Evaluation method of vulnerability risk of road infrastructure under heat waves

Abstract

The present invention discloses a method for evaluating the vulnerability risk of road infrastructure under the influence of heat waves, the method comprising: obtaining road infrastructure data, asphalt pavement design parameters, and regional temperature data of a set scenario; performing exposure evaluation on the road infrastructure based on the regional temperature data of the set scenario; performing sensitivity evaluation on the road infrastructure based on the road infrastructure data and asphalt pavement design parameters; performing fitness evaluation on the road infrastructure based on the road infrastructure data and asphalt pavement design parameters and regional temperature data of the set scenario; integrating the results of exposure evaluation, sensitivity evaluation, and fitness evaluation to obtain a road infrastructure vulnerability evaluation result. The present invention takes into account the impact of extreme weather on the long-term service reliability of road infrastructure, quantitatively evaluates the vulnerability of road infrastructure under the scenario of heat wave climate events, and provides technical support for the resilience evaluation of transportation infrastructure under the background of climate change.

Description

Technical Field

[0001] The present invention belongs to the interdisciplinary technical field of road traffic and environmental disciplines, and more specifically, relates to a method for evaluating the vulnerability risk of road infrastructure under the influence of heat waves. Background Art

[0002] In the current context of global climate change, heat wave extreme weather events are frequent, causing far-reaching impacts on society, environment, ecology and other related fields, and have attracted widespread attention. Heat waves usually refer to high temperature weather that persists for a certain period of time. It is a unique high temperature extreme event. Heat wave events are usually accompanied by extreme conditions such as high temperature and drought, which brings huge challenges to road infrastructure. High temperature weather may cause softening and deformation of road pavement, increase the risk of traffic accidents, and pose a serious threat to the sustainability and stability of road infrastructure.

[0003] As an important part of the urban transportation system, road infrastructure is facing increasingly severe challenges. How to further consider the vulnerability assessment of road infrastructure under the influence of heat wave events is crucial to the service performance of future road infrastructure. In recent years, there has been an increasing focus on the impact of climate change events on the transportation field, but a comprehensive, systematic and scientific evaluation system has not yet been formed for the impact of extreme heat wave events on road infrastructure. Summary of the invention

[0004] The present invention is provided to solve the above-mentioned problems existing in the prior art. Therefore, a method for evaluating the vulnerability risk of road infrastructure under the influence of heat waves is needed, and the evaluation method evaluates the vulnerability of road infrastructure from three aspects: exposure, sensitivity, and adaptability. In view of future climate change and road infrastructure vulnerability, the present invention proposes an evaluation method for the vulnerability risk of road infrastructure under the influence of heat wave extreme weather and climate events that integrates three indicators: exposure, sensitivity, and adaptability, determines the vulnerability of road infrastructure under the influence of heat waves, and takes timely and accurate countermeasures.

[0005] It should be noted that vulnerability in this article refers to: the possibility of road infrastructure being damaged by hazardous events and the degree of impact; exposure refers to: whether road infrastructure will experience hazardous events and the severity of the events; sensitivity refers to: the impact and performance of road infrastructure when exposed to hazardous events; adaptability refers to: the ability of road infrastructure to adjust and respond to hazardous events.

[0006] The present invention provides a method for evaluating the vulnerability risk of road infrastructure under the influence of heat waves, the method comprising:

[0007] Obtain road infrastructure data and asphalt pavement design parameters, as well as regional temperature data for setting scenarios;

[0008] Conducting exposure assessment of road infrastructure based on regional temperature data for the scenario;

[0009] Conducting a sensitivity assessment of the road infrastructure based on the road infrastructure data and asphalt pavement design parameters;

[0010] Conducting a fitness assessment of the road infrastructure based on the road infrastructure data and asphalt pavement design parameters and regional temperature data for a set scenario;

[0011] The results of exposure assessment, sensitivity assessment and adaptability assessment are integrated to obtain the road infrastructure vulnerability assessment results.

