Roadbed construction risk dynamic assessment management method and system based on real-time state

Abstract

The invention discloses a roadbed construction risk dynamic assessment management method and system based on a real-time state, and relates to the field of roadbed construction risk assessment. Comprising the following steps: acquiring a roadbed construction sub-road section with an overhigh or oversteep slope, and making a soil layering map of the road section; acquiring data of temperature, humidity, tension and jitter of different soil layers, and performing preliminary filtering and normalization processing; determining soil dry density and void ratio calculation values of different soil layers, and correcting the soil void ratio calculation values; acquiring soil void ratios of different soil layers when the slope instability problem occurs, and establishing a corresponding relation table between the slope instability and the soil void ratios; based on a machine learning algorithm model, establishing a correction model of the soil void ratio; and establishing a roadbed construction risk dynamic evaluation platform, and judging whether a slope instability risk exists or not. The roadbed construction risk is dynamically monitored in real time, the slope instability risk is effectively evaluated and judged, risk early warning is given out in time, and the roadbed construction safety is improved.

Description

Technical Field

[0001] The present invention relates to the field of subgrade construction risk assessment, and specifically to a dynamic assessment and management method and system for subgrade construction risk based on real-time status. Background Technique

[0002] The safety of subgrade construction not only concerns the lives of workers and the quality of the project, and slope instability is one of the common geological disasters in subgrade construction. If the slope design or construction is unreasonable, it may cause serious consequences such as landslides and collapses, seriously threatening construction safety and project quality. Especially in areas where high slopes need to be excavated or high embankments need to be filled, and where the natural slope is relatively steep, due to the large height of the slope, it is easy to cause instability after excavation or filling. The so-called slope instability refers to the phenomenon that a slope (such as a hillside, road cut, foundation pit, etc.) loses its original stability and undergoes sliding, collapse or rockfall under the action of natural forces or human activities. Therefore, in the process of subgrade construction, avoiding the risk of slope instability is of great significance.

[0003] The existing slope instability risk detection technologies mainly analyze by collecting the soil quality of the subgrade construction section on-site, or reinforce the slope through retaining walls, anchor bolts and other supporting structures to prevent instability risks. However, such technologies are short-term detections and preventions, lacking continuous monitoring. Once the slope instability occurs from the inside out, such as groundwater eroding the inside of the slope, resulting in softening of the soil inside the slope and reduction of soil strength, and when it cannot be observed in time from the outside, the slope instability risk will be very difficult to be detected, easily causing slope instability accidents, resulting in slope collapse and subsidence, and causing casualties. Summary of the Invention

[0004] To solve the above technical problems, a dynamic assessment and management method and system for subgrade construction risk based on real-time status are provided. The technical solution of the present invention solves the problem of lack of continuous monitoring mentioned in the above background technique. Once the slope instability occurs from the inside out, such as groundwater eroding the inside of the slope, resulting in softening of the soil inside the slope and reduction of soil strength, and when it cannot be observed in time from the outside, the slope instability risk will be very difficult to be detected, easily causing slope instability accidents, resulting in slope collapse and subsidence, and causing casualties.

[0005] To achieve the above objectives, the technical solution adopted by the present invention is as follows:

[0006] A dynamic assessment and management method for subgrade construction risk based on real-time status, including:

[0007] According to the subgrade construction environment, obtain the subgrade construction subsections with overly high or steep slopes, and make a soil stratification map of this section;

[0008] Set up a hardware acquisition device to obtain data on temperature, humidity, tension, and jitter of different soil layers, and perform preliminary data filtering and normalization processing;

[0009] Based on the humidity data of different soil layers, determine the calculated values of soil dry density and porosity of different soil layers, and correct the calculated value of soil porosity according to the actual detection situation;

[0010] Based on big data, obtain the soil porosity of different soil layers when slope instability occurs, and establish a correspondence table between slope instability and soil porosity;

[0011] According to the data of temperature, humidity, tension, jitter, and soil porosity of different soil layers, based on the machine learning algorithm model, establish a correction model of soil porosity to obtain accurate soil porosity values;

[0012] Establish a dynamic risk assessment platform for subgrade construction. By using the obtained accurate soil porosity values and combining with the correspondence table between slope instability and soil porosity, judge whether there is a risk of slope instability.

[0013] Preferably, the method for obtaining the subgrade construction sub-section with too high or too steep slope according to the subgrade construction environment and making the soil layer map of this section specifically includes:

[0014] Analyze the subgrade construction environment based on historical data or expert research, and divide the subgrade construction section into several sub-sections;

[0015] Select the subgrade construction sub-section with too high or too steep slope from several sub-sections, and mark this section as a slope instability risk section;

[0016] Through the hardware acquisition device, collect the soil layer data of the slope instability risk section, and make the soil layer map of this section.

