CN111445165A - Tunnel structure health monitoring online grading early warning evaluation method - Google Patents
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Abstract
The invention discloses an online grading early warning and evaluating method for tunnel structure health monitoring, which utilizes automatically monitored high-frequency data to realize real-time evaluation and grading early warning of tunnel structure safety through an informatization system and provides decision suggestions for timely knowing the tunnel structure safety condition, preventive maintenance and major and medium maintenance. The invention combines the intelligent monitoring technology of the tunnel structure health, realizes the online grading early warning based on the multi-sensor, multi-index and multi-level data fusion, and obviously reduces the possibility of false alarm or false alarm leakage; the method is developed into a software system which is convenient to operate and good in interactivity, online real-time evaluation and early warning can be achieved, the analysis response efficiency and the intelligent degree of the tunnel health monitoring system are improved, the manual workload is reduced, and the cost is reduced for later operation of the tunnel. Therefore, the evaluation method is high in feasibility, simple in programming and capable of being updated in real time, and the later-stage healthy operation condition of the tunnel can be guaranteed.
Description
Technical Field
The invention belongs to the technical field of tunnel structure safety monitoring engineering, and particularly relates to an online grading early warning evaluation method for tunnel structure health monitoring.
Background
The tunnel structure health monitoring means that a sensor is mounted at a key position of a tunnel, long-term online monitoring is carried out on various mechanical responses of the tunnel structure in an operation period, real-time evaluation and early warning on the health condition of the tunnel structure are achieved, and guidance is provided for safe operation, maintenance, management and maintenance work of the tunnel. How to realize the real-time evaluation and intelligent automatic early warning of the health condition of the tunnel structure by means of a large amount of monitoring data acquired by a structural health monitoring system is a critical problem to be solved urgently, related researches are still insufficient at present, and corresponding standard specifications do not exist at home and abroad.
The existing tunnel health monitoring system usually adopts a single sensor or a single evaluation index to evaluate and early warn, and due to factors such as the stability of the sensor, the interference of the external environment and the like, false alarm or false alarm omission can be avoided. In view of the limitation of early warning by using a single index, in order to effectively solve the problem, real-time comprehensive evaluation and early warning based on multi-index data fusion are required to be realized.
The existing research mainly comprises an efficacy coefficient method of Liming, Chen Wei, Yang Jian Ping and the like, early warning research of a tunnel structure health monitoring system based on the efficacy coefficient method of J rock mechanics 2015,36(S2), 729-. The efficacy coefficient method and the distance discrimination method are to forcibly endow the data of a plurality of sensors with various monitoring indexes with efficacy coefficients or forcibly carry out normalization processing, and can realize data fusion of any plurality of sensors in a form, but the shortcomings are obvious, the method cannot classify and process the sensors with various monitoring indexes, forcibly mixes various structural responses into one, cannot reflect main influence factors of the health condition of the tunnel structure, and cannot deeply evaluate the health condition of the tunnel structure from different dimensions such as internal force, deformation, durability and the like.
In view of the above, it is necessary to provide a new real-time evaluation and early warning method: the method can overcome the problem that false alarm or false alarm is easy to occur in single sensor or single evaluation index early warning, and can overcome the defects of forced fusion of data, no primary and secondary separation and incapability of deep evaluation in the existing online real-time comprehensive evaluation and early warning methods such as an efficacy coefficient method and a distance discrimination method.
Disclosure of Invention
In view of the above problems, the invention provides an online grading early warning and evaluation method for tunnel structure health monitoring, which utilizes automatically monitored high-frequency data to realize multi-index, multi-level and multi-dimensional real-time evaluation and grading early warning of 'single sensor → single evaluation index → multiple evaluation indexes → … → whole section/whole section' through an informatization system, can better reflect the degree of influence of various factors on the tunnel structure health condition through selection of reasonable weight and membership function, really realizes scientific and organic fusion analysis of a large amount of monitoring data of multiple-index and multiple-sensor, and provides scientific and reasonable decision suggestions for timely knowing, preventive maintenance and major and middle repair of the tunnel structure safety condition.