[0012] Furthermore, the regional temperature data of the set scenario includes historical and future temperature data, wherein the future temperature data includes temperature data under four different radiation forcing scenarios: SSP126, SSP245, SSP370, and SSP585.

[0013] Furthermore, the road infrastructure data and asphalt pavement design parameters include road traffic volume, directional coefficient, lane coefficient, traffic load level, highway grade and road material type.

[0014] Furthermore, the regional temperature data for the set scenario are derived from the climate model simulation results in CMIP6.

[0015] Furthermore, based on the regional temperature data of the set scenario, an exposure assessment of the road infrastructure is performed, including:

[0016] Sort the daily maximum temperature data of historical scenarios from small to large, and use the 90% temperature value as the heat wave high temperature threshold;

[0017] According to the heat wave high temperature threshold obtained by statistics, the daily maximum temperature data of the defined scenario are screened, and the number of days when the daily maximum temperature exceeds the high temperature threshold in the historical and future scenarios is obtained respectively, and the difference in the number of days is calculated. The calculation formula for the difference in the number of days is:

[0018] D k =d k -d h (1)

[0019] Where D k represents the difference in the number of days under scenario k compared with the historical scenario, k represents each future climate change scenario, and the value k=1-4, where 1, 2, 3, and 4 represent SSP126, SSP245, SSP370, and SSP585, respectively; d k represents the number of days exceeding the extreme high temperature threshold under scenario k in the future, dh Indicates the number of days exceeding the heat wave high temperature threshold during the historical period;

[0020] The difference in days is used as an exposure indicator to characterize the road traffic infrastructure in the study area.

[0021] Furthermore, based on the road infrastructure data and asphalt pavement design parameters, a sensitivity assessment of the road infrastructure is performed, including:

[0022] The road infrastructure data and asphalt pavement design parameters were integrated to simulate the impact of heat waves on the permanent deformation of the pavement in future scenarios. The results obtained were h k Compared with the result without heat wave d By comparison, the permanent deformation difference is obtained. The calculation formula for the permanent deformation difference is:

[0023] H k =h k -h d (2)

[0024] In the formula, H k h represents the difference in permanent deformation under scenario k, whether a heat wave occurs or not. k represents climate change scenarios in different periods, and the value k=1-4, where 1, 2, 3, and 4 represent the SSP126, SSP245, SSP370, and SSP585 scenarios in the future period, respectively. d Indicates the amount of permanent deformation when no heat wave occurs;

[0025] The permanent deformation is used as a sensitivity indicator of road traffic infrastructure under heat waves.

[0026] Furthermore, based on the road infrastructure data and asphalt pavement design parameters and regional temperature data of the set scenario, the road infrastructure is evaluated for adaptability, including:

[0027] The suitability of road infrastructure is scored as follows:

[0028] 1) Determine whether targeted technical improvement measures have been adopted. If yes, score 4 points; if no, score 1 point, and get the first score value;

[0029] 2) Sort the service time of road infrastructure from largest to smallest and divide them into four groups, assigning them scores from 1 to 4 to obtain the second score;

[0030] The first score value and the second score value are added together to obtain the road infrastructure adaptability result.

[0031] Furthermore, the results of exposure assessment, sensitivity assessment and adaptability assessment are integrated to obtain the road infrastructure vulnerability assessment results, including:

[0032] Based on the results of exposure assessment, sensitivity assessment and adaptability assessment, the infrastructure vulnerability is calculated using the following formula:

[0033] V=E×40%+S×40%+A×20% (3)

[0034] Where V is infrastructure vulnerability, E is infrastructure exposure, S is infrastructure sensitivity, and A is infrastructure adaptability.

[0035] The present invention has at least the following beneficial effects:

[0036] This paper takes into account the impact of extreme weather on the long-term service reliability of road infrastructure, quantitatively evaluates the vulnerability of road infrastructure under heat wave climate event scenarios, and provides technical support for the resilience assessment of transportation infrastructure under the background of climate change. BRIEF DESCRIPTION OF THE DRAWINGS

[0037] Figure 1 A flow chart of a method for evaluating the vulnerability risk of road infrastructure under the influence of a heat wave according to an embodiment of the present invention is shown.

