Method for identifying insufficient concrete strength in construction stage

Abstract

The invention discloses a method for identifying insufficient concrete strength in a construction stage, and belongs to the technical field of concrete strength identification, and the method comprises the following steps: S1, obtaining characteristic data, transportation and pouring state data and vibration energy distribution data of a concrete raw material and temperature and humidity data of a curing environment; wherein the characteristic data comprises an aggregate mud content index, a cement activity coefficient and an admixture compatibility deviation value; s2, inputting the characteristic data into a pre-trained material defect risk model to obtain material risk data; s3, slump and cold joint risk probability are calculated according to the transportation and pouring state data; s4, obtaining an area with insufficient compactness according to the vibration energy distribution data, and determining process risk data according to slump and cold seam risk probability; s5, generating environment degradation data according to the temperature and humidity data of the maintenance environment; and S6, calculating an early warning risk index according to the material risk data, the process risk data and the environmental deterioration data, and generating an early warning signal according to the early warning risk data.

Description

Technical Field

[0001] The invention belongs to the technical field of concrete strength, and in particular relates to a method for identifying insufficient concrete strength during a construction phase. Background Art

[0002] As the cornerstone material of modern construction, concrete's strength properties are directly related to the structure's bearing capacity and durability. In the global urbanization process, whether it's super-high-rise buildings, large bridges, or underground infrastructure, the quality of concrete plays a decisive role in public safety and the lifespan of the project. In recent years, structural collapse accidents caused by substandard concrete strength have occurred frequently, causing huge economic losses and social impacts. This has led to an unprecedented and urgent need for quality monitoring throughout the entire concrete construction process.

[0003] Traditional concrete quality control methods often rely on manual experience judgment and post-testing. This method has certain lags and limitations and cannot provide early warning of construction hazards. Summary of the Invention

[0004] In view of this, the present invention provides a method for identifying insufficient concrete strength during the construction phase, which is used to solve the problem that traditional concrete quality control methods rely on manual experience judgment and post-testing. This method has certain lags and limitations and cannot achieve early warning and positioning of construction hazards.

[0005] To achieve the above object, the present invention provides the following technical solutions:

[0006] The present invention provides a method for identifying insufficient concrete strength during construction, comprising the following steps:

[0007] S1: Acquire the characteristic data of concrete raw materials, transportation and pouring status data, vibration energy distribution data, and temperature and humidity data of the curing environment; the characteristic data include: aggregate mud content index, cement activity coefficient, and admixture compatibility deviation value;

[0008] S2: Input the characteristic data into the pre-trained material defect risk model to obtain material risk data;

[0009] S3: Calculate slump and cold joint risk probability based on transportation and pouring status data;

[0010] S4: Determine process risk data based on vibration energy distribution data, slump and cold joint risk probability;

[0011] S5: Generate environmental degradation data based on the temperature and humidity data of the maintenance environment;

[0012] S6: Calculate the early warning risk index based on material risk data, process risk data and environmental degradation data, and determine the strength identification result based on the early warning risk index.

[0013] As an embodiment of the present invention, calculating slump and cold joint risk probability based on transportation and pouring status data includes:

[0014] Calculate the slump using the following formula:

[0015] SL(t)=SL0·e -kt

[0016] Where SL(t) is the slump, SL0 is the initial slump, k is a constant, and t is the transportation time;

[0017] Calculate the cold seam risk probability using the following formula:

[0018] PC=1-e -λd

[0019] Where PC is the cold joint risk probability, λ is a constant, and d is the layer thickness.

[0020] As an embodiment of the present invention, determining process risk data based on vibration energy distribution data, slump and cold joint risk probability includes:

[0021] Calculate the vibration energy density using the following formula:

[0022] VED=∫0 T a(t) 2 dt

[0023] Where VED is the vibration energy density of concrete per unit volume, a(t) is the acceleration of the vibration equipment at time t, and T is the total vibration time;

[0024] Obtain porosity and determine process risk data based on slump, cold joint risk probability, and void ratio vibration energy density.