[0017] Preferably, the method for setting up a hardware acquisition device to obtain data on temperature, humidity, tension, and jitter of different soil layers, and performing preliminary data filtering and normalization processing specifically includes:

[0018] According to the soil layer map of the slope instability risk section, set up a hardware acquisition device in different soil layers to obtain data on temperature, humidity, tension, and jitter of different soil layers;

[0019] According to the Kalman filter algorithm, filter and optimize the data on temperature, humidity, tension, and jitter of different soil layers received;

[0020] According to the normalization formula, perform normalization processing on the filtered data on temperature, humidity, tension, and jitter of different soil layers to eliminate the influence of data dimension;

[0021] Store the original data and the data after filtering and normalization of the temperature, humidity, tension, and jitter of different soil layers separately, and establish datasets of the temperature, humidity, tension, and jitter of different soil layers. Herein, the datasets of the temperature, humidity, tension, and jitter of different soil layers refer to the data after filtering and normalization.

[0022] Preferably, the determination of the soil dry density and the calculated value of the porosity ratio of different soil layers according to the humidity data of different soil layers, and the correction of the calculated value of the soil porosity ratio according to the actual detection situation specifically include:

[0023] Obtain the soil particle volume and soil particle mass of the collected soil part according to the humidity acquisition device of different soil layers;

[0024] Obtain the wet density of the soil of different soil layers according to the soil particle volume and soil particle mass;

[0025] Obtain the dry density of the soil of different soil layers according to the wet density of the soil of different soil layers;

[0026] Determine the calculated value of the soil porosity ratio of different soil layers according to the dry density of the soil of different soil layers;

[0027] Obtain the soil porosity ratio values of different soil layers at the site according to the on-site field detection, and use them as the standard soil porosity ratio values;

[0028] Establish a correction function for the calculated value of the soil porosity ratio according to the calculated values of the soil porosity ratio and the standard soil porosity ratio values of multiple groups of different soil layers, and correct and optimize the calculated value of the soil porosity ratio;

[0029] The expression of the correction function for the calculated value of the soil porosity ratio is:

[0030]

[0031] In the formula, f(x) is the corrected value of the calculated value of the soil porosity ratio of different soil layers, δ is the equilibrium constant term, x i is the calculated value of the soil porosity ratio of the i-th different soil layer, is the standard soil porosity ratio value of the same layer, and N is the number of data groups of the calculated values of the soil porosity ratio and the standard soil porosity ratio values of different soil layers.

[0032] Preferably, the obtaining of the soil porosity ratio of different soil layers when a slope instability problem occurs based on big data, and the establishment of a correspondence table between slope instability and soil porosity ratio specifically include:

[0033] Obtain the range values of the soil porosity ratio of different soil layers when a slope instability problem occurs based on big data or simulation experiments;

[0034] Obtain the range values of soil void ratios for different soil layers in a fixed time period according to the time series before the occurrence of slope instability problems;

[0035] Establish a correspondence table between slope instability and soil void ratio according to the time series, slope instability problems, and the range values of soil void ratios for different soil layers.

[0036] Preferably, the establishment of a correction model for soil void ratio based on machine learning algorithm models according to the temperature, humidity, tension, jitter, and soil void ratio data of different soil layers, and obtaining accurate soil void ratio values specifically includes:

[0037] Establish a linear regression equation between soil void ratio and temperature, humidity, tension, and jitter according to the temperature, humidity, tension, jitter, and soil void ratio data of different soil layers;

[0038] Determine the loss function between soil void ratio and temperature, humidity, tension, and jitter according to the linear regression equation between soil void ratio and temperature, humidity, tension, and jitter;

[0039] Determine the parameter update equation between soil void ratio and temperature, humidity, tension, and jitter according to the gradient descent algorithm;

[0040] Based on machine learning algorithm models, establish a correction model for soil void ratio, judge the influence of soil temperature, humidity, tension, and jitter on soil void ratio, further optimize the soil void ratio data, and obtain accurate soil void ratio values.

[0041] Preferably, the establishment of a dynamic risk assessment platform for subgrade construction, and judging whether there is a slope instability risk by combining the obtained accurate soil void ratio values with the correspondence table between slope instability and soil void ratio specifically includes:

[0042] Establish a dynamic risk assessment platform for subgrade construction and build a program running environment for the correction model of soil void ratio;

[0043] Based on the dynamic risk assessment platform for subgrade construction, receive and store the data of temperature, humidity, tension, and jitter of different soil layers, and obtain accurate soil void ratio values;

[0044] Based on the dynamic risk assessment platform for subgrade construction, compare the accurate soil void ratio values with the correspondence table between slope instability and soil void ratio, screen out the accurate soil void ratio values within the range of the correspondence table between slope instability and soil void ratio, and perform coding and marking;

[0045] Set a fitting limit threshold for accurate soil void ratio values based on big data;

[0046] According to the accurately soil pore ratio coding sequence after marking, compare it according to the time series before a single slope instability problem occurs, and judge whether the data fitting degree exceeds the fitting limit threshold. If so, it indicates the existence of slope instability risk and issues a warning alarm. If not, it indicates the non-existence of slope instability risk;

[0047] Specifically, when the soil pore ratio after marking conforms to the soil pore ratio range values of different soil layers when a slope instability problem occurs, directly determine the existence of slope instability risk and issue a warning alarm.

