线网络自动传输给监测中心，在中心完成数据的存储、检索、分析和评估等。监测系统可实现远程自动控制，可以通过监测中心向现场监测站发布信息变更测量时间测量内容、召测、改变现场监测站的运行参数以及程序流程等。In this embodiment, according to the stability monitoring index and the layout scheme of the measuring points, the monitoring sensors and the data transmission scheme are selected to construct the monitoring system. Monitoring systems may include, but are not limited to, on-site monitoring stations and monitoring centers. The on-site monitoring station automatically collects various monitoring data, and automatically transmits them to the monitoring center through a multi-mode wireless network, where the data storage, retrieval, analysis and evaluation are completed. The monitoring system can realize remote automatic control, and can release information to the on-site monitoring station through the monitoring center to change the measurement time and measurement content, call for measurement, change the operating parameters and program flow of the on-site monitoring station, etc.
在S203中，通过所述监测系统获取所述监测区域的监测数据。In S203, the monitoring data of the monitoring area is acquired through the monitoring system.
本实施例构建的严寒地区高速铁路路基长期自动监测系统，具有全自动、高精度、低功耗、安全防护好、长期稳定可靠的优点，可以实现沉降变形、土体应力、地温、含水量等路基状态参量一体化的自动采集、信号自动传输、数据自动分析处理。The long-term automatic monitoring system for high-speed railway subgrades in severe cold areas constructed in this embodiment has the advantages of full automation, high precision, low power consumption, good safety protection, long-term stability and reliability, and can realize settlement deformation, soil stress, ground temperature, water content, etc. Integrated automatic collection of subgrade state parameters, automatic signal transmission, and automatic data analysis and processing.
在S102中，根据地温估算模型和经过所述初步处理的监测数据对路基地温进行不确定性预测，根据动态灰色预测模型和经过所述初步处理的监测数据对路基变形进行不确定性预测。In S102, perform uncertainty prediction on subgrade temperature according to the ground temperature estimation model and the preliminary processed monitoring data, and perform uncertainty prediction on subgrade deformation according to the dynamic gray prediction model and the preliminary processed monitoring data.
在本实施例中，可以通过不确定的路基稳定性评估方法对路基地温以及路基变形进行预测评估。下面首先对路基地温的不确定性预测进行说明。In this embodiment, the subgrade temperature and subgrade deformation can be predicted and evaluated through an uncertain subgrade stability evaluation method. Firstly, the uncertainty prediction of subgrade temperature will be described below.
根据监测数据可以得到寒区高铁路基长期地温实测资料，在此基础上，考虑平均地温、地温振幅、土体性质及相位差异等影响，同时为克服现场监测数据分散及现场监测中部分数据易缺失的不足，采用半经验半理论的分析方法，可以得到随深度和时间连续变化的路基不同位置的地温估算公式。According to the monitoring data, the long-term measured ground temperature data of the high-speed railway base in the cold region can be obtained. On this basis, the influence of the average ground temperature, ground temperature amplitude, soil property and phase difference, etc. are considered. Insufficiency of using the semi-empirical and semi-theoretical analysis method, the ground temperature estimation formula of different positions of the subgrade that changes continuously with depth and time can be obtained.
考虑时间相位差的影响，假定土体是状态不变的匀质土，对某一地区的某一路基断面，确定横向位置x，第t天深度z处的温度T(z,t)可以采用式(1)的地温估算模型进行估算：Considering the influence of time phase difference, assuming that the soil is a homogeneous soil with a constant state, for a certain subgrade section in a certain area, determine the lateral position x, and the temperature T(z,t) at the depth z on the tth day can be adopted The ground temperature estimation model of formula (1) is used for estimation:
其中，Tm为深度z处的年平均地温，As为地表处温度年变化幅值，z为距离路基表面的深度的绝对值，au为忽略地球内部热流时土体的平均热扩散系数，p为振动周期，为初相角，t为距离起算日期的时间，表示深度z处相位相对于地表的滞后程度。Among them, T m is the annual mean ground temperature at the depth z, A s is the annual variation amplitude of the temperature at the ground surface, z is the absolute value of the depth from the surface of the subgrade, and a u is the average thermal diffusivity of the soil when the heat flow inside the earth is neglected , p is the vibration period, is the initial phase angle, t is the time from the starting date, Indicates how much the phase lags relative to the surface at depth z.
地温振幅随深度增大呈指数规律衰减，如式(2)所示：The ground temperature amplitude decays exponentially with depth, as shown in formula (2):
年平均地温包络线类型可以为正梯度型或负梯度型，表示年平均地温可以随深度增加而增加或降低。因此，在实际计算中可以根据年平均地温包络线类型，来选择合适的函数形式表示年平均地温。The annual average ground temperature envelope type can be a positive gradient type or a negative gradient type, which means that the annual average ground temperature can increase or decrease with depth. Therefore, in actual calculation, the appropriate function form can be selected to represent the annual average ground temperature according to the type of the annual average ground temperature envelope.
在预测初期可以根据气象资料预测的振幅平均值作为年平均地温的原始序列，在预测后期可以根据监测数据拟合出不同位置地温振幅随深度的年平均地温函数，进而得到年平均地温。另外，由于路基填料与地基土体性质不同，地温振幅随深度的分布规律可以分为两部分进行拟合。In the early stage of prediction, the average amplitude of the predicted meteorological data can be used as the original series of annual average ground temperature. In the later stage of prediction, the annual average ground temperature function of the amplitude of ground temperature at different locations with depth can be fitted according to the monitoring data, and then the annual average ground temperature can be obtained. In addition, due to the different properties of subgrade filling and foundation soil, the distribution law of ground temperature amplitude with depth can be divided into two parts for fitting.