A tunnel structure health monitoring online grading early warning evaluation method comprises the following steps:
(1) determining a health condition evaluation index system of a shield tunnel structure, and dividing the system into a plurality of layers according to actual conditions;
(2) determining an early warning interval and an early warning grade of the system bottommost evaluation index;
(3) establishing an early warning level fuzzy relation equation of each level evaluation index;
(4) determining the weight of each evaluation index of each layer;
(5) and calculating the membership degree of each evaluation index to each early warning grade layer by layer from the bottommost layer until the membership degree of the health condition of the shield tunnel structure to each early warning grade is obtained through calculation according to the early warning grade fuzzy relation equation and the weight of each evaluation index, and taking the early warning grade with the highest membership degree as a decision suggestion for the safety evaluation of the tunnel structure and carrying out online real-time early warning.
Further, after determining an evaluation index system of the health condition of the shield tunnel structure, the step (1) carries out first-level division on the evaluation index system, namely dividing the health condition of the shield tunnel structure into a plurality of evaluation indexes for representation; and then, carrying out second-level division on the evaluation index system, namely, further dividing the evaluation index of the first level into a plurality of subclasses of evaluation indexes, and so on until the evaluation index system is divided into a plurality of levels.
Further, the early warning levels in the step (2) are divided into four levels of green, blue, orange and red, the four levels respectively correspond to four groups of early warning intervals, the four groups of early warning intervals are determined according to existing engineering specifications and by combining actual engineering conditions, and the green interval represents that the corresponding evaluation index is in a safety range; the blue interval represents that the corresponding evaluation index is larger and needs to draw attention; the orange interval represents that the corresponding evaluation index is large and is not ignored, and needs to pay close attention; the red interval represents that the corresponding evaluation index reaches the limit, and relevant experts must be organized for on-site investigation and processing.
Further, in the step (3), for any evaluation index X in the current level, if the evaluation index X is subdivided into n evaluation indexes, the early warning level fuzzy relation equation expression of the level evaluation index X is as follows:
wherein: a isiWeight, l, representing the i-th evaluation index of n evaluation indexes into which the evaluation index X is subdividedi1～li4Respectively the membership degrees of the ith evaluation index to four grades of green, blue, orange and red, b1～b4The evaluation indexes are the membership degrees of the evaluation index X to four levels of green, blue, orange and red respectively, i is a natural number and is more than or equal to 1 and less than or equal to n.
Further, in the step (4), a three-scale method or a nine-scale method is adopted to determine the weight of each evaluation index of each level.
Further, in the step (5), the membership degree of each evaluation index to each early warning level is calculated layer by layer from the bottommost layer, and monitoring values of each evaluation index at the bottommost layer are acquired in real time through a sensor; for any evaluation index at the bottommost layer, if the current monitoring value of the evaluation index is x, determining the membership degree of the evaluation index to each early warning level according to the following criteria:
if the early warning interval corresponding to the early warning level is [0, c ]1]If the lowest evaluation index has the membership degree f (x) to the early warning level, the membership degree f (x) is:
if the early warning interval corresponding to the early warning level is [ c ]1,c2]And c is1c2If the evaluation index is less than 0, the membership degree f (x) of the lowest evaluation index to the early warning grade is as follows:
if the early warning interval corresponding to the early warning level is [ c ]1,c2]And c is1c2If the evaluation index is more than 0, the membership degree f (x) of the lowest evaluation index to the early warning grade is as follows:
if the early warning interval corresponding to the early warning level is [ c ]1,c2]&[c3,c4]And c is3＜c4＜0＜c1＜c2If the lowest evaluation index has the membership degree f (x) to the early warning level, the membership degree f (x) is:
if the early warning interval corresponding to the early warning level is [ c ]1, + ∞), the degree of membership f (x) of the lowest evaluation index to the warning level is:
if the early warning interval corresponding to the early warning level is (— infinity, c)1]&[c2, + ∞) and c1c2If the evaluation index is less than 0, the membership degree f (x) of the lowest evaluation index to the early warning grade is as follows:
wherein: c. C1、c2、c3、c4F () is a boolean function, i.e., when the relation in () is satisfied, the function value is 1, otherwise, the function value is 0.
Further, when a certain bottommost evaluation index is monitored by a plurality of sensors, the membership degree f (x) of the evaluation index to each early warning level is as follows:
wherein: m is the number of sensors, x1,x2,…,xmRespectively, the monitoring values collected by the m sensors.
The invention combines the intelligent monitoring technology of the tunnel structure health, realizes the online grading early warning based on the multi-sensor, multi-index and multi-level data fusion, and obviously reduces the possibility of false alarm or false alarm leakage. The method is developed into a software system which is convenient to operate and good in interactivity, online real-time evaluation and early warning can be achieved, the analysis response efficiency and the intelligent degree of the tunnel health monitoring system are improved, the manual workload is reduced, and the cost is reduced for later operation of the tunnel. Therefore, the evaluation method has the advantages of high feasibility, simple programming, small calculation amount and real-time updating, and can ensure the later healthy operation condition of the tunnel.