[0048] Furthermore, this solution proposes a real-time state-based dynamic risk assessment and management system for subgrade construction, which is used to implement the real-time state-based dynamic risk assessment and management method for subgrade construction as described above, including:

[0049] A data processing module, which is used to obtain the sub-road sections of subgrade construction with overly high or steep slopes according to the subgrade construction environment, and make a soil layer map of this section; set up a hardware acquisition device to obtain data on temperature, humidity, tension, and jitter of different soil layers, and perform preliminary data filtering and normalization processing;

[0050] A risk judgment module, which is used to determine the soil dry density and calculated pore ratio values of different soil layers according to the humidity data of different soil layers, and correct the calculated pore ratio values of the soil according to the actual detection situation; based on big data, obtain the soil pore ratio of different soil layers when a slope instability problem occurs, and establish a correspondence table between slope instability and soil pore ratio; according to the data of temperature, humidity, tension, jitter, and soil pore ratio of different soil layers, based on a machine learning algorithm model, establish a correction model of the soil pore ratio to obtain the accurately soil pore ratio value; establish a dynamic risk assessment platform for subgrade construction, and judge whether there is a slope instability risk by combining the obtained accurately soil pore ratio value with the correspondence table between slope instability and soil pore ratio.

[0051] Preferably, the data processing module includes:

[0052] A road section analysis unit, which is used to obtain the sub-road sections of subgrade construction with overly high or steep slopes according to the subgrade construction environment, and make a soil layer map of this section;

[0053] A data acquisition unit, which is used to set up a hardware acquisition device to obtain data on temperature, humidity, tension, and jitter of different soil layers, and perform preliminary data filtering and normalization processing.

[0054] Preferably, the risk judgment module includes:

[0055] A void ratio determination unit, which is used to determine the dry density and calculated void ratio of different soil layers according to the humidity data of different soil layers, and correct the calculated void ratio of the soil according to the actual detection situation;

[0056] A risk reference unit, which is used to obtain the void ratio of different soil layers when slope instability occurs based on big data, and establish a correspondence table between slope instability and void ratio;

[0057] A void ratio correction unit, which is used to establish a correction model of the soil void ratio based on a machine learning algorithm model according to the temperature, humidity, tension, jitter and void ratio data of different soil layers, and obtain an accurate soil void ratio value;

[0058] A risk assessment unit, which is used to establish a dynamic assessment platform for subgrade construction risks, and judge whether there is a risk of slope instability by combining the obtained accurate soil void ratio value with the correspondence table between slope instability and void ratio.

[0059] Compared with the prior art, the beneficial effects of the present invention are as follows:

[0060] According to the subgrade construction environment, obtain the subsections of the subgrade construction where the slope is too high or too steep, and mark this section as a slope instability risk section. Then, make a soil stratification map of this section, and set up hardware acquisition devices according to the soil stratification situation of the soil stratification map to obtain data on temperature, humidity, tension, and jitter of different soil layers. Conduct preliminary data filtering and normalization processing, and store the original data and the filtered and normalized data of temperature, humidity, tension, and jitter of different soil layers respectively to establish datasets of temperature, humidity, tension, and jitter of different soil layers. Secondly, according to the humidity data of different soil layers, determine the soil dry density and the calculated value of the porosity ratio of different soil layers. Based on on-site detection, obtain the soil porosity values of different soil layers at the site as the standard soil porosity values, and establish a correction function for the calculated value of the soil porosity ratio according to the calculated value of the soil porosity ratio and the standard soil porosity value of different soil layers to correct and optimize the calculated value of the soil porosity ratio. Furthermore, according to the data of temperature, humidity, tension, jitter, and soil porosity ratio of different soil layers, establish a correction model of the soil porosity ratio based on a machine learning algorithm model to judge the influence of soil temperature, humidity, tension, and jitter on the soil porosity ratio, further optimize the soil porosity ratio data, and obtain accurate soil porosity values. Finally, based on big data, obtain the soil porosity ratios of different soil layers when slope instability occurs, establish a corresponding relationship table between slope instability and soil porosity ratio, and establish a dynamic risk assessment platform for subgrade construction. By using the obtained accurate soil porosity values and combining with the corresponding relationship table between slope instability and soil porosity ratio, judge whether there is a slope instability risk. By judging whether the data fitting degree exceeds the fitting limit threshold, if so, it means there is a slope instability risk and an early warning alarm is issued; if not, it means there is no slope instability risk. In particular, when the marked soil porosity value conforms to the soil porosity ratio range value of different soil layers when slope instability occurs, it is directly determined that there is a slope instability risk and an early warning alarm is issued, so as to dynamically and real-time monitor the subgrade construction risk, effectively evaluate and judge the slope instability risk, issue a risk early warning in time, and improve the safety of subgrade construction. Brief Description of the Drawings

[0061] Figure 1 Flowchart of the dynamic risk assessment and management method for subgrade construction based on real-time status of the present invention;

[0062] Figure 2 Flowchart of determining the soil dry density and the calculated value of the porosity ratio of different soil layers according to the humidity data of different soil layers and correcting the calculated value of the soil porosity ratio according to the actual detection situation of the present invention;

[0063] Figure 3A flow chart for establishing a correction model of soil porosity ratio based on machine learning algorithm model according to data of different soil layer temperatures, humidities, tensions, jitters and soil porosity ratios of the present invention, and obtaining accurate soil porosity ratio values;

[0064] Figure 4 A flow chart for establishing a dynamic assessment platform for subgrade construction risks of the present invention, and judging whether there is a risk of slope instability by combining the obtained accurate soil porosity ratio values with the corresponding relationship table between slope instability and soil porosity ratio. Specific embodiments

[0065] The following description is used to disclose the present invention so that those skilled in the art can implement the present invention. The preferred embodiments in the following description are only examples, and those skilled in the art can think of other obvious variations.