滞后相位在预测初期可以根据气象资料进行设定，而在预测后期再根据实测资料对滞后相位逐步修正，例如可以根据路基不同位置处达到最大地温的时间与起算时间的差距，计算得到滞后相位。The lag phase can be set according to the meteorological data in the early stage of prediction, and the lag phase can be gradually corrected according to the measured data in the later stage of prediction. For example, the lag phase can be calculated according to the difference between the time when the maximum ground temperature is reached at different positions of the roadbed and the starting time.
在实测资料积累到一定程度时，可以得到更为接近实测值的地温估算公式。可以通过路基不同深度实测值与估算值以及实测冻深与估算冻深的对比，验证地温估算模型的可靠性，进而利用地温估算模型进一步对各断面冻深、横向地温差异、冻结时程等数据信息进行深入挖掘，从而有助于路基温度稳定性的评估。When the measured data accumulate to a certain extent, the ground temperature estimation formula closer to the measured value can be obtained. The reliability of the ground temperature estimation model can be verified by comparing the measured and estimated values at different depths of the subgrade, as well as the measured and estimated freezing depths. Then, the ground temperature estimation model can be used to further analyze the freezing depth of each section, the lateral ground temperature difference, and the freezing time history. The information is deeply mined, which is helpful for the evaluation of the temperature stability of the subgrade.
作为本发明的一个实施例，S102中根据动态灰色预测模型和经过所述初步处理的监测数据对路基变形进行不确定性预测可以包括：As an embodiment of the present invention, in S102, the uncertainty prediction of the subgrade deformation according to the dynamic gray prediction model and the monitoring data after the preliminary processing may include:
将经过所述初步处理的监测数据形成数据序列，对所述数据序列进行级比检验；Forming the monitoring data through the preliminary processing into a data sequence, and performing a grade comparison test on the data sequence;
建立所述动态灰色预测模型，将通过级比检验的数据序列作为所述动态灰色预测模型的参考序列；其中，所述动态灰色预测模型的背景构造值为The dynamic gray prediction model is set up, and the data sequence passed through the scale test is used as the reference sequence of the dynamic gray prediction model; wherein, the background construction value of the dynamic gray prediction model is
z(1)(k)＝ax(1)(k)+(1-a)x(1)(k-1),k＝2,3,…,n (3)z (1) (k)=ax (1) (k)+(1-a)x (1) (k-1),k=2,3,...,n(3)
其中a为构造范围参数，a∈(0,1)；Where a is the construction range parameter, a∈(0,1);
根据x(0)(n)的相对误差Δn(a)、平均相对误差和均方差比值C0(a)确定最佳值aopt；Relative error Δ n (a) according to x (0) (n), average relative error and mean square error ratio C 0 (a) to determine the optimal value a opt ;
根据所述最佳值aopt和所述动态灰色预测模型得到预测值 According to the optimal value a opt and the dynamic gray prediction model to obtain the predicted value
下面进行具体说明。A detailed description will be given below.
在本实施例中，为了在监测过程中实现实时预测，尤其是在冻融循环期间实现对小量级的沉降变形实时高精度的预测，建立了动态的灰色预测模型。In this embodiment, in order to realize real-time prediction during the monitoring process, especially to realize real-time and high-precision prediction of small-scale settlement deformation during the freeze-thaw cycle, a dynamic gray prediction model is established.
首先，为了保证建模方法的可行性，需要对数据序列做检验处理，以是否通过级比检验来判断数据序列能否根据动态灰色预测模型进行灰色预测。可以通过式(4)计算数据数列的级比：First of all, in order to ensure the feasibility of the modeling method, it is necessary to test the data sequence to determine whether the data sequence can be gray forecasted according to the dynamic gray forecasting model. The level ratio of the data series can be calculated by formula (4):
如果数据序列的所有级比λ(k)都落在可容范围内，则该数据序列可以作为动态灰色预测模型的参考序列进行灰色预测。否则，需要先对该数据序列做平移变换处理，使该数据序列的所有级比都落入可容覆盖内，再将该数据序列作为动态灰色预测模型的参考序列。例如取适当的常数c，可以通过式(5)对该数据序列作平移变换：If all level ratios λ(k) of the data sequence fall within the tolerance range , the data sequence can be used as the reference sequence of the dynamic gray prediction model for gray prediction. Otherwise, the data sequence needs to be translated and transformed first, so that all the levels of the data sequence fall into the coverage, and then the data sequence is used as the reference sequence of the dynamic gray prediction model. For example, if an appropriate constant c is chosen, the data sequence can be transformed by formula (5):
y(0)＝x(0)(k)+c,k＝1,2,…,n (5)y (0) =x (0) (k)+c,k=1,2,...,n(5)
通过平移变换可以使数列y(0)＝(y(1),y(2),…,y(n))的级比λy(k)落入可容覆盖区内，再将平移变换后的数据序列作为动态灰色预测模型的参考序列。Through the translation transformation, the level ratio λ y (k) of the sequence y (0) = (y(1), y(2),...,y(n)) can be made to fall into the accommodating coverage area, and then the translation transformation The data sequence is used as the reference sequence of the dynamic gray forecasting model.
本实施例为提高预测模型的精度，改进了背景构造值，建立了动态灰色预测模型——GM(1,1,a)模型。在GM(1,1,a)模型中，对于a∈(0,1)，背景构造值为如式(3)所示。In this embodiment, in order to improve the accuracy of the prediction model, the background structure value is improved, and a dynamic gray prediction model—GM(1,1,a) model is established. In the GM(1,1,a) model, for a∈(0,1), the background construction value is shown in formula (3).
