Foundation pit engineering construction quality management method

Abstract

The present application relates to the field of foundation pit engineering, especially to a foundation pit engineering construction quality management method, by obtaining geological disturbance characteristic value and component form characteristic value, determining geological risk coefficient and component risk coefficient to obtain comprehensive lifting risk value, combining dynamic lifting threshold value to match corresponding lifting speed range; obtaining hook bottom load fluctuation data to determine lifting dynamic disturbance coefficient by sliding time window, obtaining stress wave signal buried in slot wall soil to determine soil micro-fracture activation characteristic value; determining transient disturbance coupling index based on lifting dynamic disturbance coefficient and soil micro-fracture activation characteristic value, combining risk grade to adjust subsequent lifting speed range. The present application realizes the determination of lifting operation risk grade and the self-adaptive control of lifting speed, puts lifting load fluctuation and soil micro-fracture evolution into the monitoring and evaluation dimension, improves the safety of foundation pit prefabricated component lifting construction, and is conducive to improving the foundation pit engineering construction quality.

Description

Technical Field

[0001] This invention relates to the field of foundation pit engineering, and in particular to a method for quality management of foundation pit engineering construction. Background Technology

[0002] Foundation pit engineering refers to a series of engineering measures, including support, dewatering, and excavation, taken during the construction of the underground portion of a building or structure to ensure the safety of the excavation, underground structure construction, and the surrounding environment. With the continuous expansion of urban underground space development, foundation pit engineering faces multiple challenges, including complex construction environments, tight schedules, and high safety requirements. In recent years, prefabricated building technology has been gradually promoted and applied in the field of foundation pit engineering, forming a prefabricated foundation pit support structure system represented by precast diaphragm walls and precast support components. This system transforms traditional cast-in-place operations into an industrialized construction mode of component prefabrication and on-site assembly, effectively improving construction efficiency and reducing on-site work.

[0003] Chinese Patent Publication No. CN110019607A discloses a method for recording construction conditions of foundation pit engineering, comprising the following steps: constructing a foundation pit engineering construction condition recording system; the foundation pit engineering construction condition recording system includes a server, a database, and at least one mobile terminal; drawing a GIS base map of at least one foundation pit and storing it on the mobile terminal; selecting one of the GIS base maps as a target GIS base map on the mobile terminal; editing the target GIS base map to obtain a construction condition GIS base map and / or inputting construction condition information through the information input module; uploading the construction condition GIS base map and / or the construction condition information to the server and transmitting it to the database for storage through the server. This invention, a method for recording construction conditions of foundation pit engineering, standardizes the construction process of common foundation pit engineering projects, allowing on-site inspectors to easily and accurately describe the construction conditions of foundation pit engineering, and improving the utilization efficiency of construction condition information in subsequent data analysis.

[0004] However, the following problems still exist in the existing technology: 1. In the existing technology, the impact of changes in the internal structure of the soil during hoisting on construction safety is not considered, which leads to the inability to detect the accumulation of soil damage in a timely manner in soft soil layers or sensitive and adverse geological conditions, thus posing a safety risk. 2. In the existing technology, the dynamic matching between hoisting speed control and real-time working conditions is not considered. It is impossible to adjust the operation rhythm in a timely manner according to the load fluctuations and soil response during the hoisting process, which may lead to hoisting errors and low efficiency. Summary of the Invention

[0005] Therefore, this invention provides a method for construction quality management of foundation pit engineering, which overcomes the problems of existing technologies that do not consider the impact of changes in the internal structure of the soil during hoisting on construction safety, and do not consider the dynamic matching of hoisting speed control with real-time working conditions, which may lead to safety risks, hoisting errors and low efficiency.

