Underwater construction method for highway bridge

Abstract

The invention belongs to the technical field of data processing and intelligent control, and provides an underwater construction method for a highway bridge, which specifically comprises the following steps of: arranging a settlement data sensor group at the bottom of a caisson, acquiring settlement data in real time through the settlement data sensor group, calculating a settlement uniformity factor according to the acquired settlement data, and calculating the settlement uniformity factor according to the settlement uniformity factor; and finally, the current settlement state of the caisson is judged based on the settlement uniformity factor, and differential grouting control is conducted according to the settlement state. According to the method, the occurrence risk of inclination or rotation of the caisson in the caisson process caused by the difference of the compaction degree of silt in a large caisson construction scene is effectively quantified, the relative collapsing position in the whole caisson can be accurately marked, and the occurrence of local excessive settlement or inclination risk caused by uneven supporting force is effectively reduced. The timeliness and timeliness of settlement risk identification are improved, the active adjustment frequency of human participation is reduced, the construction quality and efficiency are greatly improved, and settlement fluctuation caused by estuary region characteristics is effectively dealt with.

Description

Technical Field

[0001] The invention belongs to the technical field of data processing and intelligent control, and in particular relates to an underwater construction method for a highway bridge. Background Art

[0002] The caisson method is a method of constructing highway bridges below the groundwater level. During construction, compressed air is sent into the caisson, which is shaped like a covered caisson, to drain the groundwater. Excavation is carried out on the dry soil inside the caisson, and soil is transported through a special device well. It is used for the construction of infrastructure such as bridge piers, docks, and submarine observatories. Since most of the work of the caisson method is concentrated on the prefabrication of the caisson, which is completed on land, the caisson method rarely involves underwater operations, which can greatly reduce the disturbance to the water body and reduce the impact on aquatic organisms. It is especially suitable for projects in ecologically sensitive areas.

[0003] However, the existing technology only relies on the deadweight of the caisson and local fine-tuning of manual intervention to settle the caisson. The characteristics of the loose underwater surface sediment in the estuary area and the joint scouring of river water and sea water are often ignored. This leads to the phenomenon of differential settlement risks between the various areas at the bottom of the caisson, which will further cause the caisson to tilt or the final position of the caisson to be inaccurate, and even the consequence of local instability. In the subsequent process, it is necessary to debug and correct the settlement process through repeated adjustment of manual intervention, which greatly reduces the construction efficiency and quality. This differential settlement risk occurs because the sediment in the estuary is subjected to the uneven shear force of seawater and river water for a long time, and the gaps between particles are large, and there is more interstitial water. This structure leads to a loose structure of the sediment, which makes the particles more easily moved or rearranged by external forces. The estuary area is subjected to the non-periodic joint external force scouring of seawater and river water, which makes the sediment structure in this area continue to change dynamically, which is manifested as strong instability and unevenness, and the caisson has a tendency to tilt, rotate or swing.

[0004] In an ideal settlement process, the deadweight of the caisson can compact the silt in the foundation evenly. However, the instability of the silt in actual operation cannot be predicted, and the compaction degree of the silt is uneven in different areas. When the silt is loose, the supporting force is weak; when the silt is compact, the supporting force is strong, resulting in differences in the forces acting on different areas of the caisson. These differences occur together after the caisson, causing uneven settlement of the caisson during the settlement process. If the caisson process continues to be impacted by the water flow, the phenomenon of differential settlement will be further aggravated. However, the existing technology cannot dynamically respond to the differential settlement problem caused by the difference in the degree of foundation compaction and the joint scouring of river water and seawater during the construction of estuary bridges. Therefore, there is an urgent need for a caisson construction method and system based on real-time monitoring of foundation settlement uniformity and differential grouting compensation. Summary of the invention

[0005] The purpose of the present invention is to propose an underwater construction method for a highway bridge to solve one or more technical problems existing in the prior art and at least provide a beneficial option or create conditions.

[0006] In order to achieve the above object, according to one aspect of the present invention, there is provided an underwater construction method for a highway bridge, the method comprising the following steps:

[0007] S100, deploying a settlement data sensor group at the bottom of the caisson;

[0008] S200, collecting settlement data in real time through a settlement data sensor group;

[0009] S300, calculating a settlement uniformity factor according to the collected settlement data;

[0010] S400, judging the current settlement state of the caisson based on the settlement uniformity factor;

[0011] S500, differential grouting control according to settlement status.

[0012] Furthermore, in step S100, the method of arranging the settlement data sensor group at the bottom of the caisson is: a number of settlement data sensor groups are evenly distributed at the bottom of the caisson, the number of the settlement data sensor groups is 50-200, and each settlement data sensor group includes a settlement sensor and a pressure sensor. The settlement sensor adopts an FBG Bragg fiber grating sensor by default, and the pressure sensor is any one of a capacitive pressure sensor, a piezoresistive pressure sensor or a vibrating string sensor.

[0013] In addition to arranging the settlement data sensor groups in a uniform distribution, the settlement data sensor groups can also be arranged by a spacing constraint method, that is, the minimum or maximum spacing between the settlement data sensor groups is preset, and the distance between all adjacent settlement data sensor groups is within the preset minimum spacing and maximum spacing interval.