动态灰色预测模型白化后可以得到预测值为充分保证预测值的准确性，不同于普通模型中a最佳值的判断依据，本实施例基于灰色系统理论中的“新信息优先”原理，以x^((0))(n)的相对误差Δn(a)为主，平均相对误差和均方差比值C0(a)为辅作为a取最佳值aopt的判据，即取Δn(a)绝对值最小时的a为最佳值aopt，且最佳值aopt须满足和C0(a)在同一精度等级内。确定aopt后即可得到动态灰色预测模型GM(1,1,a)的预测值 The predicted value can be obtained after the dynamic gray forecasting model is whitened In order to fully ensure the accuracy of the predicted value, different from the basis for judging the optimal value of a in the ordinary model, this embodiment is based on the principle of "new information first" in the gray system theory, with x^((0))(n) The relative error Δ n (a) is the main one, and the average relative error and the mean square error ratio C 0 (a) as the criterion for choosing the optimal value a opt of a, that is, the optimal value a opt is taken when the absolute value of Δ n (a) is the smallest, and the optimal value a opt must be Satisfy Within the same accuracy class as C 0 (a). After determining a opt , the predicted value of the dynamic gray prediction model GM(1,1,a) can be obtained
作为本发明的一个实施例，在步骤“根据所述最佳值aopt和所述动态灰色预测模型得到预测值”之后，还可以包括：As an embodiment of the present invention, in the step "according to the optimal value a opt and the dynamic gray prediction model to obtain the predicted value ", you can also include:
判断所述预测值是否满足预设精度条件；judging whether the predicted value satisfies a preset accuracy condition;
若所述预测值不满足预设精度条件，则根据预设间隔对次最佳值a′opt进行采样，依次将采样值作为所述动态灰色预测模型中a的取值，直至求得预设值的精度稳定为止；If the predicted value does not meet the preset accuracy condition, then sample the sub-optimal value a'opt according to the preset interval, and use the sampled value as the value of a in the dynamic gray prediction model in turn until the preset until the precision of the value stabilizes;
根据所述精度稳定对应的采样值和所述动态灰色预测模型确定所述预测值 Determine the prediction value according to the sampling value corresponding to the stability of the accuracy and the dynamic gray prediction model
在本实施例中，预设精度条件可以根据实际情况和监测精度确定。若预测值不满足预设精度条件，则表明该预测值精度较低，将该预测值舍弃，然后根据预设间隔(如0.01)对次最佳值a′opt进行采样，依次将采样值作为动态灰色预测模型中a的取值，直至求得预测值的精度稳定为止，将此时a的取值代入动态灰色预测模型确定预测值 In this embodiment, the preset accuracy condition may be determined according to actual conditions and monitoring accuracy. If the predicted value does not meet the preset accuracy conditions, it indicates that the predicted value has a low precision, and the predicted value is discarded, and then the sub-optimal value a'opt is sampled according to the preset interval (such as 0.01), and the sampled value is taken as The value of a in the dynamic gray forecasting model, until the accuracy of the predicted value is stable, the value of a at this time is substituted into the dynamic gray forecasting model to determine the predicted value
可选地，若经过步骤“根据所述精度稳定对应的采样值和所述动态灰色预测模型确定所述预测值”后得到的预测值仍然不满足预设精度条件，则选择合适维数且满足残差序列修正的残差序列，采用该残差序列对动态灰色预测模型GM(1,1,a)进行修正，以提高预测值的精度。具体地，可以采用累减还原式的残差修正模型进行修正。设ε(0)(0)为残差修正模型中残差尾段的起始值，可得修正后的动态灰色预测模型GM(1,1,a)如式(6)所示：Optionally, after the step of "determining the prediction value according to the sampling value corresponding to the stability of the accuracy and the dynamic gray prediction model "After the predicted value still does not meet the preset accuracy conditions, select a residual sequence with a suitable dimension and satisfy the residual sequence correction, and use this residual sequence to correct the dynamic gray prediction model GM(1,1,a) , to improve the accuracy of the predicted value. Specifically, the residual correction model of the cumulative reduction formula can be used for correction. Let ε (0) ( 0 ) be the initial value of the residual tail in the residual correction model, and it can be obtained The revised dynamic gray prediction model GM(1,1,a) is shown in formula (6):
同样，残差修正步骤也可重复进行以提高模型的预测精度。Likewise, the residual correction step can be repeated to improve the prediction accuracy of the model.
本实施例建立的动态灰色预测模型能够根据实测数据逐步调整预测，因此短期预测路基变形稳定性精度较高，但是预测周期比较短，因此还需和确定性评估方法相结合来预测较长时间段内路基变形的发展变化。The dynamic gray prediction model established in this embodiment can gradually adjust the prediction according to the measured data, so the accuracy of short-term prediction of subgrade deformation stability is relatively high, but the prediction cycle is relatively short, so it needs to be combined with the deterministic evaluation method to predict a long period of time The development and changes of the deformation of the inner subgrade.
在S103中，根据地温场的控制微分方程和经过所述初步处理的监测数据对路基地温进行确定性预测，根据冻土区变形和应力的控制方程和经过所述初步处理的监测数据对路基变形进行确定性预测；其中所述冻土区变形和应力的控制方程的温度边界条件根据路基地温的不确定性预测结果进行调整。In S103, the subgrade temperature is deterministically predicted according to the control differential equation of the geothermal field and the monitoring data that has undergone the preliminary processing, and the temperature of the subgrade is predicted according to the control equation of deformation and stress in the permafrost region and the monitoring data that has undergone the preliminary processing. The deformation is deterministically predicted; wherein the temperature boundary conditions of the governing equations of deformation and stress in the permafrost region are adjusted according to the uncertain prediction results of the subgrade temperature.