[0066] Refer to Figure 1 As shown, a dynamic assessment management method for subgrade construction risks based on real-time status includes:

[0067] According to the subgrade construction environment, obtain the subgrade construction subsections with overly high or steep slopes, and make a soil layer map of this section;

[0068] Set up hardware acquisition devices to obtain data of different soil layer temperatures, humidities, tensions and jitters, and perform preliminary data filtering and normalization processing;

[0069] According to the humidity data of different soil layers, determine the soil dry density and calculated porosity ratio values of different soil layers, and correct the calculated porosity ratio values of the soil according to the actual detection situation;

[0070] Based on big data, obtain the soil porosity ratios of different soil layers when slope instability problems occur, and establish a corresponding relationship table between slope instability and soil porosity ratio;

[0071] According to data of different soil layer temperatures, humidities, tensions, jitters and soil porosity ratios, establish a correction model of soil porosity ratio based on machine learning algorithm model, and obtain accurate soil porosity ratio values;

[0072] Establish a dynamic assessment platform for subgrade construction risks, and judge whether there is a risk of slope instability by combining the obtained accurate soil porosity ratio values with the corresponding relationship table between slope instability and soil porosity ratio.

[0073] It can be explained that according to the subgrade construction environment, this solution obtains the sub-sections of the subgrade construction where the slope is too high or too steep, marks this section as a slope instability risk section, thereby creating a soil stratification map of this section, and setting up hardware acquisition devices according to the soil stratification of the soil stratification map to obtain data on temperature, humidity, tension, and jitter of different soil stratifications, performing preliminary data filtering and normalization processing, and storing the original data and the filtered and normalized data of temperature, humidity, tension, and jitter of different soil stratifications respectively to establish datasets of temperature, humidity, tension, and jitter of different soil stratifications. Secondly, according to the humidity data of different soil stratifications, determine the soil dry density and the calculated value of the porosity ratio of different soil stratifications, obtain the soil porosity ratio values of different soil stratifications on-site through on-site detection as the standard soil porosity ratio values, and establish a correction function for the calculated value of the soil porosity ratio according to the calculated value of the soil porosity ratio and the standard soil porosity ratio value of different soil stratifications to correct and optimize the calculated value of the soil porosity ratio. Furthermore, according to the data of temperature, humidity, tension, jitter, and soil porosity ratio of different soil stratifications, establish a correction model of the soil porosity ratio based on the machine learning algorithm model to judge the influence of soil temperature, humidity, tension, and jitter on the soil porosity ratio, further optimize the soil porosity ratio data, and obtain accurate soil porosity ratio values. Finally, based on big data, obtain the soil porosity ratios of different soil stratifications when slope instability problems occur, establish a correspondence table between slope instability and soil porosity ratio, and establish a dynamic risk assessment platform for subgrade construction. By using the obtained accurate soil porosity ratio values and combining with the correspondence table between slope instability and soil porosity ratio, judge whether there is a slope instability risk. By judging whether the data fitting degree exceeds the fitting limit threshold, if so, it means there is a slope instability risk and an early warning alarm is issued; if not, it means there is no slope instability risk. In particular, when the marked soil porosity ratio value conforms to the soil porosity ratio range value of different soil stratifications when slope instability problems occur, it is directly determined that there is a slope instability risk and an early warning alarm is issued, thereby dynamically and real-timely monitoring the subgrade construction risk, effectively evaluating and judging the slope instability risk, timely issuing a risk warning, and improving the safety of subgrade construction.

[0074] Refer to Figure 2 As shown, the determination of the soil dry density and the calculated value of the porosity ratio of different soil stratifications according to the humidity data of different soil stratifications and the correction of the calculated value of the soil porosity ratio according to the actual detection situation specifically include:

[0075] According to the humidity acquisition devices of different soil stratifications, obtain the soil particle volume and soil particle mass of the collected soil part;

[0076] According to the soil particle volume and soil particle mass, obtain the soil wet density of different soil stratifications;

[0077] Obtain the dry density of the soil for different soil layers based on the wet density of the soil for different soil layers;

[0078] Determine the calculated value of the soil porosity ratio for different soil layers based on the dry density of the soil for different soil layers;

[0079] Obtain the soil porosity ratio values for different soil layers at the site through on-site field tests, and use them as the standard soil porosity ratio values;

[0080] Establish a correction function for the calculated value of the soil porosity ratio based on multiple groups of calculated values of the soil porosity ratio and standard soil porosity ratio values for different soil layers to correct and optimize the calculated value of the soil porosity ratio;

[0081] The expression of the correction function for the calculated value of the soil porosity ratio is:

[0082]

[0083] In the formula, f(x) is the corrected value of the calculated value of the soil porosity ratio for different soil layers, δ is the equilibrium constant term, x i is the calculated value of the soil porosity ratio for the i-th different soil layer, is the standard soil porosity ratio value of the same layer, and N is the number of data groups of the calculated values of the soil porosity ratio and standard soil porosity ratio values for different soil layers.