[0025] As an embodiment of the present invention, generating environmental degradation data based on temperature and humidity data of a maintenance environment includes:

[0026] Obtain concrete data, temperature and humidity data of the curing environment, and ideal temperature and humidity range data;

[0027] According to the concrete data, temperature and humidity data, and ideal temperature and humidity range data, the actual hydration rate and ideal hydration rate are calculated respectively. The calculation formula is as follows:

[0028]

[0029] r i =k(Ti )

[0030] Among them, r s is the actual hydration rate, k(T) is the actual hydration rate coefficient, H(t) is the humidity data of the curing environment, H min is the minimum humidity value in the ideal temperature and humidity range data, A is the frequency factor, E a is the concrete data of concrete, R is the gas constant, T is the temperature data of the curing environment, r i is the ideal hydration rate, T i is the middle temperature value of the ideal temperature and humidity range data, k(T i ) is the ideal hydration rate coefficient;

[0031] According to the actual hydration rate and the ideal hydration rate, the hydration degree decay rate is calculated. The calculation formula is as follows:

[0032]

[0033] Wherein, HDR is the hydration degree decay rate;

[0034] According to the concrete data, temperature and humidity data and the ideal temperature and humidity range data, the microcrack density is calculated using the following formula:

[0035] MCD=γ·∫0 t (max(f T +f H -f t ,0))dt

[0036] f T =E·α·|TT i |

[0037] f H =E·β·|H min -H(t)|

[0038] Where MCD is the microcrack density, γ is the crack growth coefficient, t is the curing time, f T is thermal stress, f H is the shrinkage stress, f t is the tensile strength of the material, E is the elastic modulus, α is the thermal expansion coefficient, and β is the contraction coefficient;

[0039] Environmental degradation data is determined based on hydration decay rate and microcrack density.

[0040] As an embodiment of the present invention, a warning risk index is calculated based on material risk data, process risk data, and environmental degradation data, and a strength identification result is determined based on the warning risk index, including:

[0041] Pre-process material risk data, process risk data and environmental degradation data;

[0042] The early warning risk index is calculated based on the pre-processed material risk data, process risk data and environmental degradation data. The calculation formula is as follows:

[0043]

[0044] Among them, RI is the early warning risk index, ω i is the weight factor, x i is risk data, n=3; wherein risk data includes: material risk data, process risk data and environmental degradation data;

[0045] Generate a hidden danger heat map based on the early warning risk index, determine the intensity identification result based on the hidden danger heat map, and issue a reminder to the administrator.

[0046] As an embodiment of the present invention, a hidden danger heat map is generated according to the early warning risk index, and an intensity identification result is determined according to the hidden danger heat map and a reminder is issued to the administrator, including:

[0047] Build a BIM model based on the structural information of concrete pouring;

[0048] Divide the BIM model evenly into multiple unit areas and obtain the early warning risk index of each unit area;

[0049] Based on the preset color classification method, the color data of each unit area is determined according to the early warning risk index; wherein the color data includes: red, yellow and green;

[0050] Render the BIM model according to the color data of each unit area to obtain a hidden danger heat map;

[0051] Calculate the proportion of each color in the hidden danger heat map, obtain the intensity identification result based on the proportion of each color, and issue a reminder to the administrator; the intensity identification results include: insufficient intensity, possibly insufficient intensity, and met standards.

[0052] As an embodiment of the present invention, the method further includes: if the strength identification result is possible insufficient strength or insufficient strength, obtaining various monitoring data of the unit area with red or yellow color data in the hidden danger heat map to obtain a monitoring data set;

[0053] Obtain the safety range of each monitoring data and obtain a safety range set;

[0054] When all data in the monitoring data set are not within the preset nth safety range, it is determined that the unit area has an anomaly within the nth safety range, and the cause of the anomaly is obtained; when any of the data in the monitoring data set is within the preset nth safety range, it is determined that the unit area does not have an anomaly; wherein the preset safety range set includes n preset safety ranges, each preset safety range corresponds to a fault, and n is a natural number;

[0055] Clustering multiple unit areas according to abnormal causes and determining the number of unit areas in each cluster;

[0056] The abnormal cause corresponding to the cluster with the largest number of unit areas is selected as the target abnormal cause;

[0057] Input the target abnormality cause into the construction adjustment analysis network to generate a construction adjustment plan, and send the construction adjustment plan to the administrator to adjust the concrete construction process; wherein the construction analysis adjustment network includes: a first adjustment layer and a second adjustment layer;

[0058] As an embodiment of the present invention, the target abnormality cause is input into the construction adjustment analysis network to generate a construction adjustment plan, including:

[0059] Construct a construction adjustment analysis network;

[0060] Input the target abnormality cause into the construction adjustment analysis network to generate a construction adjustment plan;

[0061] Construct a construction adjustment analysis network, including:

[0062] Collecting a historical abnormal adjustment sample set based on any abnormal cause as a constraint; wherein any abnormal adjustment sample set in the historical abnormal adjustment sample set includes an abnormality detection feature and an adjustment plan;

[0063] Using anomaly detection features and adjustment plans as samples, we conduct correlation analysis in the adjustment phase and establish the first adjustment layer.