[0014] The reason why the sedimentation sensor uses a fiber optic sensor is that its working principle uses optical fiber as the measuring medium, which can reduce direct contact with the sludge and avoid physical contamination or blockage of the sensor; the accuracy of the fiber optic sensor is not affected by electromagnetic interference and humid environment and can obtain accurate data.

[0015] Further, in step S200, the method for collecting settlement data in real time through the settlement data sensor group is: obtaining settlement data once every 30-120 seconds, and recording the time point of obtaining the settlement data as a measuring point; wherein the settlement data of each measuring point includes settlement amount and bottom pressure, wherein the settlement amount is obtained by real-time measurement of the settlement amount sensor in the settlement data sensor group, and the bottom pressure is obtained by real-time measurement of the pressure sensor.

[0016] The settlement measured by each settlement data sensor group is the local settlement, which is the sum of the vertical displacements from one measuring point to the next measuring point in the time direction. Since the compressibility, non-uniformity and stress distribution of the foundation material may vary due to geological characteristics, the local settlement can capture the true compression of the foundation soil in a small area, detect the slight differences in the settlement at each position, and thus more accurately capture the cumulative degree of settlement differences at each local point, thereby monitoring the tendency of the caisson to tilt or rotate and swing.

[0017] Further, in step S300, the method for calculating the settlement uniformity factor according to the collected settlement data is as follows: each settlement data sensor group is recorded as a measurement position, a time period is set as a monitoring period RETW, RETW∈[10,60] minutes, the settlement amount and bottom contact pressure obtained by each measuring point at each measurement position in the current monitoring period are recorded to form a measuring point feature binary sequence of the measuring point, and the interquartile range of the settlement amount in the feature binary sequence of any measuring point is calculated and recorded as the first settlement difference;

[0018] Take any measurement as the current measurement, record the settlement amount and bottom pressure obtained at each measurement point to form a measurement feature binary sequence, calculate the difference between the settlement amount of any measurement point and the first measurement point in the reverse time direction, and record it as the first sedimentation rate difference MEdrce of the measurement point, where the first sedimentation rate difference of the first measurement point in the monitoring period is 1;

[0019] All measuring points whose first settlement rate difference is greater than the first settlement difference in the current positioning are obtained and the set they form is recorded as the unbalanced measuring point set, and the union of the unbalanced measuring point sets of all positioning is recorded as the first unbalanced measuring point set; the settlement amount and bottoming pressure obtained by the current positioning in the first unbalanced measuring point set constitute the first characteristic binary sequence of the positioning, and the correlation matrix of the first characteristic binary sequence is calculated, and the binary unit eigenvector corresponding to the maximum eigenvalue of the correlation matrix is ​​recorded as the positioning load factor; the scalar product of the binary vector corresponding to the positioning characteristic binary sequence at any measuring point of the current positioning and the positioning load factor is the settlement load index SEloim of the positioning at the measuring point; the product of the natural logarithm of the settlement load index and the first settlement rate difference is defined as the settlement uniformity factor of the current positioning.

[0020] The calculation principle of the settlement load index is as follows: by observing the settlement uniformity and the trend of settlement rate change at each measuring point, the corresponding correlation weights between the settlement amount and the bottoming pressure are assigned, and the uniformity of foundation settlement at each measuring point is further accurately characterized to accurately locate the differences in settlement of the caisson in various areas, greatly improving the subsequent efficient identification of defect locations and differential grouting compensation.

[0021] Since the above-mentioned settlement load index is obtained by accurately dividing the difference level of each measurement position at any measuring point based on the settlement, and the first settlement rate difference accurately shows the trend of the settlement of each measurement position over time, the relative settlement state between each measurement position over a period of time can be characterized according to the change of the settlement rate of any measurement position and the difference with other measurement positions, which helps to quickly locate the area with significant differences; but in data scenarios where it is necessary to accurately identify small differences, it is too rough to divide only based on the attribute of settlement, and it is difficult to effectively use all the data information of all measurement positions for more accurate division, that is, the data stratification effect is not good, and the ability to differentiate the differences between data points or measurement positions is insufficient, so that the settlement load index of the same measurement position at different measurement points is insufficiently correlated. In order to obtain a more accurate settlement uniformity factor, the present invention proposes a more preferred time series analysis scheme.

[0022] Preferably, in step S300, the method for calculating the settlement uniformity factor based on the collected settlement data is: each settlement data sensor group is recorded as a measurement position, and the harmonic mean and exponential weighted mean of each settlement amount in the ATG in the reverse time direction of a measurement point are calculated, wherein the smoothing factor in the calculation of the exponential weighted mean value ranges from 0.7 to 1, and the difference between the exponential weighted mean value and the harmonic mean value is defined as the settlement phase value; wherein ATG is the average window period, the value range is 3-10 minutes, and the default value is 5 minutes; the first critical condition is set to be that the settlement phase value is less than 0, and the second critical condition is that the settlement phase value is a minimum value, and the first measurement point that satisfies both the first critical condition and the second critical condition in the reverse time direction from the current moment is searched and recorded as the critical measurement point;

[0023] In the calculation of the sedimentation phase value, since the harmonic mean has the effect of reducing extreme values ​​in rate comparison, it can reduce the swing influence between the measured data of each sedimentation quantity in the ATG, and the exponentially weighted average further enhances the data weight of the recent time period at the current moment. Therefore, the difference between the two represented by the sedimentation phase value can make it easier to distinguish whether the sedimentation amount belongs to a transition trend or a shortage trend in the sedimentation process in the subsequent time series analysis.