[0038] Figure 2 Another flow chart of a method for evaluating the vulnerability risk of road infrastructure under the influence of a heat wave according to an embodiment of the present invention is shown.

[0039] Figure 3 A schematic diagram of target road infrastructure geographic information established according to an embodiment of the present invention is shown.

[0040] Figure 4 A diagram showing a road infrastructure vulnerability risk assessment result according to an embodiment of the present invention is shown. DETAILED DESCRIPTION

[0041] In order to enable those skilled in the art to better understand the technical solution of the present invention, the present invention is described in detail below in conjunction with the accompanying drawings and specific embodiments. The embodiments of the present invention are further described in detail below in conjunction with the accompanying drawings and specific embodiments, but are not intended to limit the present invention. For the various steps described herein, if there is no necessity for a causal relationship between each other, the order in which they are described as examples herein should not be regarded as a limitation, and those skilled in the art should know that they can be adjusted in order, as long as the logic between them is not destroyed, resulting in the inability to implement the entire process.

[0042] Figure 1 The flowchart of a method for evaluating the vulnerability risk of road infrastructure under the influence of heat waves according to an embodiment of the present invention is shown. The embodiment of the present invention provides a method for evaluating the vulnerability risk of road infrastructure under the influence of heat waves, such as Figure 1As shown, the method includes the following steps S100-S500.

[0043] Step S100, obtaining road infrastructure data and asphalt pavement design parameters, as well as regional temperature data for a set scenario.

[0044] In the present embodiment, according to research needs and actual needs, a certain range of regions, urban agglomerations or a single city can be selected as the study area. After selecting the study area, first, from the perspective of easy collection of data, the present invention collects the temperature data of the representative climate model in CMIP6 under the specific scenario of the study area, and extracts the daily maximum temperature data. The specific scenario here refers to the historical (1980-2010) scenario and the future (2030-2060) ssp585 radiation forcing scenario. Then, the road infrastructure data and asphalt pavement design parameters to be studied are collected, specifically including: road traffic volume, directional coefficient, lane coefficient, traffic load level, highway grade, road material type, etc.

[0045] Step S200: performing exposure assessment on road infrastructure based on regional temperature data of the set scenario.

[0046] In this embodiment, the daily maximum temperature data extracted from the above representative modes are statistically analyzed. First, the daily maximum temperature data in the historical scenarios under different modes are arranged in ascending order, and the 90% daily maximum temperature value is counted and used as the heat wave temperature threshold. Then, the daily maximum temperature data of the historical and future scenarios under different modes are compared with the heat wave temperature threshold respectively, and the average number of days in which the daily maximum temperature exceeds the heat wave temperature threshold in the historical and future scenarios is counted. The difference in the number of days exceeding the heat wave temperature threshold in the historical and future scenarios is calculated, as shown in the following formula (1). In this way, the heat wave exposure of road infrastructure is evaluated, and the evaluation results are divided into four categories, with 1 to 4 points assigned from small to large.

[0047] D k =d k -d h (1)

[0048] Where D k represents the difference in the number of days under scenario k compared with the historical scenario, k represents each future climate change scenario, and the value k=1-4, where 1, 2, 3, and 4 represent SSP126, SSP245, SSP370, and SSP585, respectively; d k represents the number of days exceeding the extreme high temperature threshold under scenario k in the future, d h Indicates the number of days exceeding the heat wave high temperature threshold during the historical period.

[0049] Step S300: Perform sensitivity assessment on the road infrastructure based on the road infrastructure data and asphalt pavement design parameters.

[0050] In this embodiment, the road traffic infrastructure data collected in step S100 are integrated to count the average annual frequency of heat waves in future scenarios. The impact of heat waves on road infrastructure in future scenarios is simulated based on the statistical results. The permanent deformation of the road surface under the influence of heat waves is used as a measurement indicator. The result h obtained in the case of a heat wave is k Compared with the result without heat wave d By comparison, the difference in permanent deformation is obtained, as shown in the following formula (2); this is used to evaluate the heat wave sensitivity of road infrastructure, and the evaluation results are divided into four categories, with scores of 1 to 4 from small to large.