[0064] Using anomaly detection features and adjustment schemes as samples, we conduct correlation analysis of adjustment parameters and establish the second adjustment layer.

[0065] A construction analysis adjustment network is constructed using the first adjustment layer and the second adjustment layer.

[0066] The beneficial effects of the present invention are: through the coupling effect from the three perspectives of materials, process and environment, early warning and precise positioning of insufficient concrete strength in the concrete construction process are achieved, and the intelligent level of concrete structure quality control is improved.

[0067] Other advantages, objectives and features of the present invention will be described in the following description and will be apparent to those skilled in the art to some extent, or those skilled in the art can be taught from the practice of the present invention. The objectives and other advantages of the present invention can be realized and obtained through the following description. BRIEF DESCRIPTION OF THE DRAWINGS

[0068] In order to make the purpose, technical solutions and beneficial effects of the present invention more clear, the present invention provides the following drawings for illustration:

[0069] Figure 1 It is a schematic diagram of the process of the present invention;

[0070] Figure 2 This is a schematic diagram of constructing a thermal hazard map according to the present invention. DETAILED DESCRIPTION

[0071] like Figures 1 to 2 As shown, the present invention provides a method for identifying insufficient concrete strength during the construction phase, comprising:

[0072] S1: Acquire the characteristic data of concrete raw materials, transportation and pouring status data, vibration energy distribution data, and temperature and humidity data of the curing environment; the characteristic data include: aggregate mud content index, cement activity coefficient, and admixture compatibility deviation value;

[0073] S2: Input the characteristic data into the pre-trained material defect risk model to obtain material risk data;

[0074] S3: Calculate slump and cold joint risk probability based on transportation and pouring status data;

[0075] S4: Obtain the insufficient density area based on the vibration energy distribution data, and determine the process risk data based on the slump and cold joint risk probability;

[0076] S5: Generate environmental degradation data based on the temperature and humidity data of the maintenance environment;

[0077] S6: Calculate the early warning risk index based on the material risk data, process risk data and environmental degradation data, and determine the strength identification result based on the early warning risk index;

[0078] The working principle of the above technical solution: in the actual identification process, by integrating Internet of Things sensors, multi-scale data acquisition and intelligent analysis technology, the material properties, process parameters and environmental status of concrete raw materials in the concrete construction process are monitored in real time; specifically, the characteristic data of the process from concrete preparation to transportation to curing, transportation and pouring status data, vibration energy distribution data and temperature and humidity data of the curing environment are collected; among them, the aggregate surface contamination detector (based on digital image processing technology) is used to measure the bone mud content index SCI in real time; the cement activity coefficient CAC is obtained through the embedded cement hydration heat sensor, and when the 3d hydration heat release is <200J / g, it is determined that the cement activity is insufficient; the admixture is calculated by the fluidity deviation method, etc. Compatibility deviation value; input the characteristic data into the pre-trained material defect risk model to obtain material risk data; at the same time, calculate the slump and cold joint risk probability based on the transportation and pouring status data; obtain the insufficient density area based on the vibration energy distribution data, and determine the process risk data based on the slump and cold joint risk probability; generate environmental degradation data based on the temperature and humidity data of the maintenance environment; finally, calculate the early warning risk index based on the material risk data, process risk data and environmental degradation data, and generate strength identification results based on the early warning risk data, and the coordinates of the problem are displayed in real time; among them, the material defect risk model is trained based on the historical characteristic data and its corresponding material risk data. The specific training process is existing technology and will not be described in detail here;

[0079] Beneficial effects of the above technical solution: Through the above technical solution, by coupling the three perspectives of materials, processes and environment, early warning and precise positioning of insufficient concrete strength in the concrete construction process can be achieved, thereby improving the intelligent level of concrete structure quality control.