[0006] To achieve the above objectives, the present invention provides a method for quality management of foundation pit construction, comprising: Obtain the soil mechanical parameters within the area affected by the hoisting of precast components to determine the geological disturbance characteristic value; obtain the design shape parameters and measured shape parameters of the precast components to determine the component morphological characteristic value. The geological risk coefficient is determined based on the geological disturbance characteristic value, the component risk coefficient is determined based on the component morphology characteristic value, and the comprehensive hoisting risk value is determined based on the geological risk coefficient and the component risk coefficient. The risk level of the current lifting operation is determined based on the comprehensive lifting risk value and the dynamic lifting threshold, so as to match the corresponding lifting speed range; The hoisting process is controlled according to the hoisting speed range, and the load fluctuation data at the bottom of the hook is obtained by using a sliding time window. The hoisting dynamic disturbance coefficient is determined based on the load fluctuation data. Stress wave signals were collected from the soil embedded in the trench wall to determine the activation characteristics of soil micro-fractures. The transient disturbance coupling index is determined based on the hoisting dynamic disturbance coefficient and the soil micro-fracture activation characteristic value. The subsequent hoisting speed range is adjusted based on the risk level and the transient disturbance coupling index. The dynamic hoisting threshold is determined based on the component morphological characteristic value, and the size of the sliding time window is related to the risk level type.

[0007] Furthermore, obtaining the soil mechanical parameters within the affected area of ​​the precast component hoisting to determine the geological disturbance characteristic values ​​includes, Several geological survey points are set up at preset intervals within the area affected by the hoisting of precast components, and the soil shear strength parameters and soil compression modulus parameters are obtained at each survey point. Calculate the coefficient of variation of the soil shear strength parameter and the coefficient of variation of the soil compression modulus parameter at each survey point. Then, perform a weighted sum of the coefficients of variation of the shear strength parameter and the coefficient of variation of the compression modulus parameter to determine the geological disturbance characteristic value.

[0008] Furthermore, the process of obtaining the design and measured shape parameters of the prefabricated components to determine the component's morphological characteristic values ​​includes: Several test sections are selected along the length of the precast component at preset intervals, and the design and measured dimensions of each test section are obtained. Calculate the difference between the designed and measured dimensions of each test section to determine the local deviation value corresponding to each test section; The number of detection sections whose local deviation values ​​exceed a preset deviation threshold is counted, the average value of the local deviation values ​​corresponding to each detection section that exceeds the preset deviation threshold is calculated, and the component morphological feature value is determined based on the number of detection sections and the average value of the local deviation values.

[0009] Furthermore, a geological risk coefficient is determined based on the geological disturbance characteristic value, a component risk coefficient is determined based on the component morphology characteristic value, and a comprehensive hoisting risk value is determined based on the geological risk coefficient and the component risk coefficient, including... The ratio of the geological disturbance characteristic value to the preset geological disturbance standard value is determined as the geological risk coefficient; The ratio of the component morphological feature value to the preset component morphological standard value is determined as the component risk coefficient; The comprehensive hoisting risk value is determined based on the geological risk coefficient and the component risk coefficient.

[0010] Furthermore, the risk level of the current lifting operation is determined based on the comprehensive lifting risk value and the dynamic lifting threshold, in order to match the corresponding lifting speed range, including: The dynamic hoisting threshold is determined based on the component morphological characteristic values; Compare the overall lifting risk value with the dynamic lifting threshold; If the comprehensive lifting risk value is greater than or equal to the dynamic lifting threshold, the current lifting operation is determined to be of a high-risk level and matched with the first lifting speed range; If the overall lifting risk value is less than the dynamic lifting threshold, the current lifting operation is determined to be of low risk level, and the second lifting speed range is used. Wherein, the hoisting speed corresponding to the first hoisting speed range is lower than the hoisting speed corresponding to the second hoisting speed range.

[0011] Furthermore, determining the dynamic hoisting threshold based on the component morphological feature values ​​includes, The ratio of the component morphological feature value to the preset component morphological standard value is calculated as an adjustment coefficient; The product of the preset base threshold and the adjustment coefficient is determined as the dynamic hoisting threshold.