Drawings
FIG. 1 is a schematic diagram of a hierarchical model tree of a tunnel structure health evaluation index system according to the present invention.
FIG. 2(a) shows the warning interval [0, x ]1]And (5) a corresponding membership function schematic diagram.
FIG. 2(b) shows an early warning interval [ x ]1,x2](x1x2<0) And (5) a corresponding membership function schematic diagram.
FIG. 2(c) shows the warning interval [ x ]1,x2](x1x2>0) And (5) a corresponding membership function schematic diagram.
FIG. 2(d) shows the warning interval [ x ]1,x2]&[x3,x4]And (5) a corresponding membership function schematic diagram.
FIG. 2(e) is the warning interval [ x ]1, + ∞) corresponding membership function.
FIG. 2(f) shows the warning interval (-infinity, x)1]&[x2, + ∞) corresponding membership function.
Fig. 3(a) is a schematic layout of the buried sensor at section a.
Fig. 3(b) is a schematic layout of a surface sensor of section a.
Fig. 3(c) is a schematic layout diagram of monitoring points of the static level gauge in the tunnel monitoring section.
Fig. 4(a) is a schematic view of seam opening monitoring data.
Fig. 4(b) is a schematic diagram of dome deformation monitoring data.
Fig. 4(c) is a schematic diagram of segment tilt deflection monitoring data.
Fig. 4(d) is a graph showing longitudinal relative differential settlement monitoring data.
Fig. 4(e) is a schematic diagram of concrete strain monitoring data.
Fig. 4(f) is a schematic diagram of the monitoring data of the internal force of the steel bar.
Fig. 4(g) is a schematic view of the strain monitoring data of the bottom surface of the lane plate.
Fig. 5 is a schematic diagram of a hierarchical model tree of a tunnel structure health condition evaluation index system in the embodiment of the present invention.
Fig. 6 is a schematic diagram of a grading pre-warning result at a certain time in the embodiment of the present invention.
FIG. 7 is a schematic diagram of the results of real-time evaluation of the overall safety of the structure of section A over a period of time.
Detailed Description
In order to more specifically describe the present invention, the following detailed description is provided for the technical solution of the present invention with reference to the accompanying drawings and the specific embodiments.
The invention relates to an online grading early warning evaluation method for tunnel structure health monitoring, which comprises the following steps:
step 1: and determining a health condition evaluation index system of the shield tunnel structure, classifying and dividing each index into a plurality of layers according to actual conditions.
As shown in fig. 1, determining a health condition evaluation index system of a shield tunnel structure, dividing the health condition evaluation index system of the shield tunnel structure into a, b, a1、a2、...、ar，b1、b2、...、bs，......，n1、n2、...、ntAnd repeating the steps until the shield tunnel structure health condition evaluation index system is divided into S levels.
Step 2: and determining the early warning interval and the early warning grade of the bottommost evaluation index.
2.1 the early warning level is divided into four intervals of green, blue, orange and red, the interval is green when the internal force deformation of the specified structure does not exceed the blue early warning value, the interval is blue when the internal force deformation exceeds the blue early warning value and does not exceed the orange early warning value, the interval is orange when the internal force deformation exceeds the orange early warning value and does not exceed the red early warning value, and the interval is red when the internal force deformation exceeds the red early warning value. The green interval represents that the internal force deformation of the structure is in a safe range; the blue interval represents that the internal force of the structure is deformed greatly and needs to draw attention; the orange interval represents that the deformation of the structure and force is not ignored and needs to pay close attention; the red interval represents that the internal force deformation of the structure reaches the bearing capacity limit or the use limit of the structure, and relevant experts must be organized for on-site investigation and processing.
2.2 on the basis of referring to the existing relevant specifications or relevant researches, firstly determining the specific numerical value of the blue early warning value or the red early warning value, and then specifying the proportional relation among the early warning values of all levels according to the conditions, wherein the blue early warning value can be specified as a% of the red early warning value, and the orange early warning value can be specified as b% of the red early warning value (wherein 0 < a < b < 100).
And step 3: and establishing a fuzzy relation equation set for determining the early warning level of each level of evaluation indexes.