[0080] In one embodiment, calculating slump and cold joint risk probability based on transportation and pouring status data includes:

[0081] Calculate the slump using the following formula:

[0082] SL(t)=SL0·e -kt

[0083] Where SL(t) is the slump, SL0 is the initial slump, k is a constant, and t is the transportation time;

[0084] Calculate the cold seam risk probability using the following formula:

[0085] PC=1-e -λd

[0086] Where PC is the cold joint risk probability, λ is a constant, and d is the layer thickness;

[0087] Determine process risk data based on vibration energy distribution data, slump and cold joint risk probability, including:

[0088] Calculate the vibration energy density using the following formula:

[0089] VED=∫0 T a(t) 2 dt

[0090] Where VED is the vibration energy density of concrete per unit volume, a(t) is the acceleration of the vibration equipment at time t, and T is the total vibration time;

[0091] The porosity is obtained, and the process risk data is determined based on the slump, cold joint risk probability, and void ratio vibration energy density. The working principle of the above technical solution is: in the actual identification process, the transport time-slump loss correlation curve is calculated using the above formula, where k = 0.015min -1 , when the measured slump loss rate SL(t) is greater than 30%, an early warning is triggered; the cold joint risk probability is calculated using the above formula, where λ = 0.002mm-1, and when PC>0.7, it is judged to be high risk; at the same time, the concrete area to be tested is evenly divided into several unit volumes of concrete areas, and the concrete energy density of the unit volume of concrete area is calculated by integrating the vibration acceleration sensor data; the porosity is obtained, and the process risk data is determined based on the slump, cold joint risk probability, vibration energy density and void ratio; it is determined whether the porosity of each area is greater than the preset porosity; if it is greater, the area is judged to be an area with insufficient density, a reminder is directly issued to the administrator, and the coordinates of the area with insufficient density are output, thereby improving the final strength of the concrete; finally, the process risk data is determined by the weighted summation method based on the slump, cold joint risk probability and porosity; wherein the porosity is determined by X-CT scanning, and the preset porosity is calibrated to 5%. When the porosity is greater than the preset porosity, the area is output as an area with insufficient density;

[0092] The beneficial effects of the above technical solution: Through the above technical solution, the defects of concrete during the construction process are determined from the slump and cold joint risk probability, thereby avoiding insufficient concrete strength caused by transportation and construction; at the same time, the coordinates of the unit volume of concrete with insufficient density are output, thereby achieving early warning and precise positioning of insufficient concrete strength that is a construction hazard, and improving the intelligent level of concrete structure quality control.

[0093] In one embodiment, generating environmental degradation data based on temperature and humidity data of the maintenance environment includes:

[0094] Obtain concrete data, temperature and humidity data of the curing environment, and ideal temperature and humidity range data;

[0095] According to the concrete data, temperature and humidity data, and ideal temperature and humidity range data, the actual hydration rate and ideal hydration rate are calculated respectively. The calculation formula is as follows:

[0096]

[0097] r i =k(T i )

[0098] Among them, r s is the actual hydration rate, k(T) is the actual hydration rate coefficient, H(t) is the humidity data of the curing environment, H min is the minimum humidity value in the ideal temperature and humidity range data, A is the frequency factor, E a is the concrete data of concrete, R is the gas constant, T is the temperature data of the curing environment, r i is the ideal hydration rate, T i is the middle temperature value of the ideal temperature and humidity range data, k(T i ) is the ideal hydration rate coefficient;

[0099] According to the actual hydration rate and the ideal hydration rate, the hydration degree decay rate is calculated. The calculation formula is as follows:

[0100]

[0101] Wherein, HDR is the hydration degree decay rate;

[0102] According to the concrete data, temperature and humidity data and the ideal temperature and humidity range data, the microcrack density is calculated using the following formula:

[0103] MCD=γ·∫0 t (max(f T +f H -f t ,0))dt

[0104] f T =E·α·|TT i |

[0105] f H =E·β·|H min -H(t)|

[0106] Where MCD is the microcrack density, γ is the crack growth coefficient, t is the curing time, f T is thermal stress, f H is the shrinkage stress, f t is the tensile strength of the material, E is the elastic modulus, α is the thermal expansion coefficient, and β is the contraction coefficient;

[0107] Environmental degradation data is determined based on hydration decay rate and microcrack density.

[0108] The working principle and beneficial effects of the above technical solution: in the process of determining the environmental degradation data, the concrete data, the temperature and humidity data of the curing environment and the ideal temperature and humidity range data of the curing environment under ideal conditions are obtained; wherein, the concrete data includes the material tensile strength and thermal collision coefficient of the concrete; through the above technical solution, the hydration decay rate and microcrack density of the concrete affected by the temperature and humidity data during the curing process are respectively calculated, and then the environmental degradation index is calculated by a preset weighted summation method to evaluate the strength of the concrete; wherein the preset weighted summation method includes normalizing the hydration decay rate and the microcrack density, that is, dividing the hydration decay rate and the microcrack density by the preset maximum allowable hydration decay rate and the preset maximum allowable microcrack density respectively. After the normalization is completed, the environmental degradation data is obtained by summing the pre-set weights; so that in the process of evaluating the strength of concrete, the influence of temperature and humidity in the environment can be fully considered, thereby improving the accuracy of the judgment of the concrete strength.