[0012] Furthermore, the step of acquiring load fluctuation data at the bottom of the hook using a sliding time window, and determining the hoisting dynamic disturbance coefficient based on the load fluctuation data, includes: If the current hoisting operation is at a high risk level, the sliding time window will take the first duration; if the current hoisting operation is at a low risk level, the sliding time window will take the second duration. Time-domain analysis is performed on the real-time acquired load fluctuation data at the bottom of the hook. The maximum and minimum load values ​​within the sliding time window are extracted, and the difference between the maximum and minimum load values ​​is calculated as the instantaneous fluctuation amplitude. The instantaneous fluctuation amplitudes of several consecutive time windows are statistically analyzed, and the average value of each instantaneous fluctuation amplitude is calculated as the hoisting dynamic disturbance coefficient. Wherein, the first duration is shorter than the second duration.

[0013] Furthermore, the acquisition of stress wave signals embedded in the soil of the trench wall to determine the activation characteristic values ​​of soil microfractures includes, Energy calculation is performed on the stress wave signal acquired in real time by the piezoelectric sensor, and waveform segments with signal energy values ​​exceeding the preset energy threshold are identified and marked as micro-fracture events. The frequency of micro-fracture events was determined as the activation characteristic value of soil micro-fracture.

[0014] Furthermore, determining the transient disturbance coupling index based on the hoisting dynamic disturbance coefficient and the soil micro-fracture activation characteristic value includes, The ratio of the hoisting dynamic disturbance coefficient to the preset hoisting dynamic disturbance standard value is determined as the hoisting disturbance coefficient; The ratio of the soil micro-fracture activation characteristic value to the preset micro-fracture activation standard value is determined as the micro-fracture activation coefficient; The weighted sum of the hoisting disturbance coefficient and the micro-fracture activation coefficient is used to determine the transient disturbance coupling index.

[0015] Furthermore, adjusting the subsequent hoisting speed range based on risk level and transient disturbance coupling index includes: Obtain a preset disturbance threshold corresponding to the current risk level, and compare the transient disturbance coupling index with the preset disturbance threshold; If the transient disturbance coupling index is greater than the disturbance threshold, it is determined that there is a risk of trench collapse, and the subsequent hoisting speed range is adjusted according to the current risk level. If the current hoisting operation is of a high-risk level and there is a risk of trench collapse, the upper and lower limits of the first hoisting speed range are multiplied by the ratio of the preset disturbance threshold to the transient disturbance coupling index to obtain the adjusted hoisting speed range after reduction. If the current hoisting operation is of low risk level but there is a risk of trench collapse, adjust the current hoisting speed range to the first hoisting speed range.

[0016] Compared with existing technologies, this invention obtains a comprehensive hoisting risk value by acquiring geological disturbance characteristic values ​​and component morphology characteristic values, determining geological risk coefficients and component risk coefficients, and matching the corresponding hoisting speed range with dynamic hoisting thresholds. It uses a sliding time window to acquire load fluctuation data at the bottom of the hook to determine the hoisting dynamic disturbance coefficient, and acquires stress wave signals embedded in the soil of the trench wall to determine the soil micro-fracture activation characteristic value. Based on the hoisting dynamic disturbance coefficient and the soil micro-fracture activation characteristic value, it determines a transient disturbance coupling index, and adjusts the subsequent hoisting speed range according to the risk level. This invention achieves the determination of hoisting operation risk level and adaptive control of hoisting speed, incorporates hoisting load fluctuations and soil micro-fracture evolution into the monitoring and evaluation dimensions, improves the safety of precast component hoisting construction in foundation pits, and is conducive to improving the construction quality of foundation pit engineering.

[0017] In particular, this invention considers the impact of the measured external dimensions of prefabricated components on hoisting risks. In practice, manufacturing deviations in components can lead to eccentric loads during hoisting, which can cause hook swaying and exacerbate lateral disturbances to the trench wall soil. This invention determines the component's morphological characteristics by obtaining the designed and measured external dimensions of each test section, thereby determining the component's risk coefficient and dynamically adjusting the hoisting threshold. This incorporates the actual geometric state of the component into the risk assessment system, avoiding hoisting accidents or adverse effects on construction quality caused by component defects.