在本实施例中，采用确定性的路基稳定性评估方法来对路基地温和路基变形进行预测评估。地温场的控制微分方程为：In this embodiment, a deterministic roadbed stability evaluation method is used to predict and evaluate roadbed temperature and roadbed deformation. The governing differential equation of the geothermal field is:
其中，λ为路基材料的热传导系数，T为温度，t为时间，x，y为坐标位置。该控制微分方程采用空间域有限元和时间域有限差分法相结合的方法进行地温数值模拟，能够保证路基地温的估计精度。Among them, λ is the thermal conductivity coefficient of the roadbed material, T is the temperature, t is the time, and x and y are the coordinate positions. The control differential equation uses the combination of space domain finite element and time domain finite difference method to carry out the numerical simulation of ground temperature, which can ensure the estimation accuracy of subgrade temperature.
由于目前普遍采用的间接耦合模型无法满足高速铁路路基变形稳定性分析的严格要求，本实施例在考虑相变区附加应力影响的基础上，结合数值模型实际约束情况，建立了热弹性力学及冻土中变形特性系数之间的关系模型，如式(8)所示，进而确定冻土区变形和应力的控制方程，并以地温场条件为基础求解应力场和变形场，实现了地温场和变形场的连续耦合计算，从而保证对路基变形的估计精度。Since the indirect coupling model commonly used at present cannot meet the strict requirements of the deformation stability analysis of high-speed railway embankment, this embodiment considers the influence of additional stress in the phase transition zone and combines the actual constraints of the numerical model to establish thermoelasticity and freezing. The relationship model between the deformation characteristic coefficients in the soil is shown in formula (8), and then the governing equations of deformation and stress in the permafrost region are determined, and the stress field and deformation field are solved based on the geothermal field conditions, and the geothermal field and The continuous coupled calculation of the deformation field ensures the estimation accuracy of the subgrade deformation.
其中，[Tu,Tl]为冻土区变形和应力的控制方程的温度边界条件，可以根据之前步骤对路基地温的不确定性预测结果进行调整，η为冻胀率，μ为模型系数。Among them, [T u , T l ] is the temperature boundary condition of the governing equation of deformation and stress in the permafrost region, which can be adjusted according to the uncertainty prediction results of subgrade temperature in the previous steps, η is the frost heave rate, and μ is the model coefficient.
本实施例的确定性路基稳定性评估方法可全面得到路基地温和力学稳定性(包括所有动力学稳定性)相关指标的分布及变化。可以将确定性路基稳定性评估方法所得的温度场、变形场与对应的实测地温、土应力和变形监测数据进行对比调整。在基于大量实测资料得到的温度边界条件输入后，可实时调整冻土区变形和应力的控制方程，从而较为准确地预测长期沉降变形及路基动应力和动变形的发展变化。The deterministic subgrade stability evaluation method of this embodiment can comprehensively obtain the distribution and changes of related indicators of subgrade temperature and mechanical stability (including all dynamic stability). The temperature field and deformation field obtained by the deterministic subgrade stability assessment method can be compared and adjusted with the corresponding measured ground temperature, soil stress and deformation monitoring data. After inputting temperature boundary conditions based on a large amount of measured data, the control equations of deformation and stress in permafrost regions can be adjusted in real time, so as to more accurately predict the development and changes of long-term settlement deformation and subgrade dynamic stress and dynamic deformation.
在S104中，根据路基地温与路基形变的不确定性预测结果和路基地温与路基形变的确定性预测结果，对路基稳定性进行评估。In S104, the subgrade stability is evaluated according to the uncertain prediction results of subgrade temperature and subgrade deformation and the deterministic prediction results of subgrade temperature and subgrade deformation.
本实施例建立了互联网大数据下远程协调的严寒地区高铁路基稳定性在线交互式的评估方法，以全面评估和预测严寒地区高铁路基的稳定性。确定性和不确定性结合的严寒地区高速铁路路基稳定性在线交互式评估方法建立后，可在以下方面为路基的运营维护提供参考：This example establishes an online interactive evaluation method for the stability of high-speed railway foundations in severe cold regions under the Internet big data, which is remotely coordinated, so as to comprehensively evaluate and predict the stability of high-speed railway foundations in severe cold regions. After the establishment of an online interactive evaluation method for the stability of high-speed railway embankment stability in severe cold regions, it can provide reference for the operation and maintenance of embankment in the following aspects:
1.实时在线全面的路基稳定性分析评估，可及时提供预警；在关键断面验证后，可建立更大范围的模型，进而推广到全线应用，从而为路基的病害防治提供科学依据；1. Real-time online and comprehensive roadbed stability analysis and evaluation can provide timely early warning; after the key section is verified, a wider range of models can be established, and then extended to the entire line of application, thus providing a scientific basis for roadbed disease prevention and control;
2.评估和预测结果可以反馈到监测系统中，调整监测频率，确定需要重点监测的区域及时间段等，达到反馈控制的目的，实现监测系统的精细化管理，在节省监测成本的同时最大化地获得更为有价值的监测信息；2. The evaluation and prediction results can be fed back to the monitoring system, adjust the monitoring frequency, determine the areas and time periods that need to be monitored, etc., to achieve the purpose of feedback control, realize the refined management of the monitoring system, and maximize the monitoring cost while saving to obtain more valuable monitoring information;
3.路基稳定性评估分析结果可以及时反馈到类似项目的施工、设计及监测方案设计等过程中，达到优化设计和施工的目的。3. The evaluation and analysis results of subgrade stability can be fed back to the process of construction, design and monitoring scheme design of similar projects in time to achieve the purpose of optimizing design and construction.
本实施例能够借助于互联网大数据和监测技术的发展，始终动态循环交互式地改进，每循环一次，都使得路基稳定性的评估更为客观全面，预测更为准确，因此必然能够为保证严寒地区高铁路基稳定性起到巨大的促进作用。This embodiment can make use of the Internet big data and the development of monitoring technology, and always improve dynamically and interactively. Each cycle makes the evaluation of the stability of the subgrade more objective and comprehensive, and the prediction is more accurate. The stability of the regional high-speed railway foundation has played a huge role in promoting.