[0109] In one embodiment, an early warning risk index is calculated based on the material risk data, the process risk data, and the environmental degradation data, and a strength identification result is determined based on the early warning risk index, including:

[0110] Pre-process material risk data, process risk data and environmental degradation data;

[0111] The early warning risk index is calculated based on the pre-processed material risk data, process risk data and environmental degradation data. The calculation formula is as follows:

[0112]

[0113] Among them, RI is the early warning risk index, ω i is the weight factor, x i is risk data, n=3; wherein risk data includes: material risk data, process risk data and environmental degradation data;

[0114] Generate a hidden danger heat map based on the early warning risk index, determine the intensity identification result based on the hidden danger heat map, and issue a reminder to the administrator;

[0115] Generate a hidden danger heat map based on the early warning risk index, determine the intensity identification results based on the hidden danger heat map, and issue a reminder to the administrator, including:

[0116] Build a BIM model based on the structural information of concrete pouring;

[0117] Divide the BIM model evenly into multiple unit areas and obtain the early warning risk index of each unit area;

[0118] Based on the preset color division method, the color data of each unit area is determined according to the early warning risk index;

[0119] Render the BIM model according to the color data of each unit area to obtain a hidden danger heat map;

[0120] Calculate the proportion of each color in the hidden danger heat map, obtain the intensity identification result based on the proportion of each color, and issue a reminder to the administrator;

[0121] Working principle of the above technical solution: Through the above technical solution, before calculating the early warning risk index, the risk index is pre-processed, that is, the risk index is normalized and quantified; the early warning risk index is calculated based on material risk data, process risk data and environmental degradation data; where x i is any risk data, including material risk data, process risk data and environmental degradation data; i is the weight factor corresponding to any risk data (the weight factor of material risk data is preferably 0.3, the weight factor of process risk data is 0.4, and the weight factor of environmental degradation risk data is 0.3); then, a hidden danger heat map is generated by the calculated early warning risk index; in the process of generating the hidden danger heat map, first, a BIM model is constructed according to the structural information of concrete pouring; then, the BIM model is evenly divided into multiple unit areas, and the early warning risk index of each unit area is obtained; based on the preset color division method, the color data of each unit area is determined according to the early warning risk index; the BIM model is rendered according to the color data of each unit area to obtain a hidden danger heat map; among which, the preset color division method is RI<0.3 (green), 0.3≤RI<0.6 (yellow) and RI≥0.6 (red); finally, a corresponding early warning signal is generated according to the color corresponding to the hidden danger heat map; Calculate the proportion of each color in the hidden danger heat map, and generate a strength identification result and a warning signal according to the proportion of each color; wherein, the process of generating the warning signal includes: 1. When there is a red area in the hidden danger heat map, the strength identification result is insufficient strength, and a secondary warning signal and the coordinates of the red zone are generated to the administrator; 2. When there is no red area in the hidden danger heat map, and the proportion of the yellow area is greater than the preset proportion, it is determined that the strength may be insufficient, and a first-level warning signal, the coordinates of the yellow area and the corresponding risk warning index are generated to the administrator; 3. When there is no red area in the hidden danger heat map, and the proportion of the yellow area is not greater than the preset proportion, the strength identification result is that the strength meets the standard, and the generated strength meets the standard signal is sent to the administrator; at the same time, after the administrator receives the warning signal and the coordinates of the insufficient strength position, the administrator will re-vibrate the insufficient strength position or extend the curing period according to the actual curing condition of the concrete;

[0122] The beneficial effects of the above technical solution: Through the above technical solution, by generating a warning signal of the corresponding color to indicate the strength of the concrete currently under construction, early warning and precise positioning of the concrete strength of construction hazards can be achieved; at the same time, the coordinate position of insufficient strength is reminded to the administrator, so that the administrator can remedy the insufficient strength area in time to improve the overall qualified rate of the strength after the concrete curing is completed.