[0024] Define any measuring point in a measurement as a site object of the settlement data, and construct a matrix with site objects of the same measurement point but different measurement points as rows and site objects of the same measurement point but different measurement points as columns, which is recorded as the settlement derivation model SDM. The constraint condition of the number of measurement points of the settlement derivation model is that each measurement contains at least one critical measurement point; the critical number of measurement points of a measurement is recorded as the critical incidence rate;

[0025] The site object is essentially a programming object in a programming language, representing a measuring point of a certain location. The site object naturally inherits the properties of the location and measuring point where it is located. For example, the site object can obtain the average value of all settlement amounts at the same site. Here, the average value of all settlement amounts belongs to the property of the corresponding measuring point of the site object.

[0026] For any measurement position, the current measurement point is set as the critical measurement point, and the area from any critical measurement point to the first critical measurement point in the reverse time direction is taken as a derivation section. The minimum value of the distal settlement phase value and the proximal settlement phase value in the derivation section is the settlement phase value difference, the extreme difference of each bottoming pressure in the derivation section is recorded as the pressure difference, and the absolute value of the product of the settlement phase value difference and the pressure difference is recorded as the missing phase value; the distal settlement phase value and the proximal settlement phase value of the derivation section refer to the measurement point closest to the current moment and the measurement point farthest away from the current moment in the derivation section, respectively. The settlement phase value; in any derived section, the corresponding position of the derived section is the current position, and the corresponding matrix is ​​intercepted from the settlement derivation model as the sub-derivation model. In any column of the sub-derivation model, the percentile value of the bottoming pressure and the percentile value of the missing volume phase value corresponding to the current position are recorded as the pressure sub-distribution and the missing volume sub-distribution, respectively. The weighted average value of each pressure sub-distribution is calculated with the missing volume sub-distribution as the weight and recorded as the missing volume distribution value ZDst of the derived section; the settlement uniformity factor BSU is calculated according to the missing volume distribution value: BSU(r0)=lg(1+gn(FQ r0 ))mean r1∈ {e -r1 lg(len(r1)ZDst r1};

[0027] Where r0 is the number of the measurement, r1 is the number of the derivation segment, e is a natural constant, lg is a logarithmic function with base 10, len(r1) is a length reading function, which returns the number of measurement points in the r1th derivation segment, gn(r0) is a critical measurement point outlier function, which returns the difference between the critical incidence rate of the r0th measurement and the average critical incidence rate, and the average critical incidence rate refers to the average value of the critical incidence rates corresponding to all measurement positions, ZDst r1 represents the shortage distribution value of the r1th derived segment, mean r1 is the mean function of r1 as the independent variable.

[0028] Since each derivation section is divided according to the critical measuring point, each derivation section has one and only one corresponding measuring position.

[0029] Beneficial effects: Since the settlement uniformity factor is calculated based on the settlement amount and the bottoming pressure, it can effectively quantify the risk of sub-tilting or rotation in the caisson process caused by the difference in the compaction degree of mud and sand in the large-scale caisson construction scene. This risk is read by the caisson's data sensor and measured in real time. The relative collapsed position of the caisson can be accurately marked, providing more efficient and accurate defect position warning and grouting position selection for dynamic adjustment of grouting construction, thereby ensuring the stability and uniformity of the caisson settlement process; effectively reducing the risk of excessive local settlement or tilting due to uneven support force.

[0030] Further, in step S400, the method for judging the current settlement state of the caisson based on the settlement uniformity factor is as follows: the standard deviation and average value of all settlement uniformity factors obtained at a measuring point are recorded as factor deviation and factor level avg_BSU respectively; the factor level corresponding to the measuring point with the minimum factor deviation in all historical data is searched and recorded as factor level base value bs_BSU; a first threshold point rate TP_F is preset, TP_F∈[3%,7%], and a second threshold point rate is set to be 1.5-2 of the first threshold point rate. 5 times; if the factor level obtained at the current moment satisfies avg_BSU≤(1-TP_S)bs_BSU||avg_BSU≥(1+TP_S)bs_BSU, it is judged as settlement instability; if the factor level obtained at the current moment satisfies (1-TP_S)bs_BSU<avg_BSU≤(1-TP_F)bs_BSU||(1+TP_S)bs_BSU>avg_BSU≥(1+TP_F)bs_BSU, it is judged as uneven settlement.

[0031] Furthermore, in step S500, the method for performing differential grouting control according to the settlement state is: if it is determined that the caisson has settled unevenly, the settlement is suspended and an unstable settlement unevenness warning signal is sent to the administrator, the caisson is adjusted to a balanced state using ballast, and the average value of the current bottoming pressures is defined. If the measured bottoming pressure is less than the average value, and the settlement uniformity factor of the measured position is less than the factor level, the measurement is recorded as a risk measurement, and grouting compensation is performed on the risk measurement; if it is determined that the settlement is unstable, the caisson construction is immediately stopped and a settlement instability warning signal is sent to the administrator.