本发明实施例通过根据地温估算模型和经过初步处理的监测数据对路基地温进行不确定性预测，根据动态灰色预测模型和经过初步处理的监测数据对路基变形进行不确定性预测，能够实现不确定评估方法对路基稳定性进行评估；通过根据地温场的控制微分方程和经过初步处理的监测数据对路基地温进行确定性预测，根据冻土区变形和应力的控制方程和经过初步处理的监测数据对路基变形进行确定性预测，能够实现确定性评估方法对路基稳定性进行评估。本发明实施例采用确定性和不确定相结合的路基稳定性评估方法，能够全面评估严寒地区高铁路基的稳定性，提高路基稳定性评估的准确度。The embodiment of the present invention predicts the subgrade temperature with uncertainty based on the ground temperature estimation model and the preliminarily processed monitoring data, and performs uncertainty prediction on the subgrade deformation according to the dynamic gray prediction model and the preliminarily processed monitoring data. Determine the evaluation method to evaluate the stability of the subgrade; through the control differential equation of the geothermal field and the monitoring data that has been preliminarily processed, the temperature of the subgrade is predicted deterministically, and according to the control equation of deformation and stress in the permafrost region and the monitoring data that has been preliminarily processed The data can deterministically predict the deformation of the subgrade, and can realize the deterministic evaluation method to evaluate the stability of the subgrade. The embodiments of the present invention adopt a roadbed stability evaluation method combining certainty and uncertainty, which can comprehensively evaluate the stability of high-speed railway foundations in severe cold regions, and improve the accuracy of roadbed stability evaluation.
应理解，上述实施例中各步骤的序号的大小并不意味着执行顺序的先后，各过程的执行顺序应以其功能和内在逻辑确定，而不应对本发明实施例的实施过程构成任何限定。It should be understood that the sequence numbers of the steps in the above embodiments do not mean the order of execution, and the execution order of each process should be determined by its functions and internal logic, and should not constitute any limitation to the implementation process of the embodiment of the present invention.
对应于上文实施例所述的严寒地区高速铁路路基稳定性在线交互式评估方法，图3示出了本发明实施例提供的严寒地区高速铁路路基稳定性在线交互式评估装置的示意图。为了便于说明，仅示出了与本实施例相关的部分。Corresponding to the online interactive evaluation method for high-speed railway embankment stability in severe cold regions described in the above embodiments, FIG. 3 shows a schematic diagram of an online interactive evaluation device for high-speed railway embankment stability in severe cold regions provided by an embodiment of the present invention. For ease of description, only the parts related to this embodiment are shown.
参照图3，该装置包括获取模块31、不确定性评估模块32、确定性评估模块33和综合评估模块34。Referring to FIG. 3 , the device includes an acquisition module 31 , an uncertainty assessment module 32 , a certainty assessment module 33 and a comprehensive assessment module 34 .
获取模块31，用于通过监测系统获取监测区域的监测数据，并对所述监测数据进行初步处理。The acquiring module 31 is configured to acquire the monitoring data of the monitoring area through the monitoring system, and perform preliminary processing on the monitoring data.
不确定性评估模块32，用于根据地温估算模型和经过所述初步处理的监测数据对路基地温进行不确定性预测，根据动态灰色预测模型和经过所述初步处理的监测数据对路基变形进行不确定性预测。Uncertainty evaluation module 32, used to predict the uncertainty of subgrade temperature according to the ground temperature estimation model and the monitoring data that has undergone the preliminary processing, and predict the deformation of the subgrade according to the dynamic gray prediction model and the monitoring data that has undergone the preliminary processing Uncertain predictions.
确定性评估模块33，用于根据地温场的控制微分方程和经过所述初步处理的监测数据对路基地温进行确定性预测，根据冻土区变形和应力的控制方程和经过所述初步处理的监测数据对路基变形进行确定性预测；其中所述冻土区变形和应力的控制方程的温度边界条件根据路基地温的不确定性预测结果进行调整。The deterministic assessment module 33 is used for deterministically predicting the subgrade temperature according to the control differential equation of the geothermal field and the monitoring data after the preliminary processing, and according to the control equation of deformation and stress in the permafrost region and the primary processing The monitoring data predicts the subgrade deformation deterministically; wherein the temperature boundary conditions of the control equations for deformation and stress in the permafrost region are adjusted according to the uncertain prediction results of subgrade temperature.
综合评估模块34，用于根据路基地温与路基形变的不确定性预测结果和路基地温与路基形变的确定性预测结果，对路基稳定性进行评估。The comprehensive assessment module 34 is used to evaluate the stability of the subgrade according to the uncertain prediction results of subgrade temperature and subgrade deformation and the deterministic prediction results of subgrade temperature and subgrade deformation.
优选地，所述获取模块31用于：Preferably, the acquiring module 31 is used for:
确定稳定性监测指标和测点布设方案，并通过数值模拟确定所述监测区域；Determine the stability monitoring index and measuring point layout plan, and determine the monitoring area through numerical simulation;
根据所述稳定性监测指标和所述测点布设方案建立所述监测系统；Establishing the monitoring system according to the stability monitoring index and the layout scheme of the measuring points;
通过所述监测系统获取所述监测区域的监测数据。Obtain monitoring data of the monitoring area through the monitoring system.
优选地，所述初步处理包括预处理、查询和初步分析；所述预处理包括对所述监测数据的检核、筛选和奇异值检验与插补；所述查询包括数据查询和曲线绘制；所述初步分析包括报警判断和数据分析。Preferably, the preliminary processing includes preprocessing, query and preliminary analysis; the preprocessing includes checking, screening, and singular value inspection and interpolation of the monitoring data; the query includes data query and curve drawing; The above preliminary analysis includes alarm judgment and data analysis.