[0123] In one embodiment, the method further includes: if the intensity identification result is possible insufficient intensity or insufficient intensity, obtaining various monitoring data of the unit area with red or yellow color data in the hidden danger heat map to obtain a monitoring data set;

[0124] Obtain the safety range of each monitoring data and obtain a safety range set;

[0125] When all data in the monitoring data set are not within the preset nth safety range, it is determined that the unit area has an anomaly within the nth safety range, and the cause of the anomaly is obtained; when any of the data in the monitoring data set is within the preset nth safety range, it is determined that the unit area does not have an anomaly; wherein the preset safety range set includes n preset safety ranges, each preset safety range corresponds to a fault, and n is a natural number;

[0126] Clustering multiple unit areas according to abnormal causes and determining the number of unit areas in each cluster;

[0127] The abnormal cause corresponding to the cluster with the largest number of unit areas is selected as the target abnormal cause;

[0128] Input the target abnormality cause into the construction adjustment analysis network to generate a construction adjustment plan, and send the construction adjustment plan to the administrator to adjust the concrete construction process; wherein the construction analysis adjustment network includes: a first adjustment layer and a second adjustment layer;

[0129] The working principle and beneficial effects of the above technical solution: During the concrete curing process, when the strength of the concrete is judged to be possibly insufficient or insufficient through the hidden danger heat map, the monitoring data of the unit area with red or yellow color data in the hidden danger heat map are obtained to obtain a monitoring data set, wherein the yellow and red concrete areas are key areas with insufficient concrete strength; the elements in the monitoring data set are the various data in the hidden danger heat map, such as aggregate mud content index, porosity, etc.; then the safety range of each monitoring data is obtained to obtain a preset safety range set, wherein the safety range is the expected range during the concrete curing process. When the monitoring data exceeds this safety range, the preset safety range set is obtained. When the full range is reached, there is a high probability that the strength of the concrete will be insufficient. The safety range is determined by experts based on experience; the preset safety range set includes n preset safety ranges, each preset safety range corresponds to a fault, and n is a natural number; then the monitoring data of the concrete in several unit areas are compared with the safety range in the preset safety range set. When all the data in the monitoring data set are not within the preset nth safety range, it is determined that the unit area has an abnormality in the nth safety range, and the cause of the abnormality is obtained; when any of the data in the monitoring data set is within the preset nth safety range, it is determined that the unit area does not have an abnormality; the above technical solution can be used to determine The reason why the color data of each unit area is red or yellow is determined; then, multiple unit areas are clustered according to the abnormal cause, and the number of unit areas in each cluster is determined. The abnormal cause corresponding to the cluster with the largest number of unit areas is selected as the target abnormal cause. By selecting the unit area with the largest number as the target abnormal cause, it is avoided that the abnormal cause judgment of the entire concrete pouring area is incorrect due to the monitoring abnormality of a certain unit area; wherein, the target abnormal cause is the abnormal cause of insufficient strength of the concrete pouring area divided into several unit areas; then the target abnormal cause is input into the construction adjustment analysis network for analysis, thereby obtaining a construction adjustment plan; Among them, the construction phase adjustment plan is an adjustment method for the unit area of ​​the output coordinates, such as vibration compensation intensity or extending the specific curing time, or adjusting the concrete material ratio and transportation time limit of the construction phase for the next pouring area, as well as the temperature and humidity control of the curing environment, etc.; through the construction adjustment analysis network, when the concrete strength is insufficient, a specific remedial plan can be given, so that the administrator can remedy the area during the curing process and improve the overall strength of the concrete; at the same time, the construction adjustment analysis network can also give suggestions on concrete configuration, construction technology, etc. to optimize the construction process and reduce the probability of insufficient strength due to concrete configuration or construction technology.

[0130] In one embodiment, the target abnormality cause is input into the construction adjustment analysis network to generate a construction adjustment plan, including:

[0131] Construct a construction adjustment analysis network;

[0132] Input the target abnormality cause into the construction adjustment analysis network to generate a construction adjustment plan;

[0133] Construct a construction adjustment analysis network, including:

[0134] A historical abnormal adjustment sample set is collected based on any abnormal cause as a constraint; wherein any abnormal adjustment sample in the historical abnormal adjustment sample set includes an abnormality detection feature and an adjustment plan;

[0135] Using anomaly detection features and adjustment plans as samples, we conduct correlation analysis in the adjustment phase and establish the first adjustment layer.

[0136] Using anomaly detection features and adjustment schemes as samples, we conduct correlation analysis of adjustment parameters and establish the second adjustment layer.

[0137] A construction analysis adjustment network is constructed using the first adjustment layer and the second adjustment layer.