[0032] When grouting compensation is performed, the required grouting amount is estimated according to the supporting force difference; grouting holes are arranged in the area, and grouting is performed gradually and slowly and evenly; grouting is suspended and the real-time supporting force is monitored every time 10% of the total estimated amount is injected; if the real-time supporting force differs from the supporting force of the key area by more than 1%, grouting is continued as planned; if the real-time supporting force differs from the supporting force of the key area by less than 1%, grouting is changed to progressive reduction, that is, the grouting amount is gradually reduced to prevent excessive grouting; the above-mentioned supporting force difference can also be in the range of 0.01%-2%; when the real-time supporting force of the area differs from the supporting force of the key area by within 0.0001%, grouting is stopped and settlement is allowed to continue; the key area defaults to the position corresponding to the median value of all bottoming pressures.

[0033] Cement or concrete blocks are used as ballast because of their good stability and are not easily corroded in a wet environment. The calculation of the grouting volume is based on the foundation bearing capacity theory, and the formula V_grout=A*(q_actual-q_reference) / ΔE is usually used, where V_grout is the volume of slurry to be injected, A is the area of ​​the grouting area, q_actual is the actual measured support force, q_reference is the support force value to be adjusted, and ΔE is the modulus difference of the soil. Settlement instability requires troubleshooting at the construction site, including confirming whether there are large obstacles that have not been surveyed in advance, abnormal groundwater levels, improper operations, or abnormal equipment. Settlement can only be restarted after the cause of instability is discovered and resolved. In addition to the above reasons, attention should also be paid to unconventional phenomena such as soil liquefaction caused by special soils, insufficient shear strength of the soil layer, or freeze-thaw cycles caused by cold regions. Therefore, it is generally believed that it cannot be simply solved by differential grouting compensation, but needs to be investigated and handled separately to avoid causing more serious structural damage.

[0034] Preferably, all undefined variables in the present invention, if not clearly defined, can be manually set thresholds.

[0035] The present invention also provides an underwater construction system for a highway bridge, the underwater construction system for a highway bridge comprising: a processor, a memory, and a computer program stored in the memory and executable on the processor, the processor implementing the steps in the underwater construction method for a highway bridge when executing the computer program, the underwater construction system for a highway bridge can be run in computing devices such as desktop computers, laptop computers, PDAs, and cloud data centers, the executable system may include, but is not limited to, a processor, a memory, and a server cluster, the processor executing the computer program runs in the following system units:

[0036] A caisson sensor arrangement unit is used to arrange a settlement data sensor group at the bottom of the caisson;

[0037] A data acquisition unit, used for collecting settlement data in real time through a settlement data sensor group;

[0038] Uniform settlement derivation unit, used to calculate the settlement uniformity factor based on the collected settlement data;

[0039] A settlement state judgment unit, used to judge the current settlement state of the caisson based on the settlement uniformity factor;

[0040] Grouting control unit, used for differential grouting control according to settlement status.

[0041] The beneficial effects of the present invention are as follows: since the settlement uniformity factor is calculated based on the settlement amount and the bottoming pressure, it can effectively quantify the risk of sub-tilting or rotation of the caisson process caused by the difference in the degree of compaction of the silt in the large-scale caisson construction scene. This risk is measured in real time by reading real-time data from the data sensor of the caisson, and can accurately mark the relative collapsed position of the caisson as a whole, providing more efficient and accurate defect position warning and grouting position selection for dynamic adjustment of grouting construction, thereby ensuring the stability and uniformity of the caisson settlement process; effectively reducing the occurrence of the risk of excessive local settlement or tilting caused by uneven support force. It improves the timeliness and promptness of identifying settlement risks, reduces the frequency of active adjustments involving human participation, and greatly improves the construction quality and efficiency; effectively responds to the settlement fluctuations caused by the characteristics of the estuary area, and achieves the effect of improving construction stability and improving construction quality. BRIEF DESCRIPTION OF THE DRAWINGS

[0042] The above and other features of the present invention will become more obvious by describing in detail the embodiments shown in the accompanying drawings. The same reference numerals in the accompanying drawings of the present invention represent the same or similar elements. Obviously, the accompanying drawings described below are only some embodiments of the present invention. For those skilled in the art, other accompanying drawings can be obtained based on these accompanying drawings without creative work. In the accompanying drawings:

[0043] Figure 1 Shown is a flow chart of an underwater construction method for a highway bridge;

[0044] Figure 2 Shown is a structural diagram of an underwater construction system for highway bridges. DETAILED DESCRIPTION

[0045] The following will be combined with the embodiments and drawings to clearly and completely describe the concept, specific structure and technical effects of the present invention, so as to fully understand the purpose, scheme and effect of the present invention. It should be noted that the embodiments and features in the embodiments of this application can be combined with each other without conflict.