优选地，所述地温估算模型表示为：Preferably, the ground temperature estimation model is expressed as:
其中，Tm为深度z处的年平均地温，As为地表处温度年变化幅值，z为距离路基表面的深度的绝对值，au为忽略地球内部热流时土体的平均热扩散系数，p为振动周期，为初相角，t为距离起算日期的时间，表示深度z处相位相对于地表的滞后程度。Among them, T m is the annual mean ground temperature at the depth z, A s is the annual variation amplitude of the temperature at the ground surface, z is the absolute value of the depth from the surface of the subgrade, and a u is the average thermal diffusivity of the soil when the heat flow inside the earth is neglected , p is the vibration period, is the initial phase angle, t is the time from the starting date, Indicates how much the phase lags relative to the surface at depth z.
优选地，所述不确定性评估模块32用于：Preferably, the uncertainty assessment module 32 is used for:
将经过所述初步处理的监测数据形成数据序列，对所述数据序列进行级比检验；Forming the monitoring data through the preliminary processing into a data sequence, and performing a grade comparison test on the data sequence;
建立所述动态灰色预测模型，将通过级比检验的数据序列作为所述动态灰色预测模型的参考序列；其中，所述动态灰色预测模型的背景构造值为The dynamic gray prediction model is set up, and the data sequence passed through the scale test is used as the reference sequence of the dynamic gray prediction model; wherein, the background construction value of the dynamic gray prediction model is
z(1)(k)＝ax(1)(k)+(1-a)x(1)(k-1),k＝2,3,…,nz (1) (k)=ax (1) (k)+(1-a)x (1) (k-1),k=2,3,...,n
其中a为构造范围参数，a∈(0,1)；Where a is the construction range parameter, a∈(0,1);
根据x(0)(n)的相对误差Δn(a)、平均相对误差和均方差比值C0(a)确定最佳值aopt；Relative error Δ n (a) according to x (0) (n), average relative error and mean square error ratio C 0 (a) to determine the optimal value a opt ;
根据所述最佳值aopt和所述动态灰色预测模型得到预测值 According to the optimal value a opt and the dynamic gray prediction model to obtain the predicted value
优选地，所述不确定性评估模块32用于：Preferably, the uncertainty assessment module 32 is used for:
判断所述预测值是否满足预设精度条件；judging whether the predicted value satisfies a preset accuracy condition;
若所述预测值不满足预设精度条件，则根据预设间隔对次最佳值a′opt进行采样，依次将采样值作为所述动态灰色预测模型中a的取值，直至求得预设值的精度稳定为止；If the predicted value does not meet the preset accuracy condition, then sample the sub-optimal value a'opt according to the preset interval, and use the sampled value as the value of a in the dynamic gray prediction model in turn until the preset until the precision of the value stabilizes;
根据所述精度稳定对应的采样值和所述动态灰色预测模型确定所述预测值 Determine the prediction value according to the sampling value corresponding to the stability of the accuracy and the dynamic gray prediction model
优选地，所述地温场的控制微分方程为Preferably, the governing differential equation of the geothermal field is
其中，λ为路基材料的热传导系数，T为温度，t为时间，x，y为坐标位置。Among them, λ is the thermal conductivity coefficient of the roadbed material, T is the temperature, t is the time, and x and y are the coordinate positions.
本发明实施例通过根据地温估算模型和经过初步处理的监测数据对路基地温进行不确定性预测，根据动态灰色预测模型和经过初步处理的监测数据对路基变形进行不确定性预测，能够实现不确定评估方法对路基稳定性进行评估；通过根据地温场的控制微分方程和经过初步处理的监测数据对路基地温进行确定性预测，根据冻土区变形和应力的控制方程和经过初步处理的监测数据对路基变形进行确定性预测，能够实现确定性评估方法对路基稳定性进行评估。本发明实施例采用确定性和不确定相结合的路基稳定性评估方法，能够全面评估严寒地区高铁路基的稳定性，提高路基稳定性评估的准确度。The embodiment of the present invention predicts the subgrade temperature with uncertainty based on the ground temperature estimation model and the preliminarily processed monitoring data, and performs uncertainty prediction on the subgrade deformation according to the dynamic gray prediction model and the preliminarily processed monitoring data. Determine the evaluation method to evaluate the stability of the subgrade; through the control differential equation of the geothermal field and the monitoring data that has been preliminarily processed, the temperature of the subgrade is predicted deterministically, and according to the control equation of deformation and stress in the permafrost region and the monitoring data that has been preliminarily processed The data can deterministically predict the deformation of the subgrade, and can realize the deterministic evaluation method to evaluate the stability of the subgrade. The embodiments of the present invention adopt a roadbed stability evaluation method combining certainty and uncertainty, which can comprehensively evaluate the stability of high-speed railway foundations in severe cold regions, and improve the accuracy of roadbed stability evaluation.
所属领域的技术人员可以清楚地了解到，为了描述的方便和简洁，仅以上述各功能单元、模块的划分进行举例说明，实际应用中，可以根据需要而将上述功能分配由不同的功能单元、模块完成，即将所述装置的内部结构划分成不同的功能单元或模块，以完成以上描述的全部或者部分功能。实施例中的各功能单元、模块可以集成在一个处理单元中，也可以是各个单元单独物理存在，也可以两个或两个以上单元集成在一个单元中，上述集成的单元既可以采用硬件的形式实现，也可以采用软件功能单元的形式实现。另外，各功能单元、模块的具体名称也只是为了便于相互区分，并不用于限制本申请的保护范围。上述系统中单元、模块的具体工作过程，可以参考前述方法实施例中的对应过程，在此不再赘述。Those skilled in the art can clearly understand that for the convenience and brevity of description, only the division of the above-mentioned functional units and modules is used for illustration. In practical applications, the above-mentioned functions can be assigned to different functional units, Completion of modules means that the internal structure of the device is divided into different functional units or modules to complete all or part of the functions described above. Each functional unit and module in the embodiment may be integrated into one processing unit, or each unit may exist separately physically, or two or more units may be integrated into one unit, and the above-mentioned integrated units may adopt hardware It can also be implemented in the form of software functional units. In addition, the specific names of the functional units and modules are only for the convenience of distinguishing each other, and are not used to limit the protection scope of the present application. For the specific working process of the units and modules in the above system, reference may be made to the corresponding process in the foregoing method embodiments, and details will not be repeated here.