[0138] The working principle and beneficial effects of the above technical solution: in the process of constructing the construction adjustment analysis network, firstly, a historical abnormal adjustment sample set is collected with any abnormal cause as a constraint, that is, monitoring samples when abnormalities occurred in the past are collected, and samples where abnormalities occurred are recorded; wherein, any abnormal adjustment sample in the historical abnormal adjustment sample set includes abnormal detection features and adjustment plans, abnormal adjustment samples refer to samples that meet the abnormal causes, and adjustment plans refer to adjustment plans for the construction process when abnormal causes occur; specifically, the construction stage that needs to be adjusted is determined through the first adjustment layer, and the data that needs to be specifically adjusted is determined through the second adjustment layer. For example, if the abnormal cause is insufficient slump, there may be two situations, one is the proportion of raw materials when concrete is configured, and the other is too long in transportation time. Through the first adjustment layer, When the slump of the entire layer is determined to be insufficient, the raw materials or transportation of the concrete are adjusted, and the adjustment ratio of the raw materials or the transportation control time is determined through the second adjustment layer; wherein, when constructing the first adjustment layer, the correlation between the adjusted construction stage and the abnormal cause in the adjustment plan of the abnormal adjustment sample is trained to obtain the first adjustment layer; then the correlation between the adjusted adjustment parameters and the abnormal cause in the adjustment plan of the abnormal adjustment sample is trained to obtain the second adjustment layer; finally, the construction analysis adjustment network is constructed with the first adjustment layer and the second adjustment layer; by linking the adjustment plans of concrete from configuration, construction technology and maintenance environment, it helps to provide remedial measures or optimization plans for subsequent pouring construction when insufficient strength occurs during the maintenance process, thereby achieving more precise control of concrete strength.

[0139] Finally, it should be noted that the above preferred embodiments are only used to illustrate the technical solutions of the present invention and are not limiting. Although the present invention has been described in detail through the above preferred embodiments, those skilled in the art should understand that various changes can be made in form and details without departing from the scope defined by the claims of the present invention.

Claims

1. A method for identifying insufficient concrete strength during construction, characterized in that: The following steps are involved: S1: Acquire the characteristic data of concrete raw materials, transportation and pouring status data, vibration energy distribution data, and temperature and humidity data of the curing environment; the characteristic data include: aggregate mud content index, cement activity coefficient, and admixture compatibility deviation value; S2: Input the characteristic data into the pre-trained material defect risk model to obtain material risk data; S3: Calculate slump and cold joint risk probability based on transportation and pouring status data; S4: Determine process risk data based on vibration energy distribution data, slump and cold joint risk probability; S5: Generate environmental degradation data based on the temperature and humidity data of the maintenance environment; S6: Calculate the early warning risk index based on material risk data, process risk data and environmental degradation data, and determine the strength identification result based on the early warning risk index.

2. A method for identifying insufficient concrete strength during construction according to claim 1, characterized in that: Calculate slump and cold joint risk probability based on transportation and pouring status data, including: Calculate the slump using the following formula: SL(t)=SL0·e -kt Where SL(t) is the slump, SL0 is the initial slump, k is a constant, and t is the transportation time; Calculate the cold seam risk probability using the following formula: pC=1-e -λd Where PC is the cold joint risk probability, λ is a constant, and d is the layer thickness.

3. The method for identifying insufficient concrete strength during construction according to claim 1, characterized in that: Determine process risk data based on vibration energy distribution data, slump and cold joint risk probability, including: Calculate the vibration energy density using the following formula: Where VED is the vibration energy density of concrete per unit volume, a(t) is the acceleration of the vibration equipment at time t, and T is the total vibration time; Obtain porosity and determine process risk data based on slump, cold joint risk probability, and void ratio vibration energy density.