[0018] In particular, this invention provides a quantitative characterization of the evolution of micro-fractures in soil. In practice, the internal damage to soil caused by hoisting vibrations is often concealed, and traditional surface observation methods struggle to capture the early deterioration process of the deep structure within the trench wall. This invention uses piezoelectric sensors embedded in the trench wall soil to collect stress wave signals in real time. Energy calculations are performed on the signals, and waveform segments exceeding a preset energy threshold are identified as micro-fracture events. The frequency of these micro-fracture events is determined as the activation characteristic value of soil micro-fractures, enabling quantitative monitoring of the damage evolution process within the soil structure and providing data for safety assessment of hoisting operations.

[0019] In particular, this invention considers the transient coupling effect between dynamic disturbance during hoisting and the activation of soil micro-fractures. Vibration during construction hoisting and soil fracturing damage have a complex correlation, making it difficult to accurately predict the risk of trench collapse using a single monitoring dimension. This invention determines the dynamic disturbance coefficient during hoisting based on the load fluctuation data at the bottom of the hook within a sliding time window, and combines this with the characteristic value of soil micro-fracture activation determined by stress wave signals to form a transient disturbance coupling index. Based on this index, the subsequent hoisting speed range is adjusted, thus achieving the capture of precursory characteristics of trench collapse.

[0020] In particular, this invention considers the compatibility between risk level and sliding time window size. In practice, high-risk and low-risk operating conditions have different requirements for monitoring sensitivity, and data interception of a fixed duration makes it difficult to balance response speed and judgment accuracy. This invention sets a sliding time window with a first duration corresponding to high-risk levels and a second duration corresponding to low-risk levels, which ensures rapid response in high-risk operating conditions while reducing the data processing burden in low-risk operating conditions.

[0021] In particular, this invention considers the feedback adjustment of the lifting speed range by the transient disturbance coupling index. In reality, the activation of soil micro-fractures has cumulative and sudden characteristics, requiring dynamic adjustment of operating parameters based on the real-time disturbance level. This invention compares the transient disturbance coupling index with a preset disturbance threshold to adjust the lifting speed range. At low-risk levels, it directly switches to the first lifting speed range, achieving adaptive graded control of the lifting speed and suppressing further development of soil fracturing damage. Attached Figure Description

[0022] Figure 1 This is a schematic diagram illustrating the steps of the foundation pit engineering construction quality management method according to an embodiment of the present invention; Figure 2 This is a logic block diagram illustrating the determination of the hoisting speed range according to an embodiment of the present invention. Figure 3 This is a logic block diagram of determining the size of the sliding time window according to an embodiment of the present invention; Figure 4 This is a logic block diagram of an embodiment of the present invention for determining whether there is a risk of collapse based on the transient disturbance coupling and disturbance threshold. Detailed Implementation

[0023] To make the objectives and advantages of the present invention clearer, the present invention will be further described below with reference to embodiments; it should be understood that the specific embodiments described herein are merely for explaining the present invention and are not intended to limit the present invention.

[0024] Preferred embodiments of the present invention will now be described with reference to the accompanying drawings. Those skilled in the art should understand that these embodiments are merely illustrative of the technical principles of the present invention and are not intended to limit the scope of protection of the present invention.

[0025] It should be noted that, in the description of this invention, unless otherwise explicitly specified and limited, the terms "installation," "connection," and "linking" should be interpreted broadly. For example, they can refer to a fixed connection, a detachable connection, or an integral connection; they can refer to a mechanical connection or an electrical connection; they can refer to a direct connection or an indirect connection through an intermediate medium; and they can refer to the internal connection of two components. Those skilled in the art can understand the specific meaning of the above terms in this invention according to the specific circumstances.

[0026] Please see Figure 1 The diagram shown illustrates the steps of a foundation pit construction quality management method according to an embodiment of the present invention. The foundation pit construction quality management method of the present invention includes: Step S1: Obtain the soil mechanical parameters in the area affected by the hoisting of precast components to determine the geological disturbance characteristic value; obtain the design shape parameters and measured shape parameters of the precast components to determine the component morphological characteristic value. Step S2: Determine the geological risk coefficient based on the geological disturbance characteristic value, determine the component risk coefficient based on the component morphology characteristic value, and determine the comprehensive hoisting risk value based on the geological risk coefficient and the component risk coefficient; Step S3: Determine the risk level of the current lifting operation based on the comprehensive lifting risk value and the dynamic lifting threshold, so as to match the corresponding lifting speed range; Step S4: Control the hoisting process according to the hoisting speed range, obtain the load fluctuation data at the bottom of the hook using a sliding time window, and determine the hoisting dynamic disturbance coefficient based on the load fluctuation data; Step S5: Obtain stress wave signals collected within the soil embedded in the trench wall to determine the activation characteristic values ​​of soil micro-fractures. Step S6: Determine the transient disturbance coupling index based on the hoisting dynamic disturbance coefficient and the soil micro-fracture activation characteristic value; adjust the subsequent hoisting speed range based on the risk level and the transient disturbance coupling index. The dynamic hoisting threshold is determined based on the component morphological characteristic value, and the size of the sliding time window is related to the risk level type.

[0027] Specifically, the stress wave signal refers to the elastic wave signal released when the soil in the trench wall undergoes micro-fractures under external disturbance during the hoisting of precast components. When micro-cracks occur or propagate within the soil, the accumulated strain energy is released outward in the form of stress waves. The signal is acquired in real time by piezoelectric sensors embedded in the soil wall. The piezoelectric sensors convert the mechanical vibrations generated by the micro-fractures in the soil into electrical signals. The signal amplitude and energy reflect the intensity of the micro-fracture event, while the signal frequency reflects the activity level of damage within the soil. Energy calculations are performed on the stress wave signals, and waveform segments exceeding a preset energy threshold are identified as micro-fracture events. By statistically analyzing the frequency of micro-fracture events per unit time, a quantitative characterization of the damage evolution process within the soil structure is achieved, providing data support for real-time assessment of the stability of the trench wall during hoisting.

[0028] Specifically, before the lifting operation begins, this invention acquires soil mechanical parameters and measured component shape parameters to determine geological disturbance characteristic values ​​and component morphological characteristic values, respectively. This allows for the calculation of a comprehensive lifting risk value. Based on the component morphological characteristic values, the lifting threshold is dynamically adjusted to classify risk levels and match corresponding lifting speed ranges. This ensures the lifting scheme can accommodate both ground inhomogeneity and component manufacturing deviations, reducing safety risks at the source. During the lifting process, a sliding time window is used to collect real-time load fluctuation data at the bottom of the hook to determine the lifting dynamic disturbance coefficient. Simultaneously, piezoelectric sensors embedded in the trench wall soil receive stress wave signals and identify micro-fracture events to determine soil micro-fracture activation characteristic values. These two are then fused to obtain a transient disturbance coupling index, enabling coordinated monitoring of lifting load disturbance and soil internal damage evolution. The lifting speed range is graded and controlled according to the risk level and this index. At high risk levels, the speed range is proportionally reduced; at low risk levels, when disturbance exceeds limits, the speed range is directly switched to low speed. This allows the construction rhythm to respond in real-time to changes in soil condition, effectively suppressing damage accumulation and trench collapse risks. This invention integrates pre-assessment, real-time monitoring, and dynamic control to form a complete construction quality control process. It is not only applicable to the hoisting of prefabricated components for prefabricated support structures in foundation pits, but can also be extended to scenarios such as subway tunnel segment assembly, integrated utility tunnel segment installation, and bridge prefabricated pier construction, providing a general technical path for the safe construction of various prefabricated components under complex geological conditions.

[0029] It is understood that the quality control method proposed in this invention is not only applicable to the hoisting construction of prefabricated support structures in foundation pit engineering, but can also be extended to the installation of prefabricated components in other geotechnical engineering fields. For example, in scenarios such as the assembly of shield tunnel segments in subway tunnels, the hoisting of prefabricated segments in integrated utility tunnels, and the installation of prefabricated bridge piers, common problems such as differences in geological conditions, component manufacturing deviations, and the coupling of hoisting disturbance and soil response are also faced. By obtaining the soil mechanical parameters and measured parameters of components in the corresponding scenarios, a risk classification and dynamic threshold adjustment mechanism is established. By combining load monitoring and soil response monitoring to construct coupling indicators and implement feedback control, a general quality control framework can be provided for various prefabricated component hoisting operations. In addition, the piezoelectric sensor stress wave monitoring technology used in this method can also be combined with wireless transmission or edge computing to realize real-time processing of monitoring data and remote push of early warning information, providing technical support for smart construction sites and remote construction monitoring and management.

[0030] Specifically, obtaining the soil mechanical parameters within the affected area of ​​precast component hoisting to determine the geological disturbance characteristic values ​​includes, Several geological survey points are set up at preset intervals within the area affected by the hoisting of precast components, and the soil shear strength parameters and soil compression modulus parameters are obtained at each survey point. Calculate the coefficient of variation of the soil shear strength parameter and the coefficient of variation of the soil compression modulus parameter at each survey point. Then, perform a weighted sum of the coefficients of variation of the shear strength parameter and the coefficient of variation of the compression modulus parameter to determine the geological disturbance characteristic value.

[0031] Specifically, the preset spacing is determined comprehensively based on the planar dimensions of the foundation pit and the geological complexity. For foundation pit projects of conventional scale, the spacing between adjacent survey points should not be too large to ensure effective capture of local changes in soil mechanical parameters within the area affected by hoisting; at the same time, it should not be too small to avoid unnecessary survey workload. Considering both survey accuracy and implementation cost, the preferred range for the preset spacing is [3 meters, 10 meters].

[0032] Specifically, by transforming the spatial variation characteristics of soil mechanical parameters within the lifting influence area into quantifiable geological disturbance characteristic values, and transforming the geometric deviation distribution law of prefabricated components along the length direction into component morphological characteristic values, a comprehensive characterization of the impact of geological condition uncertainty and component manufacturing deviation on lifting operations is achieved. This provides data basis for subsequent determination of risk coefficients, adjustment of dynamic lifting thresholds, and matching of lifting speed ranges, enabling the lifting scheme to be adaptively optimized according to actual geological conditions and component status, thereby improving the accuracy of risk assessment and the rationality of lifting speed control.

[0033] Specifically, obtaining the design and measured shape parameters of the prefabricated components to determine their morphological characteristic values ​​includes: Several test sections are selected along the length of the precast component at preset intervals, and the design and measured dimensions of each test section are obtained. Calculate the difference between the designed and measured dimensions of each test section to determine the local deviation value corresponding to each test section; The number of detection sections whose local deviation values ​​exceed a preset deviation threshold is counted, the average value of the local deviation values ​​corresponding to each detection section that exceeds the preset deviation threshold is calculated, and the component morphological feature value is determined based on the number of detection sections and the average value of the local deviation values.

[0034] Specifically, the difference between the designed and measured dimensions of each test section is calculated, and the ratio of this difference to the designed dimension is used to determine the proportion of the local deviation area for each test section. Since precast components are typically irregularly shaped, a dimensional difference in a single direction is insufficient to fully reflect the actual degree of deviation of the section. Calculating the ratio of the difference between the designed and measured section areas to the designed section area provides a more accurate characterization of the local deformation features of the section. A larger proportion of the local deviation area indicates a greater deviation between the component's shape at that section and the design requirements, correspondingly increasing the risk of eccentric loads or jamming with the trench wall during hoisting.

[0035] Specifically, by conducting multi-section inspection and deviation analysis along the length of the precast component, the distribution pattern of geometric deviations generated during the component manufacturing process is transformed into quantifiable component morphological characteristic values. This achieves a comprehensive characterization of the deviation between the actual geometric state and the ideal design state of the precast component. By calculating the proportion of local deviation areas, the limitation of single-direction dimensional differences in fully reflecting the actual deviations of irregular sections is overcome. This allows the component morphological characteristic values ​​to more accurately reflect the potential risks of eccentric loads or interference with the trench wall during hoisting. This provides a basis for determining the risk coefficient of the component, adjusting the dynamic hoisting threshold, and matching the hoisting speed range. As a result, the hoisting scheme can be adaptively optimized according to the actual manufacturing quality of the component, avoiding hoisting instability or abnormal disturbance to the trench wall soil caused by component geometric defects.

[0036] Specifically, a geological risk coefficient is determined based on the geological disturbance characteristic values, a component risk coefficient is determined based on the component morphology characteristic values, and a comprehensive hoisting risk value is determined based on the geological risk coefficient and the component risk coefficient. The ratio of the geological disturbance characteristic value to the preset geological disturbance standard value is determined as the geological risk coefficient; The ratio of the component morphological feature value to the preset component morphological standard value is determined as the component risk coefficient; The comprehensive hoisting risk value is determined based on the geological risk coefficient and the component risk coefficient.

[0037] Specifically, the preset geological disturbance standard value is determined statistically based on the results of multiple sets of soil mechanical parameter tests conducted under standard test conditions. The standard test conditions use a uniform, dense, medium-hardness clay layer as the reference stratum. Within this stratum, soil shear strength and compression modulus parameters are obtained using the same survey point layout as in the field. The coefficients of variation for each parameter are calculated and weighted summed. The average value of multiple test results is taken as the geological disturbance standard value, used to characterize the reference disturbance level under ideal uniform stratum conditions.

[0038] Specifically, the preset component morphology standard value is determined based on the statistical analysis of quality inspection data of prefabricated components under standard production conditions. Standard production conditions use components produced with stable molds and standardized curing processes as a benchmark. The design and measured dimensions of each section are obtained using the same inspection cross-section layout as on-site. The proportion of local deviation areas is calculated, and their distribution characteristics are statistically analyzed. The average value of the inspection results from multiple batches of qualified components is taken as the component morphology standard value, used to characterize the reference deviation level under normal production conditions.

[0039] Specifically, the weighted sum of the geological risk coefficient and the component risk coefficient is determined as the comprehensive hoisting risk value. The weighting weight of the geological risk coefficient is 0.65, and the weighting weight of the component risk coefficient is 0.35. The geological risk coefficient reflects the dispersion of the geological mechanical parameters in the hoisting influence area, determines the ease with which the trench wall deforms when disturbed, and is the dominant factor affecting hoisting safety, hence it is given a higher weight. The component risk coefficient reflects the degree of deviation in the shape of the precast component itself; its influence needs to be transferred through the load during the hoisting process to act on the trench wall, and is an indirect factor, hence it is given a relatively lower weight.

[0040] Please see Figure 2 The diagram shown is a logic block diagram for determining the hoisting speed range according to an embodiment of the present invention. The risk level of the current hoisting operation is determined based on the comprehensive hoisting risk value and the dynamic hoisting threshold to match the corresponding hoisting speed range, including: The dynamic hoisting threshold is determined based on the component morphological characteristic values; Compare the overall lifting risk value with the dynamic lifting threshold; If the comprehensive lifting risk value is greater than or equal to the dynamic lifting threshold, the current lifting operation is determined to be of a high-risk level and matched with the first lifting speed range; If the overall lifting risk value is less than the dynamic lifting threshold, the current lifting operation is determined to be of low risk level, and the second lifting speed range is used. Wherein, the hoisting speed corresponding to the first hoisting speed range is lower than the hoisting speed corresponding to the second hoisting speed range.