在上述实施例中，对各个实施例的描述都各有侧重，某个实施例中没有详述或记载的部分，可以参见其它实施例的相关描述。In the above-mentioned embodiments, the descriptions of each embodiment have their own emphases, and for parts that are not detailed or recorded in a certain embodiment, refer to the relevant descriptions of other embodiments.
本领域普通技术人员可以意识到，结合本文中所公开的实施例描述的各示例的单元及算法步骤，能够以电子硬件、或者计算机软件和电子硬件的结合来实现。这些功能究竟以硬件还是软件方式来执行，取决于技术方案的特定应用和设计约束条件。专业技术人员可以对每个特定的应用来使用不同方法来实现所描述的功能，但是这种实现不应认为超出本发明的范围。Those skilled in the art can appreciate that the units and algorithm steps of the examples described in conjunction with the embodiments disclosed herein can be implemented by electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are executed by hardware or software depends on the specific application and design constraints of the technical solution. Those skilled in the art may use different methods to implement the described functions for each specific application, but such implementation should not be regarded as exceeding the scope of the present invention.
以上所述实施例仅用以说明本发明的技术方案，而非对其限制；尽管参照前述实施例对本发明进行了详细的说明，本领域的普通技术人员应当理解：其依然可以对前述各实施例所记载的技术方案进行修改，或者对其中部分技术特征进行等同替换；而这些修改或者替换，并不使相应技术方案的本质脱离本发明各实施例技术方案的精神和范围，均应包含在本发明的保护范围之内。The above-described embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit them; although the present invention has been described in detail with reference to the foregoing embodiments, those of ordinary skill in the art should understand that: it can still carry out the foregoing embodiments Modifications to the technical solutions recorded in the examples, or equivalent replacement of some of the technical features; and these modifications or replacements do not make the essence of the corresponding technical solutions deviate from the spirit and scope of the technical solutions of the various embodiments of the present invention, and should be included in within the protection scope of the present invention.
Claims (8)
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201810012451.5A CN108446417A (en) | 2018-01-05 | 2018-01-05 | Severe cold area high-speed railway subgrade stability online interaction formula appraisal procedure and device |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201810012451.5A CN108446417A (en) | 2018-01-05 | 2018-01-05 | Severe cold area high-speed railway subgrade stability online interaction formula appraisal procedure and device |
Publications (1)
| Publication Number | Publication Date |
|---|---|
| CN108446417A true CN108446417A (en) | 2018-08-24 |
Family
ID=63190793
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN201810012451.5A Pending CN108446417A (en) | 2018-01-05 | 2018-01-05 | Severe cold area high-speed railway subgrade stability online interaction formula appraisal procedure and device |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN108446417A (en) |
Cited By (9)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN111254904A (en) * | 2020-01-30 | 2020-06-09 | 华东交通大学 | A system for detecting and predicting subgrade deformation in railway permafrost regions |
| CN111395296A (en) * | 2020-02-25 | 2020-07-10 | 石家庄铁道大学 | Evaluation method and device for temperature difference of subgrade in frozen soil area |
| CN112528507A (en) * | 2020-12-17 | 2021-03-19 | 四川九洲北斗导航与位置服务有限公司 | Terrain structure stability prediction method and device, electronic equipment and storage medium |
| CN112965378A (en) * | 2021-02-05 | 2021-06-15 | 中山大学 | High-speed railway foundation deformation self-adaptive control system and method |
| CN113338255A (en) * | 2021-05-18 | 2021-09-03 | 中国铁道科学研究院集团有限公司铁道建筑研究所 | Intelligent early warning system and method for service performance of transition section of railway road bridge in cold region |
| CN114117615A (en) * | 2021-12-02 | 2022-03-01 | 中国科学院武汉岩土力学研究所 | Performance determination method, device and processing equipment of roadbed of expressway section |
| CN114462126A (en) * | 2022-01-20 | 2022-05-10 | 西南交通大学 | Method for determining load distribution mode of train on roadbed at position of ballastless track structure seam |
| CN117574493A (en) * | 2023-11-13 | 2024-02-20 | 中国公路工程咨询集团有限公司 | Highway frozen soil range deformation identification method and system in permafrost region |
| CN117744876A (en) * | 2023-12-26 | 2024-03-22 | 中国公路工程咨询集团有限公司 | Method and system for predicting gradient information of frozen soil foundation of highway in long-term operation |
Citations (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN105868556A (en) * | 2016-03-29 | 2016-08-17 | 武汉福天通科技有限公司 | Bridge safety assessment method based on Beidou bridge safety monitoring system |
-
2018
- 2018-01-05 CN CN201810012451.5A patent/CN108446417A/en active Pending
Patent Citations (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN105868556A (en) * | 2016-03-29 | 2016-08-17 | 武汉福天通科技有限公司 | Bridge safety assessment method based on Beidou bridge safety monitoring system |
Non-Patent Citations (2)
| Title |
|---|
| 张玉芝: "深季节性冻土地区高速铁路路基稳定性研究", 《中国博士学位论文全文数据库》 * |
| 张玉芝等: "改进的动态灰色模型在高铁路基变形预测中的应用", 《铁道科学与工程学报》 * |
Cited By (16)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN111254904B (en) * | 2020-01-30 | 2021-05-18 | 华东交通大学 | A system for detecting and predicting subgrade deformation in railway permafrost regions |
| CN111254904A (en) * | 2020-01-30 | 2020-06-09 | 华东交通大学 | A system for detecting and predicting subgrade deformation in railway permafrost regions |
| CN111395296A (en) * | 2020-02-25 | 2020-07-10 | 石家庄铁道大学 | Evaluation method and device for temperature difference of subgrade in frozen soil area |