[0051] H k =h k -h d (2)

[0052] In the formula, H k h represents the difference in permanent deformation under scenario k, whether a heat wave occurs or not. k represents climate change scenarios in different periods, and the value k=1-4, where 1, 2, 3, and 4 represent the SSP126, SSP245, SSP370, and SSP585 scenarios in the future period, respectively. d Indicates the amount of permanent deformation when no heat wave occurs.

[0053] Step S400: evaluating the adaptability of the road infrastructure based on the road infrastructure data, asphalt pavement design parameters, and regional temperature data of a set scenario.

[0054] In this embodiment, the heat wave adaptability assessment of road traffic infrastructure is conducted by a qualitative method; ① whether targeted technical improvement measures have been adopted, if yes, score 1, otherwise score 4; ② considering the service time of the road infrastructure, sort from large to small, and divide it into four groups, assigning values ​​from 1 to 4. Note that the older the infrastructure, the lower the score; finally, the above results are added together to obtain the adaptability result of the road infrastructure.

[0055] Step S500, integrating the results of exposure assessment, sensitivity assessment and adaptability assessment to obtain a road infrastructure vulnerability assessment result.

[0056] In this embodiment, the analysis results of the exposure, sensitivity and adaptability of the traffic infrastructure under the influence of heat waves in the above steps S100 to S400 are combined, and the scores are added according to the weights of 40%, 40% and 20% to obtain the vulnerability assessment results of the road infrastructure under the influence of heat wave extreme weather and climate events. As shown in the following formula (3):

[0057] V=E×40%+S×40%+A×20% (3)

[0058] Where V is infrastructure vulnerability, E is infrastructure exposure, S is infrastructure sensitivity, and A is infrastructure adaptability.

[0059] The following embodiments of the present invention will be combined with a specific road infrastructure to provide a case study of the implementation of the method for evaluating the vulnerability risk of road infrastructure under the influence of heat wave extreme weather and climate events proposed in the present invention, so as to fully illustrate the feasibility and progressiveness of the present invention.

[0060] This case follows Figure 2 The process shown in the figure specifically includes the following steps 1 to 5.

[0061] Step 1: Select the study area (city) and target infrastructure.

[0062] This example is carried out for a road infrastructure within the scope of City A. Figure 3 As shown in Table 1, the design parameters of the asphalt pavement of the road infrastructure are investigated, as shown in Table 1. The temperature data of the three models INM-CM-4-8, INM-CM-5-0, and IPSL-CM6A-LR in the study area under the historical (1980-2010) scenario and the future (2030-2060) scenario are collected, as shown in Table 2, and the daily maximum temperature data are extracted.

[0063] This example takes the asphalt pavement design of a two-way four-lane first-class highway in area A as an example. The traffic parameters required for pavement design are as follows: the traffic volume of large passenger cars and trucks in the section is 3,500 vehicles / day, the annual traffic growth rate is 6.5%, the directional coefficient is 0.55, and the lane coefficient is 0.50; the modulus of resilience of the top surface of the roadbed is 53MPa, and the reduction coefficient of dry-wet and freeze-thaw cycle action is 0.80.

[0064] Table 1 Asphalt pavement structure design scheme

[0065]

[0066]

[0067] Table 2 Summary of selected climate models

[0068] serial number Name of climate change research organization (country) Climate model name 1 Institute for Numerical Mathematics (Russia) INM-CM4-8 2 Institute for Numerical Mathematics (Russia) INM-CM5-0 3 Institute for Pierre-Simon Laplace (France) IPSL-CM6A-LR

[0069] Step 2: Assessment of road infrastructure exposure to heat waves.

[0070] Formula (1) is used to calculate the difference between the number of days exceeding the heat wave threshold in historical and future scenarios. Based on the average daily maximum temperature data of the three models INM-CM-4-8, INM-CM-5-0, and IPSL-CM6A-LR collected in historical scenarios, the heat wave threshold of region A is determined. After statistical analysis, it is found that the heat wave threshold should be 29.8°C. On this basis, the average number of days with daily maximum temperature exceeding the heat wave threshold in historical and future scenarios is calculated, and the difference is calculated.