[0084] It can be explained that the porosity ratio is an important indicator to measure the compactness of the soil. The larger the porosity ratio, the looser the soil; the smaller the porosity ratio, the denser the soil. By analyzing the soil porosity ratio, the tightness and stability between the soils of the slope can be effectively reflected. Therefore, it has a good effect to judge the slope instability risk through the soil porosity ratio. In this solution, based on the monitored soil humidity value, the dry density and calculated value of the soil porosity ratio of different soil layers are obtained, and through on-site field tests, the soil porosity ratio values of different soil layers at the site are obtained as the standard soil porosity ratio values. A correction function for the calculated value of the soil porosity ratio is established based on the calculated values of the soil porosity ratio and standard soil porosity ratio values of different soil layers to correct and optimize the calculated value of the soil porosity ratio. Among them,

[0085] The expression of the dry density of the soil is:

[0086]

[0087] In the formula, ρ d is the dry density value of the soil for different soil layers, M is the total mass of the soil particles in the collected soil part, V is the total volume of the soil particles in the collected soil part, and ω is the water content of the soil, that is, the soil humidity value;

[0088] The expression of the calculated value of the soil porosity ratio is:

[0089]

[0090] where x i is the calculated value of the soil void ratio of the i-th different soil layer, M S is the mass of a unit soil particle in the collected soil part, V S is the volume of a unit soil particle in the collected soil part, ρ ω is the density of water.

[0091] Referring to Figure 3 shown, the establishment of a correction model for the soil void ratio and the acquisition of accurate soil void ratio values based on the machine learning algorithm model according to the temperature, humidity, tension, jitter and soil void ratio data of different soil layers specifically include:

[0092] Establish a linear regression equation between the soil void ratio and temperature, humidity, tension and jitter according to the temperature, humidity, tension, jitter and soil void ratio data of different soil layers;

[0093] Determine the loss function between the soil void ratio and temperature, humidity, tension and jitter according to the linear regression equation between the soil void ratio and temperature, humidity, tension and jitter;

[0094] Determine the parameter update equation between the soil void ratio and temperature, humidity, tension and jitter according to the gradient descent algorithm;

[0095] Based on the machine learning algorithm model, establish a correction model for the soil void ratio, judge the influence of soil temperature, humidity, tension and jitter on the soil void ratio, further optimize the soil void ratio data, and obtain accurate soil void ratio values.

[0096] It can be explained that slope instability and soil void ratio values are affected by multiple factors, such as soil temperature, humidity, tension and jitter. Therefore, when considering the soil void ratio value and slope instability risk, it is necessary to consider the influence brought by soil temperature, humidity, tension and jitter. In this solution, a linear regression equation between the soil void ratio and temperature, humidity, tension and jitter is established according to the temperature, humidity, tension, jitter and soil void ratio data of different soil layers, and the error between the calculated value of the soil void ratio and the actual soil void ratio value is optimized through the gradient descent algorithm. Thus, based on the traditional linear regression machine learning algorithm model, a correction model for the soil void ratio is established, effectively integrating the influence of soil temperature, humidity, tension and jitter on the calculated value of the soil void ratio, and obtaining accurate soil void ratio values, where

[0097] The expression of the linear regression equation between the soil void ratio and temperature, humidity, tension and jitter is:

[0098]

[0099] In the formula, is the exact value of the calculated soil porosity ratio of different soil layers, f(x) is the corrected value of the calculated soil porosity ratio of different soil layers, θ0 is the error balance term, θ1, θ2, θ3, and θ4 are the parameters of soil temperature, humidity, tension, and jitter data respectively, x1 is the collected soil temperature data, x2 is the collected soil humidity data, x3 is the collected soil tension data, and x4 is the collected soil jitter data;

[0100] The expression of the loss function of the soil porosity ratio with temperature, humidity, tension, and jitter is:

[0101]

[0102] In the formula, g θ (x) is the deviation degree value between the calculated soil porosity ratio of different soil layers and the standard soil porosity ratio value of the same layer, m is the number of collected data groups, is the standard soil porosity ratio value of the same layer;

[0103] The expression of the parameter update equation of the soil porosity ratio with temperature, humidity, tension, and jitter is:

[0104]

[0105] In the formula, is the parameter value at the (i + 1)-th iteration of the parameter corresponding to the j-th influencing factor, is the parameter value at the i-th iteration of the parameter corresponding to the j-th influencing factor, γ is the learning rate, and θ j is the parameter corresponding to the j-th influencing factor.