4. The method for identifying insufficient concrete strength during construction according to claim 1, characterized in that: Generate environmental degradation data based on the temperature and humidity data of the maintenance environment, including: Obtain concrete data, temperature and humidity data of the curing environment, and ideal temperature and humidity range data; According to the concrete data, temperature and humidity data, and ideal temperature and humidity range data, the actual hydration rate and ideal hydration rate are calculated respectively. The calculation formula is as follows: r i =k(T i ) Among them, r s is the actual hydration rate, k(T) is the actual hydration rate coefficient, H(t) is the humidity data of the curing environment, H min is the minimum humidity value in the ideal temperature and humidity range data, A is the frequency factor, E a is the concrete data of concrete, R is the gas constant, T is the temperature data of the curing environment, r i is the ideal hydration rate, T i is the middle temperature value of the ideal temperature and humidity range data, k(T i ) is the ideal hydration rate coefficient; According to the actual hydration rate and the ideal hydration rate, the hydration degree decay rate is calculated. The calculation formula is as follows: Wherein, HDR is the hydration degree decay rate; According to the concrete data, temperature and humidity data and the ideal temperature and humidity range data, the microcrack density is calculated using the following formula: MCD=γ·∫0 t (max(f T +f H -f t ,0))dt f T =E·α·|T-T i | f H =E·β·|H min -H(t)| Where MCD is the microcrack density, γ is the crack growth coefficient, t is the curing time, f T is thermal stress, f H is the shrinkage stress, f t is the tensile strength of the material, E is the elastic modulus, α is the thermal expansion coefficient, and β is the contraction coefficient; Environmental degradation data is determined based on hydration decay rate and microcrack density.

5. The method for identifying insufficient concrete strength during construction according to claim 1, characterized in that: Calculate the early warning risk index based on material risk data, process risk data, and environmental degradation data, and determine the strength identification results based on the early warning risk index, including: Pre-process material risk data, process risk data and environmental degradation data; The early warning risk index is calculated based on the pre-processed material risk data, process risk data and environmental degradation data. The calculation formula is as follows: Among them, RI is the early warning risk index, ω i is the weight factor, x i is risk data, n=3; wherein risk data includes: material risk data, process risk data and environmental degradation data; Generate a hidden danger heat map based on the early warning risk index, determine the intensity identification result based on the hidden danger heat map, and issue a reminder to the administrator.

6. The method for identifying insufficient concrete strength during construction according to claim 1, characterized in that: Generate a hidden danger heat map based on the early warning risk index, determine the intensity identification results based on the hidden danger heat map, and issue a reminder to the administrator, including: Build a BIM model based on the structural information of concrete pouring; Divide the BIM model evenly into multiple unit areas and obtain the early warning risk index of each unit area; Based on the preset color classification method, the color data of each unit area is determined according to the early warning risk index; wherein the color data includes: red, yellow and green; Render the BIM model according to the color data of each unit area to obtain a hidden danger heat map; Calculate the proportion of each color in the hidden danger heat map, obtain the intensity identification result based on the proportion of each color, and issue a reminder to the administrator; the intensity identification results include: insufficient intensity, possibly insufficient intensity, and met standards.

7. A method for identifying insufficient concrete strength during construction according to claim 6, characterized in that: Also includes: If the intensity identification result is possible insufficient intensity or insufficient intensity, obtain the monitoring data of the unit area with red or yellow color data in the hidden danger heat map to obtain the monitoring data set; Obtain the safety range of each monitoring data and obtain a safety range set; When all data in the monitoring data set are not within the preset nth safety range, it is determined that the unit area has an anomaly within the nth safety range, and the cause of the anomaly is obtained; when any of the data in the monitoring data set is within the preset nth safety range, it is determined that the unit area does not have an anomaly; wherein the preset safety range set includes n preset safety ranges, each preset safety range corresponds to a fault, and n is a natural number; Clustering multiple unit areas according to abnormal causes and determining the number of unit areas in each cluster; The abnormal cause corresponding to the cluster with the largest number of unit areas is selected as the target abnormal cause; The target abnormality cause is input into the construction adjustment analysis network to generate a construction adjustment plan, and the construction adjustment plan is sent to the administrator to adjust the concrete construction process; wherein the construction analysis adjustment network includes: a first adjustment layer and a second adjustment layer.

8. A method for identifying insufficient concrete strength during construction according to claim 7, characterized in that: Input the target abnormality cause into the construction adjustment analysis network to generate a construction adjustment plan, including: Construct construction adjustment analysis network; Input the target abnormality cause into the construction adjustment analysis network to generate a construction adjustment plan; Construct a construction adjustment analysis network, including: Collecting a historical abnormal adjustment sample set based on any abnormal cause as a constraint; wherein any abnormal adjustment sample set in the historical abnormal adjustment sample set includes an abnormality detection feature and an adjustment plan; Using anomaly detection features and adjustment plans as samples, we conduct correlation analysis in the adjustment phase and establish the first adjustment layer. Using anomaly detection features and adjustment schemes as samples, we conduct correlation analysis of adjustment parameters and establish the second adjustment layer. A construction analysis adjustment network is constructed using the first adjustment layer and the second adjustment layer.

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