[0046] like Figure 1 The figure shows a flow chart of an underwater construction method for a highway bridge. Figure 1 To illustrate an underwater construction method for a highway bridge according to an embodiment of the present invention, the method comprises the following steps:

[0047] Example 1

[0048] S100, deploying a settlement data sensor group at the bottom of the caisson;

[0049] S200, collecting settlement data in real time through a settlement data sensor group;

[0050] S300, calculating a settlement uniformity factor according to the collected settlement data;

[0051] S400, judging the current settlement state of the caisson based on the settlement uniformity factor;

[0052] S500, differential grouting control according to settlement status.

[0053] Further, in step S100, the method of arranging the settlement data sensor group at the bottom of the caisson is: a number of settlement data sensor groups are evenly distributed at the bottom of the caisson, the number of settlement data sensor groups is 120, and each settlement data sensor group includes a settlement amount sensor and a pressure sensor. The settlement amount sensor adopts an FBG Bragg fiber grating sensor by default, and the pressure sensor is a capacitive pressure sensor.

[0054] Further, in step S200, the method for collecting settlement data in real time through the settlement data sensor group is: obtaining settlement data once every 30-120 seconds, and recording the time point of obtaining the settlement data as a measuring point; wherein the settlement data of each measuring point includes settlement amount and bottom pressure, wherein the settlement amount is obtained by real-time measurement of the settlement amount sensor in the settlement data sensor group, and the bottom pressure is obtained by real-time measurement of the pressure sensor.

[0055] Further, in step S300, the method for calculating the settlement uniformity factor according to the collected settlement data is as follows: each settlement data sensor group is recorded as a measurement position, a time period is set as a monitoring period RETW, and the value is taken as 20 minutes, and the settlement amount and bottom contact pressure obtained by each measuring point at each measurement position in the current monitoring period are recorded to form a measuring point feature binary sequence of the measuring point, and the interquartile range of the settlement amount in the feature binary sequence of any measuring point is calculated and recorded as the first settlement difference;

[0056] Take any measurement as the current measurement, record the settlement amount and bottom pressure obtained at each measurement point to form a measurement feature binary sequence, calculate the difference between the settlement amount of any measurement point and the first measurement point in the reverse time direction, and record it as the first settlement rate difference MEdrce of the measurement point;

[0057] All the measuring points whose first settlement rate difference is greater than the first settlement difference in the current positioning are obtained and the set is recorded as the unbalanced measuring point set, and the union of the unbalanced measuring point sets of all positioning is recorded as the first unbalanced measuring point set; the settlement amount and bottoming pressure obtained by the current positioning in the first unbalanced measuring point set constitute the first characteristic binary sequence of the positioning, calculate the correlation matrix of the first characteristic binary sequence, and record the binary unit eigenvector corresponding to the maximum eigenvalue of the correlation matrix as the positioning load factor; the scalar product of the binary vector corresponding to the positioning characteristic binary sequence at any measuring point of the current positioning and the positioning load factor is the settlement load index SEloim of the positioning at the measuring point; the product of the natural logarithm of the settlement load index and the first settlement rate difference is defined as the settlement uniformity factor of the current positioning. That is, BSU = MEdrce × ln (SEloim), ln () is a logarithmic function with the natural constant e as the base.

[0058] Further, in step S400, the method for judging the current settlement state of the caisson based on the settlement uniformity factor is as follows: the standard deviation and the average value of all settlement uniformity factors obtained from a measuring point are recorded as the factor deviation and the factor level avg_BSU respectively, and the factor level corresponding to the measuring point with the minimum factor deviation in all historical data is searched and recorded as the factor level base value bs_BSU; the first threshold point rate TP_F is preset to be 4%, and the second threshold point rate is set to be twice the first threshold point rate; if the factor level obtained at the current moment satisfies avg_BSU≤(1-TP_S)bs_BSU||avg_BSU≥(1+TP_S)bs_BSU, it is judged as settlement instability; if the factor level obtained at the current moment satisfies (1-TP_S)bs_BSU<avg_BSU≤(1-TP_F)bs_BSU||(1+TP_S)bs_BSU>avg_BSU≥(1+TP_F)bs_BSU, it is judged as uneven settlement.

[0059] Furthermore, in step S500, the method for performing differential grouting control according to the settlement state is: if it is determined that the caisson has settled unevenly, the settlement is suspended and an unstable settlement unevenness warning signal is sent to the administrator, the caisson is adjusted to a balanced state using ballast, and the average value of the current bottoming pressures is defined. If the measured bottoming pressure is less than the average value, and the settlement uniformity factor of the measured position is less than the factor level, the measurement is recorded as a risk measurement, and grouting compensation is performed on the risk measurement; if it is determined that the settlement is unstable, the caisson construction is immediately stopped and a settlement instability warning signal is sent to the administrator.

[0060] When grouting compensation is performed, the required amount of grouting is estimated based on the difference in supporting force; grouting holes are arranged in the area, and grouting is performed gradually and slowly and evenly; grouting is suspended every time 10% of the total estimated amount is injected and the real-time supporting force is monitored; if the real-time supporting force differs from the supporting force of the critical area by more than 1%, grouting is continued as planned; if the real-time supporting force differs from the supporting force of the critical area by less than 1%, grouting is performed in a progressively reduced amount, that is, the grouting amount is gradually reduced to prevent excessive grouting; when the real-time supporting force of the area differs from the supporting force of the critical area by within 0.0001%, grouting is stopped and settlement is allowed to continue; the critical area defaults to the position corresponding to the median value of all bottoming pressures.