[0071] Under historical and future scenarios, the difference in the number of days exceeding the heat wave threshold is between 0 and 120. The result is divided into four equal parts: 0 to 30 is assigned 1 point, 31 to 60 is assigned 2 points, 61 to 90 is assigned 3 points, and 91 to 120 is assigned 4 points. The final result of the heat wave exposure evaluation of road infrastructure is obtained.

[0072] Step 3: Assessment of heat wave sensitivity of road infrastructure.

[0073] According to formula (2), the difference in permanent deformation of road infrastructure is calculated when a heat wave event occurs or not. Based on the daily maximum temperature data in the future scenario and the heat wave temperature threshold obtained above, the frequency and duration of heat waves in the future scenario are assumed to simulate the impact of heat waves on road infrastructure and determine the degree of impact of heat waves on the permanent deformation of road infrastructure. In the future scenario, if the heat wave extreme climate event does not occur, according to the heat wave temperature threshold: 29.8℃, round down, and use 29℃ as the temperature parameter for simulation in this case; if the heat wave extreme climate event occurs, the average value of the daily maximum temperature data exceeding the heat wave threshold in the future scenario is calculated: 32.8℃, round up, and use 33℃ as the temperature parameter for simulation in this case.

[0074] According to the analysis of the daily maximum temperature data under future scenarios, combined with the analysis of future high temperature heat waves in the references "Changes and predictions of high temperature heat waves of different intensities and types in China" and "Study on the characteristics of temporal and spatial changes and mechanisms of heat waves in China", and the estimated results, it is shown that the average occurrence frequency of high temperature heat waves of different intensities in the entire Chinese region is 0.81, 0.40, 0.14 and 0.11 times / year, respectively. It is generally believed that the frequency of heat waves in future scenarios is 2 to 4 times / year. Therefore, the present invention assumes that an average of 2, 3, and 4 extreme heat wave events will occur each year in the future, with a unified duration of 5 days.

[0075] Assuming that the exposure score is 1-2 points, there will be 2 heat wave extreme climate events every year; the exposure score is 2-3 points, there will be 3 heat wave extreme climate events every year; the exposure score is 3-4 points, there will be 4 heat wave extreme climate events every year.

[0076] Step 4: Assessment of road infrastructure adaptability to heat waves.

[0077] The adaptability of road infrastructure under heat wave extreme climate events is evaluated by qualitative analysis. The present invention assumes that the infrastructure is a newly built road and no targeted technical improvement measures have been adopted. Also, because the infrastructure is only within the area of ​​City A and the scope is relatively small, it is believed that the adaptability of each section of the infrastructure is basically consistent, and a unified score of 4 points is given.

[0078] Step 5: Assessment of road infrastructure vulnerability to heat waves.

[0079] Finally, the exposure, sensitivity, and adaptability assessment scores are calculated according to formula (3) to obtain the vulnerability risk assessment results of each section of road infrastructure under the influence of extreme heat wave events. Figure 4 shown.

[0080] The above description is intended to be illustrative rather than restrictive. For example, the above examples (or one or more of them) can be used in combination with each other. For example, those of ordinary skill in the art can use other embodiments when reading the above description. In addition, in the above-mentioned specific embodiments, various features can be grouped together to simplify the present invention. This should not be interpreted as a feature of an invention that is not claimed for protection being necessary for any claim. On the contrary, the subject matter of the present invention may be less than all the features of the embodiments of a specific invention. Thus, the following claims are incorporated into the specific embodiments as examples or embodiments, wherein each claim is independently used as a separate embodiment, and it is considered that these embodiments can be combined with each other in various combinations or arrangements. The scope of the present invention should be determined with reference to the full scope of the equivalent forms of the attached claims and these claims.