| CN112528507A (en) * | 2020-12-17 | 2021-03-19 | 四川九洲北斗导航与位置服务有限公司 | Terrain structure stability prediction method and device, electronic equipment and storage medium |
| CN112528507B (en) * | 2020-12-17 | 2022-10-28 | 四川九洲北斗导航与位置服务有限公司 | Terrain structure stability prediction method and device, electronic equipment and storage medium |
| CN112965378A (en) * | 2021-02-05 | 2021-06-15 | 中山大学 | High-speed railway foundation deformation self-adaptive control system and method |
| CN112965378B (en) * | 2021-02-05 | 2022-02-11 | 中山大学 | Adaptive control system and method for high-speed railway foundation deformation |
| CN113338255B (en) * | 2021-05-18 | 2022-10-18 | 中国铁道科学研究院集团有限公司铁道建筑研究所 | Intelligent early warning system for service performance of transition section of railway road bridge in cold region |
| CN113338255A (en) * | 2021-05-18 | 2021-09-03 | 中国铁道科学研究院集团有限公司铁道建筑研究所 | Intelligent early warning system and method for service performance of transition section of railway road bridge in cold region |
| US11847383B2 (en) | 2021-12-02 | 2023-12-19 | Institute Of Rock And Soil Mechanics, Chinese Academy Of Sciences | Method for determining a performance of an unsaturated subgrade of a multi-layered expressway section and processing device thereof |
| CN114117615B (en) * | 2021-12-02 | 2022-09-20 | 中国科学院武汉岩土力学研究所 | Method and device for determining performance of roadbed of highway section and processing equipment |
| CN114117615A (en) * | 2021-12-02 | 2022-03-01 | 中国科学院武汉岩土力学研究所 | Performance determination method, device and processing equipment of roadbed of expressway section |
| CN114462126A (en) * | 2022-01-20 | 2022-05-10 | 西南交通大学 | Method for determining load distribution mode of train on roadbed at position of ballastless track structure seam |
| CN117574493A (en) * | 2023-11-13 | 2024-02-20 | 中国公路工程咨询集团有限公司 | Highway frozen soil range deformation identification method and system in permafrost region |
| CN117574493B (en) * | 2023-11-13 | 2024-05-28 | 中国公路工程咨询集团有限公司 | Highway frozen soil range deformation identification method and system in permafrost region |
| CN117744876A (en) * | 2023-12-26 | 2024-03-22 | 中国公路工程咨询集团有限公司 | Method and system for predicting gradient information of frozen soil foundation of highway in long-term operation |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN108446417A (en) | Severe cold area high-speed railway subgrade stability online interaction formula appraisal procedure and device | |
| CN107038524B (en) | Comprehensive evaluation method for construction quality of roller compacted concrete dam considering parameter uncertainty | |
| Li et al. | Time series prediction of mining subsidence based on a SVM | |
| CN108153989B (en) | Rapid prediction method of compaction quality of concrete dam considering the influence of parameter uncertainty | |
| CN111157346B (en) | Analysis system and method for design and correction of water curtain system in underground oil storage reservoir | |
| CN115115123A (en) | Foundation settlement prediction system and settlement prediction method | |
| Wei et al. | Modeling method for predicting seepage of RCC dams considering time‐varying and lag effect | |
| CN110705178A (en) | A dynamic prediction method for surrounding rock deformation in the whole process of tunnel/subway construction based on machine learning | |
| Liang et al. | An sand plug of fracturing intelligent early warning model embedded in remote monitoring system | |
| CN113065267A (en) | Tunnel crack detection and safety evaluation system | |
| Chen et al. | Probabilistic performance prediction of shield tunnels in operation through data mining | |
| Jalali et al. | Geostatistical evaluation of spatial variation related to groundwater quality database: case study for Arak plain aquifer, Iran | |
| CN110344327B (en) | A real-time elevation calculation method for CPIII point of track control network on cable-stayed bridge | |
| AU2021102395A4 (en) | BIM (Building Information Modeling) Parametric Modeling and Augmented Reality Mobile Inspection Method for Pavement Distresses | |
| Li et al. | Thermal contact resistance of granite joints under normal stress | |
| CN109190291A (en) | The method for obtaining dynamic sounding blow counts correction factor | |
| CN113609198B (en) | Method for reconstructing concrete temperature field in pouring warehouse based on optical fiber measured temperature data | |
| CN119047343B (en) | Wellbore stability control method, device, equipment and storage medium for dynamically adjusting drilling fluid ratio | |
| CN102567606B (en) | Method for drawing equal-water-pressure surface of water supply pipeline network in real time | |
| CN119918347A (en) | Confidence analysis method for foundation settlement prediction based on preloading stratification conditions | |
| CN118095020B (en) | A construction method for a porous flood diversion gate | |
| CN103606112A (en) | Power plant operation performance determination method based on multilevel gray evaluation | |
| CN114818221A (en) | Pipe network operation situation analysis method based on pipe network hydrodynamic model and data driving model | |
| CN110083933B (en) | Bayesian degradation analysis method for corrosion pipeline considering random effect | |
| Mubaraki | A methodology for project-level maintenance for urban roads |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination |