CN116227931A - Prediction method and system for collapse risk of side slope of deep foundation pit of building - Google Patents
Prediction method and system for collapse risk of side slope of deep foundation pit of building Download PDFInfo
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Abstract
The invention belongs to the field of building safety, and particularly relates to a prediction method and a prediction system for a side slope collapse risk of a deep foundation pit of a building. The prediction method comprises the following steps: s1: and (5) defining a data sampling area and a data sampling block around the foundation pit. S2: and arranging measuring points for acquiring different types of sample data according to a preset sampling standard. S3: corresponding sample data at each measurement point is collected and stored. S4: and generating risk state evaluation values of the test blocks. The risk evaluation value is mainly obtained by carrying out normalization and fusion treatment on temperature comparability deviation, moisture content deviation, stress deviation and pressure deviation; s5: calculating the dispersion of risk state evaluation values of all the test blocks, and generating a prediction result for evaluating collapse risk. The prediction system comprises an on-line monitoring mechanism for soil humidity, temperature, pit wall pressure and anchor cable stress, a data processing module and an alarm. The method solves the problems that the collapse risk of the deep foundation pit engineering of a large building is difficult to predict, the potential safety hazard is difficult to check in time and the like.
Description
Technical Field
The invention belongs to the field of building safety, and particularly relates to a prediction method and a prediction system for a side slope collapse risk of a deep foundation pit of a building.
Background
The foundation pit is a soil pit excavated at a foundation design position according to the elevation of a substrate and the plane size of the foundation in the building engineering. Foundation pits are required to be excavated in building engineering such as houses, bridges, tunnels and the like. Before the foundation pit is excavated, an excavation scheme is determined according to geological hydrologic data and the conditions of buildings nearby the site, and waterproof and drainage work is performed. The foundation pit which is completed needs to be protected by adopting a supporting structure, so that collapse of the foundation pit is avoided. The method can be used for releasing a slope for a shallower foundation pit to stabilize the soil slope, and the gradient of the soil slope is determined according to relevant construction regulations. For those with deeper excavation and nearby buildings, a foundation pit wall supporting method and a concrete spraying wall protecting method can be used. The large foundation pit even needs to adopt methods such as underground continuous walls, column-type bored piles for interlocking and the like, so as to prevent the collapse of the soil layer at the outer side.
Deep foundation pits generally excavate foundation pits with a depth of greater than or equal to 5m, and supporting structures are usually required to be arranged on the foundation pits. According to relevant regulations, foundation pit engineering with excavation depth exceeding 5m or excavation depth not exceeding 5m and complex field geological conditions and surrounding environment is subjected to foundation pit engineering monitoring; to prevent occurrence of foundation pit collapse event.
The technical scheme adopted by the existing foundation pit engineering slope monitoring is that a displacement sensor or other mechanical sensors are arranged at key nodes of a slope protection structure, the stress state and deformation conditions of all positions around a foundation pit are dynamically detected, and the slope stability is monitored. The foundation pit safety monitoring scheme has the advantages that the appearance change of the foundation pit in the construction process can be detected very intuitively, and whether the collapse risk exists in the foundation pit is further determined. The defect is that the monitoring result has hysteresis, namely the scheme can give early warning only after the pit engineering has generated collapse hidden trouble and has obvious degradation trend. Considering that accidents such as foundation pit collapse and the like have the characteristics of high occurrence speed, high hazard and the like, the traditional monitoring scheme can only provide limited disaster response time for owners, so that personnel safety risks caused by foundation pit collapse are avoided, and economic losses are reduced as much as possible. But can not help industry masters to check potential safety hazards in time and stop occurrence of foundation pit collapse events.
Furthermore, special emphasis is required: the collapse of the foundation pit is caused by a plurality of factors, such as whether the design of the supporting structure is reasonable, whether the construction measures are correct, the complexity of the construction environment, abnormal weather and hydrologic conditions, seasons and environmental temperature, the type of soil and the like. The foundation pit collapse accident is equivalent to a chaotic event, and has high predictability. Therefore, how to effectively monitor the stability of foundation pit engineering by using multiple data, discover early features of collapse accidents in time, and give out risk prediction results with reference values is becoming a technical problem to be solved by those skilled in the art.
Disclosure of Invention
The invention provides a prediction method and a prediction system for collapse risk of a deep foundation pit slope of a building, which aim to solve the problems that collapse risk of the deep foundation pit engineering of a large building is difficult to predict, potential safety hazards cannot be timely checked and the like.
The invention is realized by adopting the following technical scheme:
a prediction method for collapse risk of a deep foundation pit slope of a building is used for monitoring an earth slope containing anchoring measures in engineering construction, predicting collapse risk possibly in time and assisting in checking potential safety hazards.
The prediction method provided by the invention comprises the following steps:
s1: and (3) taking the outward 5m of the edge of the foundation pit as a far limit, defining a region along the edge of the foundation pit as a near limit, and uniformly dividing the region into a plurality of measuring blocks which are distributed in a radial manner in the center according to the length and the shape of the outline of the foundation pit.
S2: and arranging measuring points for acquiring different types of sample data in the measuring area according to a preset sampling standard. The types of the obtained sample data comprise soil temperature t, soil water content waf, anchor cable stress f and pit wall pressure p.
S3: and respectively acquiring corresponding sample data at each measuring point according to a preset sampling period, and recording historical data of the sample data of each measuring point.
S4: after at least one round of updating is completed on all types of sample data in any one test block, all the sample data in the current period are formed into a test block data packet. And calculating a risk state evaluation value corresponding to each block according to the block data packet.
The risk status evaluation value is calculated as follows:
s41: calculating the temperature same ratio deviation Dt of the current state value of each soil temperature measuring point in the measuring block and the average value of the same time in the past three days; the calculation formula is as follows:
in the above, t i The soil temperature at the current moment of the ith measuring point in the current measuring block is represented; n1 is the number of soil temperature measuring points in the current measuring block; tm (tm) i The average value of the soil temperature at the same time of three days in the past of the current measuring point is obtained.
S42: calculating the increase of the water content of soil of different depth measuring points in the measuring block compared with a reference position reference value, and calculating the corresponding water content deviation Dwaf: the calculation formula is as follows:
in the above-mentioned method, the step of,m1 represents the corresponding depth of the measuring points of different soil water contents; waf i The average value of the soil moisture content of all measuring points with the depth of i in the current measuring block is represented; waf0 i A reference value of the water content of the soil at a depth i, epsilon i And representing corresponding risk coefficients when the soil moisture contents at different depths i are supersaturated.
S43: calculating the increment of the average value of the anchor cable stress of each measuring point in the measuring block relative to the initial value in the current period, and calculating the stress offset Df in the measuring block; the calculation formula is as follows:
in the above formula, n2 represents the number of measuring points of the anchor rope stress in the current measuring block; ft (ft) i A measured value of the current moment of the stress measuring point of the ith anchor cable is represented; f0 of i The initial value of the stress measuring point of the ith anchor cable is shown.
S44: calculating the increasing rate of the average value of pit wall pressure of each measuring point in the measuring block relative to the initial value in the current sampling period, and calculating the pressure deviation Dp in the measuring block; the calculation formula is as follows:
in the above formula, m2 represents the depth corresponding to the measuring point of the pit wall pressure; p is p i Representing pit wall pressure at the depth i in the current measured block; p0 i The depth is the initial value of the pit wall pressure at i.
S45: respectively to the temperature and the same ratio deviation D t And carrying out normalization processing on the water content deviation Dwaf, the stress deviation Df and the pressure deviation Dp, and then fusing to obtain a corresponding risk state evaluation value Ass. The calculation formula is as follows:
Ass＝α·Dt′+β·Dwaf′+γ·Df′+λ·Dp′
in the above formula, dt ', dwaf', df 'and Dp' are normalized values of Dt, dwaf, df and Dp, respectively; alpha, beta, gamma and lambda are respectively the influence weights of the changes of four indexes of preset soil temperature, soil moisture content, anchor cable stress and pit wall pressure on the collapse risk of the foundation pit; and satisfies the following: alpha+beta+gamma+lambda is less than or equal to 1.
S5: calculating the dispersion of risk state evaluation values Ass of all the test pieces, and generating one prediction result Y for collapse risk according to the dispersion: wherein, when y=1, it indicates that there is a risk of collapse; when Y is less than or equal to 1, no collapse risk is indicated.
As a further improvement of the present invention, in step S1, the method for dividing the measurement area and the measurement block is as follows:
s11: and acquiring the profile characteristics of the upper edge of the foundation pit by a orthographic method.
S12: and fitting the outer contour of the upper edge of the foundation pit by adopting a polygon fitting algorithm to obtain the polygon with the minimum edge number, the coincidence rate of which is not lower than 95%.
S13: segmenting the edge of the upper opening of the foundation pit, wherein the segmentation standard is as follows: different edges are divided into different segments; the same side is uniformly segmented and the single segment length is not more than 60m.
S14: directing rays to each sectional boundary point by taking the polygon center of the foundation pit as an end point; then, taking the edge of the foundation pit as a near boundary and the edge of the foundation pit outwards by 5m as a far boundary, taking two adjacent rays as two side boundaries, and taking the entity space which is corresponding to each independent enclosing area and downwards reaches the maximum depth of the foundation pit as each measuring block.
As a further improvement of the present invention, the distribution states of the measurement points of the different types of sample data in step S2 are as follows:
(1) The measuring points of the soil temperature are uniformly distributed in the range from the earth surface to the soil depth of 2m in the measuring block; the distribution density of the measurement points is not less than 200m 3 Each one of which.
(2) The measuring points of the water content of the soil are divided into a surface layer type measuring point and a deep layer type measuring point; the surface layer type measuring points are distributed in the range from the earth surface to the soil depth of 2m in the measuring block; the deep layers are distributed in the range from the soil depth below 2m to the maximum depth of the foundation pit, and are uniformly distributed on layers with different depths at intervals of 5 m.
Wherein the distribution density of the measuring points corresponding to each depth layer in the surface layer type and the depth layer type is not less than 400m 3 Each one of which.
(3) The measuring points of the anchor rope stress are distributed on slope protection structures corresponding to slopes in all the measuring blocks, and at least 4 anchor rods are selected from the slope protection structures of all the measuring blocks to serve as the measuring points of the anchor rope stress.
(4) The measuring points of the pit wall pressure are distributed on the longitudinal interfaces of the slope protection structure and the soil body in each measuring block and are distributed downwards in sequence according to the depth interval of 3 m.
As a further improvement of the present invention, tm in step S41 i The value of (2) is set manually in the initial state, and the historical data collected in the step S3 is updated autonomously after meeting the requirements.
As a further improvement of the present invention, in step S42, waf0 i The measurement method of the value of (2) is as follows: selecting a non-building area with the same location and the soil surface subjected to hardening treatment as a reference area, taking a position group corresponding to the depth in the reference area as a reference measuring point, and carrying out actual measurement on the soil moisture content of the reference measuring point to obtain a reference value waf0 of the soil moisture content of the position with the required depth of i i 。
As a further improvement of the invention, in step S43, the anchor cable stress acting on any one anchor cable is axial tensile stress, and the initial value N of the axial tensile stress k Satisfies the following formula:
in the above, H k Representing the standard value of the horizontal tension of the anchor rod; θ represents the horizontal tilt angle of the anchor rod.
As a further improvement of the present invention, in step S45, the following normalization formula is used to compare the same deviation D t Normalizing four parameters of the water content deviation Dwaf, the stress deviation Df and the pressure deviation Dp:
in the above formula, x represents the original value of each parameter, x'Representing the normalized value of x; x is x max And x min Respectively representing the maximum value and the minimum value of each parameter; wherein x is max And x min The initial value of (2) is initialized and set by expert according to experience, and when any measured value exceeds the initial value in the state of not triggering collapse early warning, the measured upper limit value and lower limit value are adopted to count x max Or x min And updating.
As a further improvement of the present invention, in step S5, the generating function of the prediction result is as follows:
in the above, ass i A risk status evaluation value indicating an i-th block; k represents the number of measurement blocks in the measurement region; μ represents the average value of risk state evaluation values of all the test pieces; sigma represents the standard deviation of risk state evaluation values of all the test blocks; sigma (sigma) 0 Representing a preset collapse risk threshold.
As a further improvement of the present invention, when there is a risk of collapse, the following method is used to determine the source of the anomaly data that causes the risk:
(1) Calculating the discrete influence delta sigma of each measured block by adopting the following method i ；
Δσ i ＝|σ k -σ k-i |
In the above, sigma k A standard deviation representing risk status evaluation values of all test blocks including the i-th test block class; sigma (sigma) k-i The standard deviation of risk state evaluation values of all the blocks except the i-th block is represented.
(2) Discrete influence degree DeltaSigma calculated for each measurement block i And sequencing to obtain an influence queue.
(3) And selecting a plurality of side blocks in front in the influence degree queue as key areas for hidden trouble investigation.
The invention further comprises a prediction system for the collapse risk of the side slope of the deep foundation pit of the building, which is applied to on-line monitoring of the collapse risk of the foundation pit or the artificially constructed slope protection structure excavated in the building engineering, and timely early warning when the collapse risk exists. The prediction system includes: the device comprises a soil humidity on-line monitoring mechanism, a soil temperature on-line monitoring mechanism, a pit wall pressure on-line monitoring mechanism, an anchor cable stress on-line monitoring mechanism, a data processing module and an alarm.
The soil humidity online monitoring mechanism completes deployment in a layout mode in the prediction method of the side slope collapse risk of the deep foundation pit of the building; the soil humidity on-line monitoring mechanism is used for collecting the real-time soil moisture content of each measuring point in the building area in real time and collecting the real-time soil moisture content of the reference measuring point.
The soil temperature on-line monitoring mechanism adopts a layout mode in the prediction method of the side slope collapse risk of the deep foundation pit of the building to complete deployment. The soil temperature on-line monitoring mechanism is used for collecting the real-time temperature of the shallow soil around the side slope.
The pit wall pressure on-line monitoring mechanism is used for collecting interface pressure values corresponding to different depths along the longitudinal interface of the soil body and the slope protection structure in each area. The anchor cable stress on-line monitoring mechanism is used for collecting tensile stress values of a plurality of sample anchor rods in each area.
The data processing module comprises a data sampling unit, a data storage unit, a temperature synchronous deviation calculation unit, a moisture content deviation calculation unit, a stress deviation calculation unit, a pressure deviation calculation unit, a data fusion unit and a data analysis unit. The data sampling unit is used for acquiring monitoring data of the soil humidity on-line monitoring mechanism, the soil temperature on-line monitoring mechanism, the pit wall pressure on-line monitoring mechanism and the anchor cable stress on-line monitoring mechanism according to preset signals by adopting frequency, and generating corresponding block measuring data packets and reference humidity data. The data storage unit is used for classifying and storing the historical values of all the collected sample data. The temperature synchronization deviation calculation unit is used for calculating a corresponding temperature synchronization deviation according to each updated block measurement data packet and historical data. The water content deviation calculation unit is used for calculating a corresponding water content deviation according to each block measurement data packet and the reference humidity data. The stress offset calculating unit is used for calculating a corresponding stress offset according to each block measurement data packet. The pressure deviation calculation unit is used for calculating a corresponding pressure deviation according to each block measurement data packet. The data fusion unit is used for carrying out fusion processing on the temperature synchronous deviation, the water content deviation, the stress deviation and the pressure deviation calculated in each measuring block to obtain a corresponding risk state evaluation value. The data analysis unit is used for generating and outputting a corresponding collapse risk prediction result according to the risk state evaluation values of all the measured blocks.
The alarm is electrically connected with the data processing module; the alarm is used for sending out a corresponding safety alarm according to the prediction result generated by the data analysis unit.
As a further development of the invention, the data processing module employs a server or microprocessor with data computing functionality. The detection parts of the soil humidity on-line monitoring mechanism, the soil temperature on-line monitoring mechanism, the pit wall pressure on-line monitoring mechanism and the anchor cable stress on-line monitoring mechanism are arranged at each measuring point, and the detection results are sent to the data processing module in a wired or wireless data transmission mode.
The technical scheme provided by the invention has the following beneficial effects:
aiming at the problem of difficult prediction of pit collapse, the embodiment optimizes the inducement of risk and selects a series of measurable effective parameters to monitor the state of the side slope on line. The monitored sample data can lay a foundation for predicting the slope collapse risk. The invention also designs a new data analysis logic aiming at the selected sample data type, so as to quantify the early characteristics of foundation pit collapse and timely find the time-varying characteristics of the foundation pit side slope. Compared with the prior art, the method can effectively distinguish the early characteristics of collapse rather than the apparent state of collapse, so that the technical scheme provided by the invention can be used for guiding an owner party to timely check hidden danger and avoiding collapse accidents.
Drawings
The accompanying drawings are included to provide a further understanding of the invention and are incorporated in and constitute a part of this specification, illustrate the invention and together with the embodiments of the invention, serve to explain the invention. In the drawings:
fig. 1 is a typical state diagram of a foundation pit side slope structure according to the embodiment 1 of the present invention.
Fig. 2 is a flow chart of steps of a method for predicting collapse risk of a side slope of a deep foundation pit of a building according to embodiment 1 of the present invention.
Fig. 3 is a flowchart illustrating steps of a method for partitioning a measurement area and a measurement block according to embodiment 1 of the present invention.
Fig. 4 illustrates a typical block division pattern corresponding to the polygonal area.
FIG. 5 is a diagram showing the spatial layout of different types of measurement points in embodiment 1 of the present invention.
Fig. 6 is a system architecture diagram of a prediction system for landslide risk of a deep foundation pit slope of a building provided in embodiment 2 of the present invention.
Detailed Description
The present invention will be described in further detail with reference to the drawings and examples, in order to make the objects, technical solutions and advantages of the present invention more apparent. It should be understood that the specific embodiments described herein are for purposes of illustration only and are not intended to limit the scope of the invention.
Example 1
The embodiment provides a prediction method for collapse risk of a deep foundation pit slope of a building, which is used for monitoring an earth slope containing anchoring measures in engineering construction, predicting collapse risk possibly existing in the foundation pit in time and assisting in checking potential safety hazards. Fig. 1 is a cross-sectional typical characteristic diagram of this type of slope, from which it can be seen: the side slope of the foundation pit is mainly composed of soil and a slope protection structure built on one side of the soil, and an inner side area surrounded by the slope protection structure is the building foundation pit. The slope protection structure has the function of protecting the slope of the foundation pit, and preventing soil from creeping and even collapsing.
In order to discover the creep trend of the soil body in time as early as possible, and timely perform early warning when the landslide is at risk, and assist the industry owner to perform hidden trouble investigation, the embodiment provides a prediction method for the landslide risk of the deep foundation pit of a building, as shown in fig. 2, the prediction method comprises the following steps:
s1: and (3) taking the outward 5m of the edge of the foundation pit as a far limit, defining a region along the edge of the foundation pit as a near limit, and uniformly dividing the region into a plurality of measuring blocks which are distributed in a radial manner in the center according to the length and the shape of the outline of the foundation pit. The main reason for the collapse of the foundation pit is that the creep trend of the soil body around the foundation pit exceeds the maximum load which can be borne by the slope protection structure; therefore, in the actual monitoring and early warning process, the embodiment combines the cause and the change trend of the creep of the soil body, selects the soil body around the foundation pit as an observation object, and analyzes the state change trend of the soil body. Specifically, in this embodiment, the observation object in the whole annular area is taken as a measurement area, and meanwhile, in order to more accurately distinguish the difference of the creep trend of the soil body in different directions of the foundation pit, the measurement area is divided into a plurality of measurable blocks, and each of the measurable blocks is taken as an object for performing independent analysis.
Specifically, in this embodiment, the method for dividing the measurement area and the measurement block is shown in fig. 3, and includes the following steps:
s11: in the practical application process, for the foundation pit in the house construction engineering, the outline and the size of the foundation pit are accurately marked in the design drawing, so that on-site measurement is not needed. For some protection projects designed to deep foundation pit formed by natural foundation pit, mountain and road collapse, the shape of the foundation pit or collapse needs to be determined, and the design scheme of the protection project and the corresponding collapse monitoring scheme are designed according to the shape of the foundation pit or collapse. In actual measurement, the embodiment needs to acquire the profile features of the upper edge of the foundation pit through a orthographic projection method.
S12: considering that the shape of the foundation pit is not a regular pattern in the practical application process, the division of the area and the installation of related detection facilities are disadvantageous. In order to better divide the measuring blocks, the embodiment adopts a polygon fitting algorithm to fit the outer contour of the upper edge of the foundation pit, and obtains the polygon with the minimum edge number, the coincidence rate of which is not lower than 95%.
S13: after the polygonal profile is obtained, the present embodiment further segments the upper edge of the pit as shown in fig. 4.
The segmentation criteria are:
(1) Different edges in the polygonal contour are divided into different segments.
(2) The same side is uniformly segmented and the single segment length is not more than 60m.
S14: directing rays to each sectional boundary point by taking the polygon center of the foundation pit as an end point; then, taking the edge of the foundation pit as a near boundary and the edge of the foundation pit outwards by 5m as a far boundary, taking two adjacent rays as two side boundaries, and taking the entity space which is corresponding to each independent enclosing area and downwards reaches the maximum depth of the foundation pit as each measuring block.
The segmentation method combined with step S13 can be as follows: each measuring block at the periphery of the foundation pit is adjacent to each other; the range of each test block is approximately the same, and the maximum length along the edge of the foundation pit is also lower than 60m. In particular, each independent measuring block divided by the embodiment is positioned on the same side of the edge of the foundation pit, so that the creep trend of soil in each measuring block can be kept consistent if creep occurs.
S2: and arranging measuring points for acquiring different types of sample data in the measuring area according to a preset sampling standard. The types of the obtained sample data comprise soil temperature t, soil water content waf, anchor cable stress f and pit wall pressure p.
As shown in fig. 5, the distribution states of the measurement points of the different types of sample data are as follows:
(1) The measuring points of the soil temperature are uniformly distributed in the range from the earth surface to the soil depth of 2m in the measuring block; the distribution density of the measurement points is not less than 200m 3 Each one of which.
(2) The soil moisture content measuring points are classified into surface layer type and deep layer type. The surface layer type measuring points are distributed in the range from the earth surface to the soil depth of 2m in the measuring block; the deep layers are distributed in the range from the soil depth below 2m to the maximum depth of the foundation pit, and are uniformly distributed on layers with different depths at intervals of 5 m.
Wherein the distribution density of the measuring points corresponding to each depth layer in the surface layer type and the depth layer type is not less than 400m 3 Each one of which.
(3) The measuring points of the anchor rope stress are distributed on slope protection structures corresponding to slopes in all the measuring blocks, and at least 4 anchor rods are selected from the slope protection structures of all the measuring blocks to serve as the measuring points of the anchor rope stress.
(4) The measuring points of the pit wall pressure are distributed on the longitudinal interfaces of the slope protection structure and the soil body in each measuring block and are distributed downwards in sequence according to the depth interval of 3 m.
S3: and respectively acquiring corresponding sample data at each measuring point according to a preset sampling period, and recording historical data of the sample data of each measuring point.
In this embodiment, the detection modes adopted for different types of data are different, and the update frequencies of different data in the actual application process are different. Since different sample data is not updated synchronously, it is necessary to store the values of the respective sample data in a classified manner. It should be noted that: after the sample data of different measuring points are updated, the sample data of each measuring point needs to be marked on the data source and the updating time of the data in the storage process; and the time-varying characteristics corresponding to different measurement blocks in the whole area are obtained from a series of discrete information.
S4: after at least one round of updating is completed on all types of sample data in any one test block, all the sample data in the current period are formed into a test block data packet. And calculating a risk state evaluation value corresponding to each block according to the block data packet. In the practical application process of the embodiment, the state data of each measuring point is updated in real time according to a preset sampling period, and the data needs to be developed after the different data are updated for one round in the data analysis process, so that the defect that the time characteristics of the different data are ignored in the traditional multivariate data analysis method can be avoided.
Specifically, the calculation process of the risk status evaluation value in the present embodiment is as follows:
s41: calculating the temperature same ratio deviation Dt of the current state value of each soil temperature measuring point in the measuring block and the average value of the same time in the past three days; the calculation formula is as follows:
in the above, t i The soil temperature at the current moment of the ith measuring point in the current measuring block is represented; n1 is the number of soil temperature measuring points in the current measuring block; tm (tm) i The average value of the soil temperature at the same time of three days in the past of the current measuring point is obtained. Consider tm before the start of the protocol i Is invalid, thus tm i The value of (2) is set manually in the initial state and becomes updated autonomously after the acquired history data meets the requirements.
S42: calculating the increase of the water content of soil of different depth measuring points in the measuring block compared with a reference position reference value, and calculating the corresponding water content deviation Dwaf: the calculation formula is as follows:
in the formula, m1 represents the depth corresponding to the measuring points of different soil moisture contents; waf i The average value of the soil moisture content of all measuring points with the depth of i in the current measuring block is represented; waf0 i A reference value of the water content of the soil at a depth i, epsilon i And representing corresponding risk coefficients when the soil moisture contents at different depths i are supersaturated.
Reference value waf0 i Is mainly used for comparing the actual measurement value of the building area with the difference of the non-building area under the same regional condition. And further, the influence of the soil moisture content change caused by natural factors such as climate, weather and the like on the soil moisture content is distinguished from the influence of groundwater change or engineering construction. Specifically, the reference value waf0 in the present embodiment i The measurement method of (2) is as follows: selecting a non-building area with the same location and the soil surface subjected to hardening treatment as a reference area, taking a position group corresponding to the depth in the reference area as a reference measuring point, and carrying out actual measurement on the soil moisture content of the reference measuring point to obtain a reference value waf0 of the soil moisture content of the position with the required depth of i i 。
S43: calculating the increment of the average value of the anchor cable stress of each measuring point in the measuring block relative to the initial value in the current period, and calculating the stress offset Df in the measuring block; the calculation formula is as follows:
In the above formula, n2 represents the number of measuring points of the anchor rope stress in the current measuring block; ft (ft) i A measured value of the current moment of the stress measuring point of the ith anchor cable is represented; f0 of i The initial value of the stress measuring point of the ith anchor cable is shown. Wherein the anchor cable stress acting on any anchor cable is axial tensile stress, and the initial value N of the axial tensile stress k Satisfies the following formula:
in the above, H k Representing the standard value of the horizontal tension of the anchor rod; θ represents the horizontal tilt angle of the anchor rod.
S44: calculating the increasing rate of the average value of pit wall pressure of each measuring point in the measuring block relative to the initial value in the current sampling period, and calculating the pressure deviation Dp in the measuring block; the calculation formula is as follows:
in the above formula, m2 represents the depth corresponding to the measuring point of the pit wall pressure; p is p i Representing pit wall pressure at the depth i in the current measured block; p0 i The depth is the initial value of the pit wall pressure at i.
S45: respectively to the temperature and the same ratio deviation D t And carrying out normalization treatment on the water content deviation Dwaf, the stress deviation Df and the pressure deviation Dp, wherein the normalization formula is as follows:
in the above formula, x represents the original value of each parameter, and x' represents the normalized value of x;x max and x min Respectively representing the maximum value and the minimum value of each parameter; wherein x is max And x min The initial value of (2) is initialized and set by expert according to experience, and when any measured value exceeds the initial value in the state of not triggering collapse early warning, the measured upper limit value and lower limit value are adopted to count x max Or x min And updating.
Then, fusion processing is performed on the normalized values of the different parameters, so as to obtain a corresponding risk state evaluation value Ass. The calculation formula of the risk status evaluation value Ass is as follows:
Ass＝α·Dt′+β·Dwaf′+γ·Df′+λ·Dp′
in the above formula, dt ', dwaf', df 'and Dp' are normalized values of Dt, dwaf, df and Dp, respectively; alpha, beta, gamma and lambda are respectively the influence weights of the changes of four indexes of preset soil temperature, soil moisture content, anchor cable stress and pit wall pressure on the collapse risk of the foundation pit; and satisfies the following: alpha+beta+gamma+lambda is less than or equal to 1.
S5: calculating the dispersion of risk state evaluation values Ass of all the test pieces, and generating one prediction result Y for collapse risk according to the dispersion: wherein, when y=1, it indicates that there is a risk of collapse; when Y is less than or equal to 1, no collapse risk is indicated. The prediction result generation function is as follows:
in the above, ass i A risk status evaluation value indicating an i-th block; k represents the number of measurement blocks in the measurement region; μ represents the average value of risk state evaluation values of all the test pieces; sigma represents the standard deviation of risk state evaluation values of all the test blocks; sigma (sigma) 0 Representing a preset collapse risk threshold.
Finally in this embodiment, σ and σ are utilized 0 The risk of collapse of the whole foundation pit can be effectively analyzed. When y=1, i.e. there is a risk of collapse, for better investigation of potential safety hazards, the following method may also be used to determine the source of the anomaly data that is causing the risk:
First, the discrete influence degree Δσ of each measurement block i is calculated by the following formula i ；
Δσ i ＝|σ k -σ k-i |
In the above, sigma k A standard deviation representing risk status evaluation values of all test blocks including the i-th test block class; sigma (sigma) k-i The standard deviation of risk state evaluation values of all the blocks except the i-th block is represented.
Then, the discrete influence degree Δσ calculated for each measurement block i And sequencing to obtain an influence queue.
And finally, selecting a plurality of side blocks in front in the influence degree queue as key areas for hidden trouble investigation. In the hidden trouble investigation process, natural factors or human factors possibly causing foundation pit collapse on the joss stick site are subjected to item-by-item inspection according to corresponding instruction manuals.
Example 2
The embodiment provides a prediction system for collapse risk of a side slope of a deep foundation pit of a building, which is applied to online monitoring of collapse risk of a foundation pit or a slope protection structure constructed manually and excavated in a building engineering, and timely early warning when the collapse risk exists. The prediction system of the embodiment is an automatic system, an online monitoring mechanism automatically collects all state data of a relevant area, a data processing module at the rear end analyzes and processes the collected sample data to obtain a conclusion whether collapse risks exist or not, and a corresponding early warning signal is sent according to the relevant conclusion.
As shown in fig. 6, the predictive system includes various groups of monitoring facilities deployed in the field, a data processing center located in the background, and various alarms communicatively coupled to the data center. The monitoring mechanism specifically comprises a soil humidity on-line monitoring mechanism, a soil temperature on-line monitoring mechanism, a pit wall pressure on-line monitoring mechanism and an anchor cable stress on-line monitoring mechanism.
The soil humidity on-line monitoring mechanism comprises a plurality of sensors, the sensors are arranged at all measuring points, and the deployment of all measuring points is completed according to the layout mode in the prediction method of the collapse risk of the deep foundation pit slope of the building in the embodiment 1. What needs to be specifically stated is: the soil humidity on-line monitoring mechanism is not only used for collecting the real-time soil moisture content of each measuring point in a building area in real time, but also needs to be deployed in a specific non-building area, and is further used for collecting the real-time soil moisture content of a reference measuring point.
The soil temperature on-line monitoring mechanism also comprises a plurality of sensors; the sensors are installed at each measuring point, and each measuring point is also deployed in a layout manner in the prediction method of the collapse risk of the side slope of the deep foundation pit of the building as in embodiment 1. Unlike the soil humidity on-line monitoring mechanism, the soil temperature on-line monitoring mechanism only needs to be deployed in shallow soil and collects the real-time temperature of the shallow soil around the slope.
The pit wall pressure on-line monitoring mechanism is used for collecting interface pressure values corresponding to different depths along the longitudinal interface of the soil body and the slope protection structure in each area. The anchor cable stress on-line monitoring mechanism is used for collecting tensile stress values of a plurality of sample anchor rods in each area.
The background data processing center is actually a corresponding server, and the server can perform data transmission with each group of monitoring institutions and receive sampling data reported by each monitoring institution. The communication mode between the server and the monitoring mechanism is not limited by the scheme of the embodiment, and only the server and the monitoring mechanism can timely and effectively complete data transmission; both can use wireless communication or wire communication mode to complete data transmission. Of course, from the viewpoint of security and reliability, data transmission should be performed by preferentially using a dedicated communication line. Furthermore, in view of the complex environment of the construction site, the related communication cable should also employ a cable with an armor layer.
In this embodiment, a data processing module that can perform data processing according to corresponding data processing logic is running in the background server. The data processing module comprises a data sampling unit, a data storage unit, a temperature synchronous deviation calculation unit, a moisture content deviation calculation unit, a stress deviation calculation unit, a pressure deviation calculation unit, a data fusion unit and a data analysis unit.
The data sampling unit is used for acquiring monitoring data of the soil humidity on-line monitoring mechanism, the soil temperature on-line monitoring mechanism, the pit wall pressure on-line monitoring mechanism and the anchor cable stress on-line monitoring mechanism according to preset signals by adopting frequency, and generating corresponding block measuring data packets and reference humidity data. The data storage unit is used for classifying and storing the historical values of all the collected sample data. The temperature synchronization deviation calculation unit is used for calculating a corresponding temperature synchronization deviation according to each updated block measurement data packet and historical data. The water content deviation calculation unit is used for calculating a corresponding water content deviation according to each block measurement data packet and the reference humidity data. The stress offset calculating unit is used for calculating a corresponding stress offset according to each block measurement data packet. The pressure deviation calculation unit is used for calculating a corresponding pressure deviation according to each block measurement data packet. The data fusion unit is used for carrying out fusion processing on the temperature synchronous deviation, the water content deviation, the stress deviation and the pressure deviation calculated in each measuring block to obtain a corresponding risk state evaluation value. The data analysis unit is used for generating and outputting a corresponding collapse risk prediction result according to the risk state evaluation values of all the measured blocks.
The alarm is electrically connected with the data processing module; the alarm is used for sending out a corresponding safety alarm according to the prediction result generated by the data analysis unit. In this embodiment, the alarm may be installed in a data center or may be installed in a distributed manner in an engineering site, and particularly, considering that in embodiment 1, the risk degrees of different measurements may be estimated approximately by the discrete influence degrees of the respective measurement blocks, when the alarm in this embodiment is installed in the engineering site, the background server may also send different alarm signals according to the discrete influence degrees of the different measurement blocks.
The foregoing description of the preferred embodiments of the invention is not intended to be limiting, but rather is intended to cover all modifications, equivalents, and alternatives falling within the spirit and principles of the invention.
Claims (10)
1. The prediction method for the collapse risk of the side slope of the deep foundation pit of the building is characterized by being used for monitoring the soil side slope containing the anchoring measures in engineering construction, predicting the collapse risk possibly in time and assisting in checking potential safety hazards;
the prediction method comprises the following steps:
s1: the method comprises the steps of defining a region by taking the edge of a foundation pit outwards by 5m as a far boundary and taking the edge of the foundation pit as a near boundary, and uniformly dividing the region into a plurality of measuring blocks which are distributed in a central radial manner according to the length and the shape of the outline of the foundation pit;
S2: arranging measuring points for acquiring different types of sample data in a measuring area according to a preset sampling standard; the types of the obtained sample data comprise soil temperature t, soil water content waf, anchor cable stress f and pit wall pressure p;
s3: respectively acquiring corresponding sample data at each measuring point according to a preset sampling period, and recording historical data of the sample data of each measuring point;
s4: after at least one round of updating is completed on all types of sample data in any one test block, forming a test block data packet on all the sample data in the current period; calculating a risk state evaluation value corresponding to each block according to the block data packet;
the risk state evaluation value is calculated as follows:
s41: calculating the temperature same ratio deviation Dt of the current state value of each soil temperature measuring point in the measuring block and the average value of the same time in the past three days; the calculation formula is as follows:
in the above, t i The soil temperature at the current moment of the ith measuring point in the current measuring block is represented; n1 is the number of soil temperature measuring points in the current measuring block; tm (tm) i The average value of soil temperature at the same time of three days in the past for the current measuring point;
s42: calculating the increase of the water content of soil of different depth measuring points in the measuring block compared with a reference position reference value, and calculating the corresponding water content deviation Dwaf: the calculation formula is as follows:
In the formula, m1 represents the depth corresponding to the measuring points of different soil moisture contents; waf i The average value of the soil moisture content of all measuring points with the depth of i in the current measuring block is represented; waf0 i A reference value of the water content of the soil at a depth i, epsilon i Representing corresponding risk coefficients when the soil moisture content at different depths i is supersaturated;
s43: calculating the increment of the average value of the anchor cable stress of each measuring point in the measuring block relative to the initial value in the current period, and calculating the stress offset Df in the measuring block; the calculation formula is as follows:
in the above formula, n2 represents the number of measuring points of the anchor rope stress in the current measuring block; ft (ft) i A measured value of the current moment of the stress measuring point of the ith anchor cable is represented; f0 of i Representing the initial value of the stress measuring point of the ith anchor cable;
s44: calculating the increasing rate of the average value of pit wall pressure of each measuring point in the measuring block relative to the initial value in the current sampling period, and calculating the pressure deviation Dp in the measuring block; the calculation formula is as follows:
in the above formula, m2 represents the depth corresponding to the measuring point of the pit wall pressure; p is p i Representing pit wall pressure at the depth i in the current measured block; p0 i An initial value of pit wall pressure at depth i;
s45: respectively to the temperature and the same ratio deviation D t The water content deviation Dwaf, the stress deviation Df and the pressure deviation Dp are subjected to normalization The first step is to fuse and obtain a corresponding risk status evaluation value Ass, and the calculation formula is as follows:
Ass＝α·Dt′+β·Dwaf′+γ·Df′+λ·Dp′
in the above formula, dt ', dwaf', df 'and Dp' are normalized values of Dt, dwaf, df and Dp, respectively; alpha, beta, gamma and lambda are respectively the influence weights of the changes of four indexes of preset soil temperature, soil moisture content, anchor cable stress and pit wall pressure on the collapse risk of the foundation pit; and satisfies the following: alpha+beta+gamma+lambda is less than or equal to 1;
s5: calculating the dispersion of risk state evaluation values Ass of all the test pieces, and generating one prediction result Y for collapse risk according to the dispersion: wherein, when y=1, it indicates that there is a risk of collapse; when Y is less than or equal to 1, no collapse risk is indicated.
2. The prediction method for the collapse risk of the side slope of the deep foundation pit of the building, as set forth in claim 1, is characterized in that: in step S1, the method for dividing the measurement area and the measurement block is as follows:
s11: acquiring outline features of the upper edge of the foundation pit by a orthographic projection method;
s12: fitting the outer contour of the upper edge of the foundation pit by adopting a polygon fitting algorithm to obtain a polygon with the minimum edge number, the coincidence rate of which is not lower than 95%;
s13: segmenting the edge of the upper opening of the foundation pit, wherein the segmentation standard is as follows: different edges are divided into different segments; the same side is uniformly segmented, and the single segmentation length is not more than 60m;
S14: directing rays to each sectional boundary point by taking the polygon center of the foundation pit as an end point; then, taking the edge of the foundation pit as a near boundary and the edge of the foundation pit outwards by 5m as a far boundary, taking two adjacent rays as two side boundaries, and taking the entity space which is corresponding to each independent enclosing area and downwards reaches the maximum depth of the foundation pit as each measuring block.
3. The prediction method for the collapse risk of the side slope of the deep foundation pit of the building, as set forth in claim 1, is characterized in that: the distribution states of the measurement points of the different types of sample data in step S2 are as follows:
(1) SoilThe measuring points of the soil temperature are uniformly distributed in the range from the earth surface to the soil depth of 2m in the measuring block; the distribution density of the measurement points is not less than 200m 3 Each one of which;
(2) The measuring points of the water content of the soil are divided into a surface layer type measuring point and a deep layer type measuring point; the surface layer type measuring points are distributed in the range from the earth surface to the soil depth of 2m in the measuring block; the deep layers are distributed in the range from the soil depth below 2m to the maximum depth of the foundation pit and uniformly distributed on different depth layers at intervals of 5 m; the distribution density of the corresponding measuring points of each depth layer in the surface layer type and the depth layer type is not lower than 400m 3 Each one of which;
(3) The measuring points of the anchor rope stress are distributed on slope protection structures corresponding to slopes in all the measuring blocks, and at least 4 anchor rods are selected from the slope protection structures of all the measuring blocks to serve as the measuring points of the anchor rope stress;
(4) The measuring points of the pit wall pressure are distributed on the longitudinal interfaces of the slope protection structure and the soil body in each measuring block and are distributed downwards in sequence according to the depth interval of 3 m.
4. The prediction method for the collapse risk of the side slope of the deep foundation pit of the building, as set forth in claim 1, is characterized in that: in step S41, tm i The value of (2) is set manually in the initial state, and the historical data collected in the step S3 is updated autonomously after meeting the requirements.
5. The prediction method for the collapse risk of the side slope of the deep foundation pit of the building, as set forth in claim 1, is characterized in that: in step S42, waf0 i The measurement method of the value of (2) is as follows: selecting a non-building area with the same location and the soil surface subjected to hardening treatment as a reference area, taking a position group corresponding to the depth in the reference area as a reference measuring point, and carrying out actual measurement on the soil moisture content of the reference measuring point to obtain a reference value waf0 of the soil moisture content of the position with the required depth of i i 。
6. The prediction method for the collapse risk of the side slope of the deep foundation pit of the building, as set forth in claim 1, is characterized in that: in step S43, the anchor cable stress acting on any one anchor cable is axial tensile stress, and the initial value of the axial tensile stress is N k Satisfies the following formula:
in the above, H k Representing the standard value of the horizontal tension of the anchor rod; θ represents the horizontal tilt angle of the anchor rod.
7. The prediction method for the collapse risk of the side slope of the deep foundation pit of the building, as set forth in claim 1, is characterized in that: in step S45, the comparison deviation D is compared by using the following normalization formula t Normalizing four parameters of the water content deviation Dwaf, the stress deviation Df and the pressure deviation Dp:
in the above formula, x represents the original value of each parameter, and x' represents the normalized value of x; x is x max And x min Respectively representing the maximum value and the minimum value of each parameter; wherein x is max And x min The initial value of (2) is initialized and set by expert according to experience, and when any measured value exceeds the initial value in the state of not triggering collapse early warning, the measured upper limit value and lower limit value are adopted to count x max Or x min And updating.
8. The method for predicting the collapse risk of the side slope of the deep foundation pit of the building according to claim 7, wherein the method comprises the following steps: in step S5, the prediction result generation function is as follows:
in the above, ass i A risk status evaluation value indicating an i-th block; k represents the number of measurement blocks in the measurement region; μ represents the average value of risk state evaluation values of all the test pieces; sigma represents the risk status assessment value of all test blocksStandard deviation of (2); sigma (sigma) 0 Representing a preset collapse risk threshold.
9. The method for predicting the collapse risk of the side slope of the deep foundation pit of the building according to claim 8, wherein the method comprises the following steps: when there is a risk of collapse, the source of the anomaly data that creates the risk is determined using the following method:
(1) Calculating the discrete influence delta sigma of each measured block by adopting the following method i ；
Δσ i ＝σ k -σ k-i
In the above, sigma k A standard deviation representing risk status evaluation values of all test blocks including the i-th test block class; sigma (sigma) k-i Representing standard deviation of risk state evaluation values of all the rest of the blocks except the ith block;
(2) Discrete influence degree DeltaSigma calculated for each measurement block i Sequencing to obtain an influence queue;
(3) And selecting a plurality of side blocks in front in the influence degree queue as key areas for hidden trouble investigation.
10. The prediction system for the collapse risk of the side slope of the deep foundation pit of the building is characterized by being applied to online monitoring of the collapse risk of a foundation pit or a slope protection structure constructed manually and excavated in the building engineering, and timely early warning is carried out when the risk exists; the prediction system includes:
the soil humidity on-line monitoring mechanism is deployed by adopting a layout mode in the prediction method of the collapse risk of the side slope of the deep foundation pit of the building according to any one of claims 1-3 and 5; the soil humidity on-line monitoring mechanism is used for collecting the real-time soil moisture content of each measuring point in the building area in real time and collecting the real-time soil moisture content of the reference measuring point;
The soil temperature on-line monitoring mechanism is deployed by adopting a layout mode in the prediction method of the collapse risk of the side slope of the deep foundation pit of the building according to any one of claims 1-3; the soil temperature on-line monitoring mechanism is used for collecting the real-time temperature of the shallow soil around the side slope;
the pit wall pressure on-line monitoring mechanism is used for collecting interface pressure values corresponding to different depths along the longitudinal interface of the soil body and the slope protection structure in each area;
the anchor cable stress on-line monitoring mechanism is used for collecting tensile stress values of a plurality of sample anchor rods in each area;
the data processing module comprises a data sampling unit, a data storage unit, a temperature synchronous deviation calculation unit, a moisture content deviation calculation unit, a stress deviation calculation unit, a pressure deviation calculation unit, a data fusion unit and a data analysis unit; the data sampling unit is used for acquiring monitoring data of the soil humidity on-line monitoring mechanism, the soil temperature on-line monitoring mechanism, the pit wall pressure on-line monitoring mechanism and the anchor cable stress on-line monitoring mechanism according to preset signals by adopting frequency, and generating a corresponding block measuring data packet and reference humidity data; the data storage unit is used for classifying and storing the historical values of all the collected sample data; the temperature synchronous deviation calculation unit is used for calculating a corresponding temperature synchronous deviation according to each updated block measurement data packet and historical data; the water content deviation calculation unit is used for calculating a corresponding water content deviation according to each block measurement data packet and the reference humidity data; the stress offset calculating unit is used for calculating a corresponding stress offset according to each block measurement data packet; the pressure deviation calculation unit is used for calculating a corresponding pressure deviation according to each block measurement data packet; the data fusion unit is used for carrying out fusion processing on the temperature synchronous deviation, the water content deviation, the stress deviation and the pressure deviation calculated by each measuring block to obtain a corresponding risk state evaluation value; the data analysis unit is used for generating and outputting a corresponding collapse risk prediction result according to the risk state evaluation values of all the measured blocks; and
An alarm electrically connected to the data processing module; the alarm is used for sending out a corresponding safety alarm according to the prediction result generated by the data analysis unit.
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