And (3) passing the membership degree of all the lower-layer indexes to each early warning level through a fuzzy relation equation:
and calculating and converting the membership degree of the index of the upper layer to each early warning level, and ascending layer by layer to finally obtain the membership degree of the health condition of the whole tunnel structure to each early warning level. Wherein a isiNormalized weights for respective factors,/ij、bijThe membership degree of the ith factor to the jth early warning grade is fuzzy operator (the most common weighted average operator is adopted in the invention), and l1jAnd b1Corresponding to the green warning level,/2jAnd b2Corresponding to the blue warning level,/3jAnd b3Corresponding to orange early warning level,/4jAnd b4Corresponding to the red early warning level.
And 4, step 4: the weight of each index of each layer is determined by a three-scale method.
4.1 determining the relative importance of a pointer to a decision target in a three-scale method can be expressed as:
4.2 two-by-two comparison matrix C can be generated according to specific problems:
4.3 calculating an optimal transfer matrix O based on the pairwise comparison matrix C:
wherein:
4.4 converting the optimal transfer matrix into a consistency judgment matrix A:
wherein: a isij＝exp(oij) Due to the fact that
Therefore cij＝cimcmjThe consistency check is not needed according to the definition matrix A of the consistency matrix.
After the consistency judgment matrix is obtained, the characteristic value and the corresponding characteristic vector can be obtained, and the characteristic vector corresponding to the maximum characteristic value is normalized, so that the weight of each index pair decision target can be obtained.
And 5: and constructing a membership function aiming at each evaluation index of the bottommost layer, carrying out weighted average fusion on a plurality of monitoring data of each evaluation index of the bottommost layer in one section, and substituting the weighted average fusion value into the membership function to calculate the membership of the corresponding evaluation index to each early warning level.
The membership function of the invention is constructed into a normal distribution type, and the following results are obtained:
wherein: (x) the membership degree of a structural response value of a certain evaluation index to a specified early warning level is x, and when the monitoring value x of a single sensor is known, f (x) the membership degree of the evaluation index at the measuring point of the sensor to the certain early warning level is f; f (r) is a boolean function, if r is true, f (r) is 1; otherwise, f (r) is 0. When determining the membership degree, the form of the early warning interval is considered first, and then the corresponding function calculation formula is determined, wherein the form of the function image of each early warning interval is shown in fig. 2(a) to 2 (f).
The weighted average fusion value calculation method for the multiple sensors has the following formula:
step 6: the algorithm is implanted into automatic monitoring software, online multilevel hierarchical early warning is achieved, and potential safety hazards of a tunnel structure are found in time.
In the following embodiments, the sensor arrangement of a certain tunnel section a is shown in fig. 3(a) -3 (c), where fig. 3(a) is an installation diagram of embedded sensors; FIG. 3(b) is a surface sensor mounting diagram, i.e., section A has 72 sensors mounted; fig. 3(c) is a sensor layout diagram of a longitudinal differential settlement monitoring section spanning a section a, which comprises 9 fiber bragg grating static level meters, and measuring points are arranged at intervals of 20 meters. Fig. 4(a) to 4(g) show sensor monitoring data of all sensors in section a over a certain period of time.
And performing online grading early warning evaluation on the structural health condition of the section based on the sensor monitoring data of the section A.
Step a: the health condition of the tunnel structure is defined into two levels, the first level is structural deformation and structural internal force, the structural deformation is subdivided into seam opening, longitudinal uneven settlement, vault convergence and inclined deflection of duct pieces in the second level, the structural internal force is subdivided into steel bar internal force, concrete strain and lane plate strain, and the evaluation index system level model tree shown in figure 5 is formed.
Step b: referring to the relevant specifications and relevant scientific achievements, the early warning grade and early warning interval division result of each specific index in the embodiment are determined, as shown in table 1.
TABLE 1
Step c: according to the hierarchical model tree in fig. 5, establishing a fuzzy relation equation set for calculating the early warning level of each hierarchical evaluation index:
step d: and determining the weight of each index in the hierarchical model tree by adopting a three-scale method.
For the first level, the importance degree of the structure deformation is larger than the internal force of the structure, and the weights of the structure deformation are respectively 0.73 and 0.27 by adopting a three-scale method. For the second level, the respective weights of the four indexes under the structural deformation are 0.45, 0.28, 0.17 and 0.1 in sequence; the weights of the three indexes under the internal force of the structure are 0.56, 0.29 and 0.15 in sequence.