[0106] Referring to Figure 4 shown, the establishment of the dynamic risk assessment platform for subgrade construction, by obtaining the exact soil porosity ratio value, combined with the corresponding relationship table between slope instability and soil porosity ratio, to determine whether there is a risk of slope instability specifically includes:

[0107] Establish a dynamic risk assessment platform for subgrade construction and build a program running environment for the correction model of soil porosity ratio;

[0108] Based on the dynamic risk assessment platform for subgrade construction, receive and store data on temperature, humidity, tension, and jitter of different soil layers, and obtain the exact soil porosity ratio value;

[0109] Based on the dynamic risk assessment platform for subgrade construction, compare the exact soil porosity ratio value with the corresponding relationship table between slope instability and soil porosity ratio, screen out the exact soil porosity ratio values within the range of the corresponding relationship table between slope instability and soil porosity ratio, and perform coding and marking;

[0110] Based on big data, set an accurate fitting limit threshold for the soil pore ratio;

[0111] According to the accurately marked soil pore ratio coding sequence, compare it according to the time series before a single slope instability problem occurs, and judge whether the data fitting degree exceeds the fitting limit threshold. If so, it indicates the existence of slope instability risk and issues a warning alarm. If not, it indicates the non-existence of slope instability risk;

[0112] In particular, when the marked soil pore ratio conforms to the soil pore ratio range values of different soil layers when slope instability problems occur, directly determine the existence of slope instability risk and issue a warning alarm.

[0113] It can be explained that the steps to judge whether there is slope instability risk according to the soil pore ratio are specifically as follows:

[0114] According to the correction model of the soil pore ratio, obtain the accurate soil pore ratio value;

[0115] According to the correspondence table between slope instability and soil pore ratio, check whether the obtained accurate soil pore ratio value is within the corresponding range;

[0116] Screen out the accurately marked soil pore ratio values within the corresponding range of the correspondence table between slope instability and soil pore ratio, and perform coding and marking;

[0117] According to the accurately marked soil pore ratio coding sequence, compare it according to the time series before a single slope instability problem occurs;

[0118] Judge whether the data fitting degree exceeds the fitting limit threshold. If so, it indicates the existence of slope instability risk and issues a warning alarm. If not, it indicates the non-existence of slope instability risk;

[0119] In particular, when the marked soil pore ratio conforms to the soil pore ratio range values of different soil layers when slope instability problems occur, directly determine the existence of slope instability risk and issue a warning alarm;

[0120] Thus, through the above steps, it is possible to effectively judge whether there is slope instability risk, predict and warn of slope instability risk, and achieve the purpose of preventing slope instability risk.

[0121] Furthermore, based on the same inventive concept as the above-mentioned dynamic assessment and management method for subgrade construction risk based on real-time status, this solution proposes a dynamic assessment and management system for subgrade construction risk based on real-time status, including:

[0122] A data processing module, which is used to obtain sub-sections of roadbed construction with overly high or steep slopes according to the roadbed construction environment, and create a soil layer map for this section; set up a hardware acquisition device to obtain data on temperature, humidity, tension, and jitter of different soil layers, and perform preliminary data filtering and normalization processing;

[0123] A risk judgment module, which is used to determine the dry density and calculated value of void ratio of different soil layers according to the humidity data of different soil layers, and correct the calculated value of soil void ratio according to the actual detection situation; based on big data, obtain the soil void ratios of different soil layers when slope instability problems occur, and establish a correspondence table between slope instability and soil void ratio; according to the data of temperature, humidity, tension, jitter, and soil void ratio of different soil layers, based on a machine learning algorithm model, establish a correction model for soil void ratio, and obtain an accurate soil void ratio value; establish a dynamic risk assessment platform for roadbed construction, and judge whether there is a risk of slope instability by combining the obtained accurate soil void ratio value with the correspondence table between slope instability and soil void ratio;

[0124] The data processing module includes:

[0125] A section analysis unit, which is used to obtain sub-sections of roadbed construction with overly high or steep slopes according to the roadbed construction environment, and create a soil layer map for this section;

[0126] A data acquisition unit, which is used to set up a hardware acquisition device to obtain data on temperature, humidity, tension, and jitter of different soil layers, and perform preliminary data filtering and normalization processing;

[0127] The risk judgment module includes:

[0128] A void ratio determination unit, which is used to determine the dry density and calculated value of void ratio of different soil layers according to the humidity data of different soil layers, and correct the calculated value of soil void ratio according to the actual detection situation;

[0129] A risk reference unit, which is used to obtain the soil void ratios of different soil layers when slope instability problems occur based on big data, and establish a correspondence table between slope instability and soil void ratio;

[0130] A void ratio correction unit, which is used to establish a correction model for soil void ratio based on a machine learning algorithm model according to the data of temperature, humidity, tension, jitter, and soil void ratio of different soil layers, and obtain an accurate soil void ratio value;

[0131] A risk assessment unit, which is used to establish a dynamic risk assessment platform for subgrade construction. By obtaining the accurate soil porosity ratio and combining it with the corresponding relationship table between slope instability and soil porosity ratio, it determines whether there is a risk of slope instability.

[0132] In summary, the advantages of the present invention are as follows: effectively evaluating and judging the risk of slope instability, timely issuing risk warnings, and improving the safety of subgrade construction.