[0061] Cement or concrete blocks are used as ballast because of their good stability and are not easily corroded in a wet environment. The calculation of the grouting volume is based on the foundation bearing capacity theory, and the formula V_grout=A*(q_actual-q_reference) / ΔE is usually used, where V_grout is the volume of slurry to be injected, A is the area of ​​the grouting area, q_actual is the actual measured support force, q_reference is the support force value to be adjusted, and ΔE is the modulus difference of the soil. Settlement instability requires troubleshooting at the construction site, including confirming whether there are large obstacles that have not been surveyed in advance, abnormal groundwater levels, improper operations, or abnormal equipment. Settlement can only be restarted after the cause of instability is discovered and resolved. In addition to the above reasons, attention should also be paid to unconventional phenomena such as soil liquefaction caused by special soils, insufficient shear strength of the soil layer, or freeze-thaw cycles caused by cold regions. Therefore, it is generally believed that it cannot be simply solved by differential grouting compensation, but needs to be investigated and handled separately to avoid causing more serious structural damage.

[0062] Example 2

[0063] Embodiment 2 adopts the underwater construction method of highway bridges that is the same as that of Embodiment 1, and the difference is that, in step S300, the method for calculating the settlement uniformity factor based on the collected settlement data is as follows: each settlement data sensor group is recorded as a measurement position, and the harmonic mean and exponential weighted mean of each settlement amount in the ATG in the reverse time direction of a measurement point are calculated, wherein the smoothing factor in the calculation of the exponential weighted mean is taken as 0.8, and the difference between the exponential weighted mean and the harmonic mean is defined as the settlement phase value; wherein ATG is the average window period, and the value is 5 minutes; the first critical condition is set to be that the settlement phase value is less than 0, and the second critical condition is that the settlement phase value is a minimum value, and the first measurement point that satisfies both the first critical condition and the second critical condition is searched in the reverse time direction from the current moment and is recorded as the critical measurement point;

[0064] Define any measuring point in a measurement as a site object of the settlement data, and construct a matrix with site objects of the same measurement point but different measurement points as rows and site objects of the same measurement point but different measurement points as columns, which is recorded as the settlement derivation model SDM. The constraint condition of the number of measurement points of the settlement derivation model is that each measurement contains at least one critical measurement point; the critical number of measurement points of a measurement is recorded as the critical incidence rate;

[0065] For any measurement position, the current measurement point is set as the critical measurement point, and the area from any critical measurement point to the first critical measurement point in the reverse time direction is taken as a derivation section. The minimum value of the distal settlement phase value and the proximal settlement phase value in the derivation section is the settlement phase value difference, the extreme difference of each bottoming pressure in the derivation section is recorded as the pressure difference, and the absolute value of the product of the settlement phase value difference and the pressure difference is recorded as the missing phase value; the distal settlement phase value and the proximal settlement phase value of the derivation section refer to the measurement point closest to the current moment and the measurement point farthest away from the current moment in the derivation section, respectively. The settlement phase value; in any derived section, the corresponding position of the derived section is the current position, and the corresponding matrix is ​​intercepted from the settlement derivation model as the sub-derivation model. In any column of the sub-derivation model, the percentile value of the bottoming pressure and the percentile value of the missing volume phase value corresponding to the current position are recorded as the pressure sub-distribution and the missing volume sub-distribution, respectively. The weighted average value of each pressure sub-distribution is calculated with the missing volume sub-distribution as the weight and recorded as the missing volume distribution value ZDst of the derived section; the settlement uniformity factor BSU is calculated according to the missing volume distribution value: BSU(r0)=lg(1+gn(FQ r0 ))mean r1∈ {e -r1 lg(len(r1)ZDst r1};

[0066] Where r0 is the number of the measurement, r1 is the number of the derivation segment, e is a natural constant, lg is a logarithmic function with base 10, len(r1) is a length reading function, which returns the number of measurement points in the r1th derivation segment, gn(r0) is a critical measurement point outlier function, which returns the difference between the critical incidence rate of the r0th measurement and the average critical incidence rate, and the average critical incidence rate refers to the average value of the critical incidence rates corresponding to all measurement positions, ZDst r1 represents the shortage distribution value of the r1th derived segment, mean r1 is the mean function of r1 as the independent variable.

[0067] An embodiment of the present invention provides an underwater construction system for a highway bridge, such as Figure 2Shown is a structural diagram of an underwater construction system for a highway bridge of the present invention. The underwater construction system for a highway bridge of this embodiment includes: a processor, a memory, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, the steps in the above-mentioned embodiment of an underwater construction method for a highway bridge are implemented.

[0068] The system comprises: a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to run in the following units of the system:

[0069] A caisson sensor arrangement unit is used to arrange a settlement data sensor group at the bottom of the caisson;

[0070] A data acquisition unit, used for collecting settlement data in real time through a settlement data sensor group;

[0071] Uniform settlement derivation unit, used to calculate the settlement uniformity factor based on the collected settlement data;

[0072] A settlement state judgment unit, used to judge the current settlement state of the caisson based on the settlement uniformity factor;

[0073] Grouting control unit, used for differential grouting control according to settlement status.