Step e: and calculating the membership of each level evaluation index to each early warning level according to the membership function form corresponding to the graphs in the figures 2(a) to 2 (f). Firstly, carrying out weighted average fusion on a plurality of monitoring data of each evaluation index at the bottommost layer in a section, substituting a weighted average fusion value into a membership function to calculate the membership of the corresponding evaluation index to each early warning grade; and substituting the calculated membership degrees at the bottom layer into a fuzzy relation equation set, and substituting the calculated membership degrees layer by layer upwards to obtain the membership degrees of all the evaluation indexes to each early warning level, wherein a grading early warning result at the moment of finishing at 12 noon in 16 th of month 9 in the embodiment is shown in fig. 6.
Step f: the above algorithm is implanted into the automatic monitoring software, and the continuous real-time evaluation result of the overall safety of the section A structure within a period of time is obtained through calculation, as shown in fig. 7.
The embodiments described above are presented to enable a person having ordinary skill in the art to make and use the invention. It will be readily apparent to those skilled in the art that various modifications to the above-described embodiments may be made, and the generic principles defined herein may be applied to other embodiments without the use of inventive faculty. Therefore, the present invention is not limited to the above embodiments, and those skilled in the art should make improvements and modifications to the present invention based on the disclosure of the present invention within the protection scope of the present invention.
Claims (8)
1. A tunnel structure health monitoring online grading early warning evaluation method comprises the following steps:
(1) determining a health condition evaluation index system of a shield tunnel structure, and dividing the system into a plurality of layers according to actual conditions;
(2) determining an early warning interval and an early warning grade of the system bottommost evaluation index;
(3) establishing an early warning level fuzzy relation equation of each level evaluation index;
(4) determining the weight of each evaluation index of each layer;
(5) and calculating the membership degree of each evaluation index to each early warning grade layer by layer from the bottommost layer until the membership degree of the health condition of the shield tunnel structure to each early warning grade is obtained through calculation according to the early warning grade fuzzy relation equation and the weight of each evaluation index, and taking the early warning grade with the highest membership degree as a decision suggestion for the safety evaluation of the tunnel structure and carrying out online real-time early warning.
2. The tunnel structure health monitoring online grading early warning evaluation method according to claim 1, characterized in that: after determining an evaluation index system of the health condition of the shield tunnel structure, dividing the evaluation index system for the first level, namely dividing the health condition of the shield tunnel structure into a plurality of evaluation indexes for representing; and then, carrying out second-level division on the evaluation index system, namely, further dividing the evaluation index of the first level into a plurality of subclasses of evaluation indexes, and so on until the evaluation index system is divided into a plurality of levels.
3. The tunnel structure health monitoring online grading early warning evaluation method according to claim 1, characterized in that: the early warning grades in the step (2) are divided into four grades of green, blue, orange and red, the four grades respectively correspond to four groups of early warning intervals, the four groups of early warning intervals are determined according to existing engineering specifications and by combining actual engineering conditions, and the green interval represents that corresponding evaluation indexes are in a safety range; the blue interval represents that the corresponding evaluation index is larger and needs to draw attention; the orange interval represents that the corresponding evaluation index is large and is not ignored, and needs to pay close attention; the red interval represents that the corresponding evaluation index reaches the limit, and relevant experts must be organized for on-site investigation and processing.
4. The tunnel structure health monitoring online grading early warning evaluation method according to claim 1, characterized in that: in the step (3), for any evaluation index X in the current level, if the evaluation index X is subdivided into n evaluation indexes, the early warning level fuzzy relation equation expression of the level evaluation index X is as follows:
wherein: a isiWeight, l, representing the i-th evaluation index of n evaluation indexes into which the evaluation index X is subdividedi1～li4Respectively the membership degrees of the ith evaluation index to four grades of green, blue, orange and red, b1～b4The evaluation indexes are the membership degrees of the evaluation index X to four levels of green, blue, orange and red respectively, i is a natural number and is more than or equal to 1 and less than or equal to n.
5. The tunnel structure health monitoring online grading early warning evaluation method according to claim 1, characterized in that: and (4) determining the weight of each evaluation index of each layer by adopting a three-scale method or a nine-scale method.