[0133] The above shows and describes the basic principles, main features and advantages of the present invention. Those skilled in the art should understand that the present invention is not limited by the above embodiments. What is described in the above embodiments and the specification is only the principle of the present invention. Without departing from the spirit and scope of the present invention, the present invention will have various changes and improvements, and these changes and improvements all fall within the scope of the present invention claimed. The scope of protection claimed by the present invention is defined by the appended claims and their equivalents.

Claims

1. A dynamic assessment and management method for subgrade construction risks based on real-time status, characterized in that, Including: According to the subgrade construction environment, obtain the subgrade construction subsections with overly high or steep slopes, and create a soil stratification map for this section; Set up hardware acquisition devices to obtain data on temperature, humidity, tension, and jitter of different soil layers, and perform preliminary data filtering and normalization processing; Based on the humidity data of different soil layers, determine the soil dry density and the calculated value of the porosity ratio for different soil layers, and correct the calculated value of the soil porosity ratio according to the actual detection situation; Based on big data, obtain the soil porosity ratios of different soil layers when slope instability occurs, and establish a correspondence table between slope instability and soil porosity ratio; Based on the temperature, humidity, tension, jitter, and soil porosity ratio data of different soil layers, establish a correction model for the soil porosity ratio based on a machine learning algorithm model to obtain accurate soil porosity values; Establish a dynamic risk assessment platform for subgrade construction. Based on the obtained accurate soil porosity values and combined with the correspondence table between slope instability and soil porosity ratio, determine whether there is a risk of slope instability.

2. The dynamic assessment and management method for subgrade construction risks based on real-time status according to claim 1, wherein, The step of obtaining the subgrade construction subsections with overly high or steep slopes according to the subgrade construction environment and creating a soil stratification map for this section specifically includes: Analyze the subgrade construction environment based on historical data or expert research, and divide the subgrade construction section into several subsections; From several subsections, select the subgrade construction subsections with overly high or steep slopes, and mark this section as a slope instability risk section; Through hardware acquisition devices, collect the soil stratification data of the slope instability risk section and create a soil stratification map for this section.

3. The dynamic assessment and management method for subgrade construction risks based on real-time status according to claim 2, characterized in that, The step of setting up hardware acquisition devices to obtain data on temperature, humidity, tension, and jitter of different soil layers and performing preliminary data filtering and normalization processing specifically includes: According to the soil stratification map of the slope instability risk section, set up hardware acquisition devices in different soil layers to obtain data on temperature, humidity, tension, and jitter of different soil layers; According to the Kalman filter algorithm, perform filtering and optimization processing on the received data on temperature, humidity, tension, and jitter of different soil layers; According to the normalization formula, perform normalization processing on the filtered data on temperature, humidity, tension, and jitter of different soil layers to eliminate the influence of data dimensions; Store the original data and the filtered and normalized data on temperature, humidity, tension, and jitter of different soil layers respectively, and establish a dataset on temperature, humidity, tension, and jitter of different soil layers, where the dataset on temperature, humidity, tension, and jitter of different soil layers refers to the filtered and normalized data.

4. The dynamic risk assessment and management method for subgrade construction based on real-time status according to claim 3, characterized in that, The step of determining the soil dry density and the calculated value of the porosity ratio for different soil layers based on the humidity data of different soil layers and correcting the calculated value of the soil porosity ratio according to the actual detection situation specifically includes: According to the humidity acquisition devices of different soil layers, obtain the soil particle volume and soil particle mass of the collected soil part; Based on the soil particle volume and soil particle mass, obtain the soil wet density of different soil layers; Based on the soil wet density of different soil layers, obtain the soil dry density of different soil layers; Determine the calculated soil porosity ratio values for different soil layers based on the dry density of the soil in different soil layers; Obtain the soil porosity ratio values of different soil layers at the site through on-site field tests as the standard soil porosity ratio values; Establish a correction function for the calculated soil porosity ratio values based on the calculated soil porosity ratio values and the standard soil porosity ratio values of multiple groups of different soil layers to correct and optimize the calculated soil porosity ratio values; The expression of the correction function for the calculated soil porosity ratio values is: In the formula, f(x) is the correction value of the calculated value of the soil void ratio of different soil layers, δ is the equilibrium constant term, and x i is the calculated value of the soil void ratio of the i-th different soil layer, is the standard soil void ratio value of the same layer, and N is the number of data groups of the calculated value of the soil void ratio and the standard soil void ratio value of different soil layers.

5. The dynamic assessment and management method for subgrade construction risks based on real-time status according to claim 4, characterized in that The specific steps of obtaining the soil porosity ratio of different soil layers when slope instability occurs based on big data and establishing a corresponding relationship table between slope instability and soil porosity ratio include: Obtain the range values of the soil porosity ratio of different soil layers when slope instability occurs based on big data or simulation experiments; Obtain the range values of the soil porosity ratio of different soil layers for a fixed time period according to the time series before slope instability occurs; Establish a corresponding relationship table between slope instability and soil porosity ratio based on the time series, slope instability problems, and range values of the soil porosity ratio of different soil layers.