[0074] The underwater construction system for highway bridges can be run in computing devices such as desktop computers, laptops, PDAs, and cloud servers. The underwater construction system for highway bridges can include, but is not limited to, processors and memories. Those skilled in the art will appreciate that the example is merely an example of an underwater construction system for highway bridges and does not constitute a limitation on an underwater construction system for highway bridges. It can include more or fewer components than the example, or a combination of certain components, or different components. For example, the underwater construction system for highway bridges can also include input and output devices, network access devices, buses, etc.

[0075] The processor may be a central processing unit (CPU), or other general-purpose processors, digital signal processors (DSP), application-specific integrated circuits (ASIC), field-programmable gate arrays (FPGA) or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. The general-purpose processor may be a microprocessor or any conventional processor, etc. The processor is the control center of the underwater construction system for highway bridges, and uses various interfaces and lines to connect the various parts of the entire underwater construction system for highway bridges.

[0076] The memory can be used to store the computer program and / or module, and the processor realizes various functions of the underwater construction system for highway bridges by running or executing the computer program and / or module stored in the memory and calling the data stored in the memory. The memory can mainly include a program storage area and a data storage area, wherein the program storage area can store an operating system, an application required for at least one function (such as a sound playback function, an image playback function, etc.), etc.; the data storage area can store data created according to the use of the mobile phone (such as audio data, a phone book, etc.), etc. In addition, the memory can include a high-speed random access memory, and can also include a non-volatile memory, such as a hard disk, a memory, a plug-in hard disk, a smart memory card (Smart Media Card, SMC), a secure digital (SecureDigital, SD) card, a flash card (Flash Card), at least one disk storage device, a flash memory device, or other volatile solid-state storage devices.

[0077] Although the description of the present invention has been quite detailed and has been described in particular with respect to several described embodiments, it is not intended to be limited to any of these details or embodiments or any particular embodiment, so as to effectively cover the intended scope of the present invention. In addition, the present invention is described above with the embodiments foreseeable by the inventors, and its purpose is to provide a useful description, and those non-substantial changes to the present invention that are not currently foreseen may still represent equivalent changes of the present invention.

Claims

1. An underwater construction method for a highway bridge, characterized in that: The method comprises the following steps: S100, deploying a settlement data sensor group at the bottom of the caisson; S200, collecting settlement data in real time through a settlement data sensor group; S300, calculating a settlement uniformity factor according to the collected settlement data; S400, judging the current settlement state of the caisson based on the settlement uniformity factor; S500, differential grouting control according to settlement status; The method for calculating the settlement uniformity factor based on the collected settlement data is to obtain the bottoming pressure and settlement amount from the settlement data, construct a measuring point feature binary sequence according to the bottoming pressure and settlement amount, obtain the first settlement difference and identify the unbalanced measuring point set using the measuring point feature binary sequence, construct the first feature binary sequence and construct a correlation matrix using the unbalanced measuring point set, calculate the settlement load index according to the correlation matrix, and finally calculate the settlement uniformity factor through the settlement load index and the first settlement rate difference.

2. The underwater construction method for a highway bridge according to claim 1, characterized in that: In step S100, the method of arranging the settlement data sensor group at the bottom of the caisson is: a number of settlement data sensor groups are evenly distributed at the bottom of the caisson, the number of the settlement data sensor groups is 50-200, each settlement data sensor group includes a settlement sensor and a pressure sensor, the settlement sensor adopts an FBG Bragg fiber grating sensor by default, and the pressure sensor is any one of a capacitive pressure sensor, a piezoresistive pressure sensor or a vibrating string sensor.

3. The underwater construction method for a highway bridge according to claim 1, characterized in that: In step S200, the method for collecting settlement data in real time through the settlement data sensor group is: acquiring settlement data once every 30-120 seconds, and recording the time point of obtaining the settlement data as a measuring point; wherein the settlement data of each measuring point includes settlement amount and bottom pressure, wherein the settlement amount is obtained by real-time measurement of the settlement amount sensor in the settlement data sensor group, and the bottom pressure is obtained by real-time measurement of the pressure sensor.

4. The underwater construction method for a highway bridge according to claim 1, characterized in that: In step S300, the method for calculating the settlement uniformity factor based on the collected settlement data is as follows: each settlement data sensor group is recorded as a measurement position, a time period is set as a monitoring period RETW, RETW∈[10,60] minutes, the settlement amount and bottom contact pressure obtained at each measurement point at each measurement position in the current monitoring period are recorded to form a measurement point feature binary sequence of the measurement point, and the interquartile range of the settlement amount in any measurement point feature binary sequence is calculated and recorded as the first settlement difference; Take any measurement as the current measurement, record the settlement amount and bottoming pressure obtained at each measurement point to form a measurement feature binary sequence, calculate the difference in settlement amount between any measurement point and the first measurement point in the reverse time direction, and record it as the first settlement rate difference of the measurement point; obtain all measurement points in the current measurement whose first settlement rate difference is greater than the first settlement difference and form a set recorded as the unbalanced measurement point set, and record the union of the unbalanced measurement point sets of all measurements as the first unbalanced measurement point set; the settlement amount and bottoming pressure obtained by the current measurement in the first unbalanced measurement point set constitute the first feature binary sequence of the measurement, calculate the correlation matrix of the first feature binary sequence, and record the binary unit eigenvector corresponding to the maximum eigenvalue of the correlation matrix as the measurement load factor; the scalar product of the binary vector corresponding to the current measurement at any measurement point in the measurement feature binary sequence and the measurement load factor is the settlement load index of the measurement at the measurement point; define the product of the natural logarithm of the settlement load index and the first settlement rate difference as the settlement uniformity factor of the current measurement.