6. The tunnel structure health monitoring online grading early warning evaluation method according to claim 1, characterized in that: calculating the membership degree of each evaluation index to each early warning grade layer by layer from the bottommost layer in the step (5), and acquiring the monitoring value of each evaluation index of the bottommost layer in real time through a sensor; for any evaluation index at the bottommost layer, if the current monitoring value of the evaluation index is x, determining the membership degree of the evaluation index to each early warning level according to the following criteria:
if the pre-warning level corresponds toAlarm interval is [0, c1]If the lowest evaluation index has the membership degree f (x) to the early warning level, the membership degree f (x) is:
if the early warning interval corresponding to the early warning level is [ c ]1,c2]And c is1c2If the evaluation index is less than 0, the membership degree f (x) of the lowest evaluation index to the early warning grade is as follows:
if the early warning interval corresponding to the early warning level is [ c ]1,c2]And c is1c2If the evaluation index is more than 0, the membership degree f (x) of the lowest evaluation index to the early warning grade is as follows:
if the early warning interval corresponding to the early warning level is [ c ]1,c2]&[c3,c4]And c is3＜c4＜0＜c1＜c2If the lowest evaluation index has the membership degree f (x) to the early warning level, the membership degree f (x) is:
if the early warning interval corresponding to the early warning level is [ c ]1, + ∞), the degree of membership f (x) of the lowest evaluation index to the warning level is:
if the early warning interval corresponding to the early warning level is (— infinity, c)1]&[c2, + ∞) and c1c2If the evaluation index is less than 0, the membership degree f (x) of the lowest evaluation index to the early warning grade is as follows:
wherein: c. C1、c2、c3、c4F () is a boolean function, i.e., when the relation in () is satisfied, the function value is 1, otherwise, the function value is 0.
7. The tunnel structure health monitoring online grading early warning evaluation method according to claim 6, characterized in that: when a plurality of sensors acquire monitoring values of a certain bottommost evaluation index, the membership degree f (x) of the evaluation index to each early warning level is as follows:
wherein: m is the number of sensors, x1,x2,…,xmRespectively, the monitoring values collected by the m sensors.
8. The tunnel structure health monitoring online grading early warning evaluation method according to claim 1, characterized in that: the method combines the intelligent monitoring technology of tunnel structure health, realizes online grading early warning based on multi-sensor, multi-index and multi-level data fusion, and obviously reduces the possibility of false alarm or false alarm leakage.
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Cited By (5)
| Publication number | Priority date | Publication date | Assignee | Title |
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| CN112036734A (en) * | 2020-08-27 | 2020-12-04 | 同济大学 | Tunnel main body structure health state evaluation and maintenance strategy determination method |
| CN112284282A (en) * | 2020-10-26 | 2021-01-29 | 广西建工集团控股有限公司 | Template support frame deformation monitoring and early warning method |
| CN113701818A (en) * | 2021-08-30 | 2021-11-26 | 中国建筑第七工程局有限公司 | Distributed optical fiber tunnel state monitoring method |
| CN114006767A (en) * | 2021-11-10 | 2022-02-01 | 中交长大桥隧技术有限公司 | Tunnel safety and health assessment and monitoring system based on scientific and technological intelligence |
| CN117875796A (en) * | 2024-03-12 | 2024-04-12 | 交通运输部公路科学研究所 | Highway tunnel maintenance level evaluation method |
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| CN107609805A (en) * | 2017-10-31 | 2018-01-19 | 辽宁工程技术大学 | A kind of metro safety evaluation method based on fuzzy comprehensive evoluation |
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| CN104462736A (en) * | 2013-09-24 | 2015-03-25 | 冯宏伟 | System assessment method based on IAHP-F method |
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Cited By (7)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN112036734A (en) * | 2020-08-27 | 2020-12-04 | 同济大学 | Tunnel main body structure health state evaluation and maintenance strategy determination method |
| CN112036734B (en) * | 2020-08-27 | 2022-10-21 | 同济大学 | Tunnel main body structure health state evaluation and maintenance strategy determination method |
| CN112284282A (en) * | 2020-10-26 | 2021-01-29 | 广西建工集团控股有限公司 | Template support frame deformation monitoring and early warning method |
| CN113701818A (en) * | 2021-08-30 | 2021-11-26 | 中国建筑第七工程局有限公司 | Distributed optical fiber tunnel state monitoring method |
| CN114006767A (en) * | 2021-11-10 | 2022-02-01 | 中交长大桥隧技术有限公司 | Tunnel safety and health assessment and monitoring system based on scientific and technological intelligence |
| CN117875796A (en) * | 2024-03-12 | 2024-04-12 | 交通运输部公路科学研究所 | Highway tunnel maintenance level evaluation method |
| CN117875796B (en) * | 2024-03-12 | 2024-05-07 | 交通运输部公路科学研究所 | Highway tunnel maintenance level evaluation method |
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