6. The dynamic assessment and management method for subgrade construction risks based on real-time status according to claim 5, characterized in that, The specific steps of establishing a correction model for soil porosity ratio based on machine learning algorithm models according to the temperature, humidity, tension, vibration, and soil porosity ratio data of different soil layers to obtain accurate soil porosity ratio values include: Establish a linear regression equation between soil porosity ratio and temperature, humidity, tension, and vibration according to the temperature, humidity, tension, vibration, and soil porosity ratio data of different soil layers; Determine the loss function between soil porosity ratio and temperature, humidity, tension, and vibration according to the linear regression equation between soil porosity ratio and temperature, humidity, tension, and vibration; Determine the parameter update equation between soil porosity ratio and temperature, humidity, tension, and vibration according to the gradient descent algorithm; Based on the machine learning algorithm model, establish a correction model for soil porosity ratio, judge the influence of soil temperature, humidity, tension, and vibration on soil porosity ratio, further optimize the soil porosity ratio data, and obtain accurate soil porosity ratio values.

7. A dynamic risk assessment and management method for subgrade construction based on real-time status according to claim 6, characterized in that The specific steps of establishing a dynamic risk assessment platform for subgrade construction, judging whether there is a slope instability risk by combining the obtained accurate soil porosity ratio values with the corresponding relationship table between slope instability and soil porosity ratio include: Establish a dynamic risk assessment platform for subgrade construction and build a program running environment for the correction model of soil porosity ratio; Based on the dynamic risk assessment platform for subgrade construction, receive and store the data of temperature, humidity, tension, and vibration of different soil layers, and obtain accurate soil porosity ratio values; Based on the dynamic risk assessment platform for subgrade construction, compare the accurate soil porosity ratio values with the corresponding relationship table between slope instability and soil porosity ratio, screen out the accurate soil porosity ratio values within the range of the corresponding relationship table between slope instability and soil porosity ratio, and perform coding and marking; Based on big data, set a fitting limit threshold for accurate soil porosity ratio values; According to the encoded sequence of the marked accurate soil porosity ratio values, compare them according to the time series before a single slope instability problem occurs, and judge whether the data fitting degree exceeds the fitting limit threshold. If so, it indicates that there is a slope instability risk and an early warning alarm is issued. If not, it indicates that there is no slope instability risk; Specifically, when the marked soil porosity ratio conforms to the soil porosity ratio range values of different soil layers when slope instability problems occur, it is directly determined that there is a risk of slope instability, and a warning alarm is issued.

8. A real-time status-based dynamic risk assessment and management system for subgrade construction, characterized in that, A method for dynamically evaluating and managing the risks of subgrade construction based on real-time status as described in any one of claims 1-7, comprising: A data processing module, which is used to obtain subsections of subgrade construction with overly high or steep slopes according to the subgrade construction environment, and create a soil layer map of this section; set up a hardware acquisition device to obtain data on temperature, humidity, tension, and jitter of different soil layers, and perform preliminary data filtering and normalization processing; A risk judgment module, which is used to determine the soil dry density and calculated porosity ratio values of different soil layers according to the humidity data of different soil layers, and correct the calculated porosity ratio values according to the actual detection situation; based on big data, obtain the soil porosity ratios of different soil layers when slope instability problems occur, and establish a correspondence table between slope instability and soil porosity ratio; according to the data on temperature, humidity, tension, jitter, and soil porosity ratio of different soil layers, establish a correction model of soil porosity ratio based on a machine learning algorithm model to obtain accurate soil porosity ratio values; establish a dynamic risk assessment platform for subgrade construction, and judge whether there is a risk of slope instability by combining the obtained accurate soil porosity ratio values with the correspondence table between slope instability and soil porosity ratio.

9. The dynamic risk assessment and management system for subgrade construction based on real-time status according to claim 8, wherein The data processing module includes: A section analysis unit, which is used to obtain subsections of subgrade construction with overly high or steep slopes according to the subgrade construction environment, and create a soil layer map of this section; A data acquisition unit, which is used to set up a hardware acquisition device to obtain data on temperature, humidity, tension, and jitter of different soil layers, and perform preliminary data filtering and normalization processing.

10. A real-time status-based dynamic risk assessment and management system for subgrade construction according to claim 9, characterized in that, The risk judgment module includes: A porosity ratio determination unit, which is used to determine the soil dry density and calculated porosity ratio values of different soil layers according to the humidity data of different soil layers, and correct the calculated porosity ratio values according to the actual detection situation; A risk reference unit, which is used to obtain the soil porosity ratios of different soil layers when slope instability problems occur based on big data, and establish a correspondence table between slope instability and soil porosity ratio; A porosity ratio correction unit, which is used to establish a correction model of soil porosity ratio based on a machine learning algorithm model according to the data on temperature, humidity, tension, jitter, and soil porosity ratio of different soil layers, and obtain accurate soil porosity ratio values; A risk assessment unit, which is used to establish a dynamic risk assessment platform for subgrade construction, and judge whether there is a risk of slope instability by combining the obtained accurate soil porosity ratio values with the correspondence table between slope instability and soil porosity ratio.

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