Claims

1. A method for evaluating the vulnerability risk of road infrastructure under the influence of heat waves, characterized in that: The method comprises: Obtain road infrastructure data and asphalt pavement design parameters, as well as regional temperature data for setting scenarios; Conducting exposure assessment of road infrastructure based on regional temperature data for the scenario; Conducting a sensitivity assessment of the road infrastructure based on the road infrastructure data and asphalt pavement design parameters; Conducting a fitness assessment of the road infrastructure based on the road infrastructure data and asphalt pavement design parameters and regional temperature data for a set scenario; Integrate the results of exposure assessment, sensitivity assessment and adaptability assessment to obtain the vulnerability assessment results of road infrastructure; Based on the regional temperature data for the scenario, the exposure assessment of road infrastructure is conducted, including: Sort the daily maximum temperature data of historical scenarios from small to large, and use the 90% temperature value as the heat wave high temperature threshold; According to the heat wave high temperature threshold obtained by statistics, the daily maximum temperature data of the defined scenario are screened, and the number of days when the daily maximum temperature exceeds the high temperature threshold in the historical and future scenarios is obtained respectively, and the difference in the number of days is calculated. The calculation formula for the difference in the number of days is: D k =d k -d h (1) Where D k represents the difference in the number of days under scenario k compared with the historical scenario, k represents each future climate change scenario, and the value k=1-4, where 1, 2, 3, and 4 represent SSP126, SSP245, SSP370, and SSP585, respectively; d k represents the number of days exceeding the extreme high temperature threshold under scenario k in the future, d h Indicates the number of days exceeding the heat wave high temperature threshold during the historical period; The difference in days is used as an exposure indicator to characterize the road traffic infrastructure in the study area.

2. The method according to claim 1, characterized in that: The regional temperature data for the set scenario include historical and future temperature data, wherein the future temperature data include temperature data under four different radiation forcing scenarios: SSP126, SSP245, SSP370, and SSP585.

3. The method according to claim 1, characterized in that The road infrastructure data and asphalt pavement design parameters include road traffic volume, directional coefficient, lane coefficient, traffic load level, highway grade and road material type.

4. The method according to claim 1, characterized in that: The regional temperature data for the scenario are derived from the climate model simulation results in CMIP6.

5. The method according to claim 1, characterized in that Based on the road infrastructure data and asphalt pavement design parameters, a sensitivity assessment of the road infrastructure is conducted, including: The road infrastructure data and asphalt pavement design parameters were integrated to simulate the impact of heat waves on the permanent deformation of the pavement in future scenarios. The results obtained were h k Compared with the result without heat wave d By comparison, the permanent deformation difference is obtained. The calculation formula for the permanent deformation difference is: H k =h k -h d (2) In the formula, H k h represents the difference in permanent deformation under scenario k, whether a heat wave occurs or not. k represents climate change scenarios in different periods, and the value k=1-4, where 1, 2, 3, and 4 represent the SSP126, SSP245, SSP370, and SSP585 scenarios in the future period, respectively. d Indicates the amount of permanent deformation when no heat wave occurs; The permanent deformation is used as a sensitivity indicator of road traffic infrastructure under heat waves.

6. The method according to claim 1, characterized in that The suitability of the road infrastructure is evaluated based on the road infrastructure data, asphalt pavement design parameters and regional temperature data of the set scenario, including: The suitability of road infrastructure is scored as follows: 1) Determine whether targeted technical improvement measures have been adopted. If yes, score 4 points; if no, score 1 point, and get the first score value; 2) Sort the service time of road infrastructure from largest to smallest and divide them into four groups, assigning them scores from 1 to 4 to obtain the second score; The first score value and the second score value are added together to obtain the road infrastructure adaptability result.

7. The method according to claim 1, characterized in that The results of exposure assessment, sensitivity assessment and adaptability assessment are integrated to obtain the road infrastructure vulnerability assessment results, including: Based on the results of exposure assessment, sensitivity assessment and adaptability assessment, the infrastructure vulnerability is calculated using the following formula: V=E×40%+S×40%+A×20% (3) Where V is infrastructure vulnerability, E is infrastructure exposure, S is infrastructure sensitivity, and A is infrastructure adaptability.

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