5. The underwater construction method for a highway bridge according to claim 1, characterized in that: In step S300, the method for calculating the settlement uniformity factor based on the collected settlement data can be replaced by: each settlement data sensor group is recorded as a measurement position, and the harmonic mean and exponentially weighted average of each settlement amount in the ATG in the reverse time direction of a measurement point are calculated, wherein the smoothing factor in the calculation of the exponentially weighted average value is in the range of 0.7-1, and the difference between the exponentially weighted average value and the harmonic mean value is defined as the settlement phase value; wherein ATG is the average window period, the value range is 3-10 minutes, and the default value is 5 minutes; the first critical condition is set to that the settlement phase value is less than 0, and the second critical condition is that the settlement phase value is a minimum value, and the first measurement point that satisfies both the first critical condition and the second critical condition in the reverse time direction from the current moment is searched and recorded as the critical measurement point; Define any measuring point in a measurement as a site object of the settlement data, and construct a matrix with site objects of the same measurement point but different measurement points as rows and site objects of the same measurement point but different measurement points as columns, which is recorded as the settlement derivation model SDM. The constraint condition of the number of measurement points of the settlement derivation model is that each measurement contains at least one critical measurement point; the critical number of measurement points of a measurement is recorded as the critical incidence rate; For any measurement position, the current measurement point is set as the critical measurement point, and the area from any critical measurement point to the first critical measurement point in the reverse time direction is taken as a derivation section. The minimum value of the distal settlement phase value and the proximal settlement phase value in the derivation section is the settlement phase value difference, the extreme difference of each bottoming pressure in the derivation section is recorded as the pressure difference, and the absolute value of the product of the settlement phase value difference and the pressure difference is recorded as the missing phase value; the distal settlement phase value and the proximal settlement phase value of the derivation section refer to the measurement point closest to the current time in the derivation section. and the settlement phase values ​​corresponding to the measuring points farthest away; in any derivation section, the corresponding measurement position of the derivation section is the current measurement position, and the corresponding matrix is ​​intercepted from the settlement derivation model as the sub-derivation model. In any column of the sub-derivation model, the percentile value of the bottom pressure and the percentile value of the missing quantity phase value corresponding to the current measurement position are recorded as pressure sub-distribution and missing quantity sub-distribution, respectively. The weighted average of each pressure sub-distribution is calculated with the missing quantity sub-distribution as the weight and recorded as the missing quantity distribution value of the derivation section. The settlement uniformity factor is calculated according to the missing quantity distribution value.

6. The underwater construction method for a highway bridge according to claim 1, characterized in that: In step S400, the method for judging the current settlement state of the caisson based on the settlement uniformity factor is as follows: the standard deviation and the average value of all settlement uniformity factors obtained from a measuring point are recorded as the factor deviation and the factor level avg_BSU respectively, and the factor level corresponding to the measuring point with the minimum factor deviation in all historical data is searched and recorded as the factor level base value bs_BSU; a first threshold point rate TP_F is preset, TP_F∈[3%,7%), and a second threshold point rate is set to be 1.5-2.5 times of the first threshold point rate; if the factor level obtained at the current moment satisfies avg_BSU≤(1-TP_S)bs_BSU||avg_BSU≥(1+TP_S)bs_BSU, it is judged as settlement instability; if the factor level obtained at the current moment satisfies (1-TP_S)bs_BSU<avg_BSU≤(1-TP_F)bs_BSU||(1+TP_S)bs_BSU>avg_BSU≥(1+TP_F)bs_BSU, it is judged as uneven settlement.

7. The underwater construction method for a highway bridge according to claim 1, characterized in that: In step S500, the method of performing differential grouting control according to the settlement state is: if it is determined that the caisson has settled unevenly, the settlement is suspended and an unstable settlement unevenness warning signal is sent to the administrator, the caisson is adjusted to a balanced state using ballast, and the average value of the current bottoming pressures is defined. If the measured bottoming pressure is less than the average value, and the settlement uniformity factor of the measured position is less than the factor level, the measurement is recorded as a risk measurement, and grouting compensation is performed on the risk measurement; if it is determined that the settlement is unstable, the caisson construction is immediately stopped and a settlement instability warning signal is sent to the administrator.

8. An underwater construction system for a highway bridge, characterized in that: The underwater construction system for highway bridges comprises: a processor, a memory, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, the steps of the underwater construction method for highway bridges described in any one of claims 1 to 7 are implemented. The underwater construction system for highway bridges runs on a desktop computer, a laptop computer, a PDA, and a computing device in a cloud data center.

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