[0041] Specifically, the first and second hoisting speed ranges are determined comprehensively based on the performance parameters of the hoisting equipment, the weight class of the prefabricated components, and engineering practice experience. The upper limit of the first hoisting speed range shall not exceed 80% of the lower limit of the second hoisting speed range to ensure sufficient safety margin for hoisting operations under high-risk levels. The specific numerical ranges of the first and second hoisting speed ranges can be adaptively adjusted according to site conditions during project implementation to adapt to the operational needs of different scale foundation pit projects and different types of hoisting equipment, which will not be elaborated further here.

[0042] Specifically, determining the dynamic hoisting threshold based on the component morphological feature values ​​includes, The ratio of the component morphological feature value to the preset component morphological standard value is calculated as an adjustment coefficient; The product of the preset base threshold and the adjustment coefficient is determined as the dynamic hoisting threshold.

[0043] Specifically, the basic threshold is determined based on statistical results of historical hoisting operation data under standard working conditions. Several groups of hoisting conditions with uniform geological conditions and component deviations within acceptable ranges are selected as benchmark samples. The comprehensive hoisting risk value of each sample during the hoisting process is obtained, and the average and standard deviation of the comprehensive hoisting risk values ​​of all samples are calculated. The sum of the average and twice the standard deviation is determined as the basic threshold. This invention uses the sum of the average and twice the standard deviation as the basic threshold, which can effectively identify potentially high-risk working conditions that deviate from the normal state while ensuring that normal construction is not frequently disturbed, providing a reasonable benchmark reference for subsequent dynamic adjustments based on component morphological characteristics.

[0044] Specifically, the aforementioned technical features constitute the core of the risk classification and control mechanism of this invention. By comparing the comprehensive hoisting risk value with a hoisting threshold dynamically adjusted based on component morphological characteristics, a quantitative determination of the risk level of the current hoisting operation is achieved. For operations determined to be high-risk, a lower first hoisting speed range is matched to reduce the disturbance intensity to the trench wall during hoisting; for low-risk operations, a higher second hoisting speed range is matched to balance construction efficiency with safety. The upper limit of the first hoisting speed range does not exceed 80% of the lower limit of the second hoisting speed range. This ratio provides sufficient safety redundancy for high-risk conditions, avoiding the accumulation of disturbance due to excessively fast hoisting speed. The basic threshold is calculated by adding twice the standard deviation to the average value obtained from historical data statistics. This ensures that most operations under normal conditions can pass the judgment smoothly, while also effectively identifying abnormal conditions that deviate from the normal state. The dynamic hoisting threshold is multiplied by an adjustment coefficient related to the component morphological characteristics, allowing the risk judgment standard to be personalized according to the actual manufacturing precision of the component, avoiding misjudgment or omission of risk due to component deviations. Through the coordination of the above steps, the present invention completes the differentiated matching of risk level classification and hoisting speed range before the start of hoisting operation, providing a basis for real-time monitoring and dynamic control in the subsequent construction process.

[0045] Please see Figure 3 As shown, this is a logic block diagram for determining the size of the sliding time window according to an embodiment of the present invention. The step of acquiring load fluctuation data at the bottom of the hook using the sliding time window and determining the hoisting dynamic disturbance coefficient based on the load fluctuation data includes: Identify the current risk level. If the current hoisting operation is at a high risk level, the sliding time window takes the first duration; if the current hoisting operation is at a low risk level, the sliding time window takes the second duration. Time-domain analysis is performed on the real-time acquired load fluctuation data at the bottom of the hook. The maximum and minimum load values ​​within the sliding time window are extracted, and the difference between the maximum and minimum load values ​​is calculated as the instantaneous fluctuation amplitude. The instantaneous fluctuation amplitudes of several consecutive time windows are statistically analyzed, and the average value of each instantaneous fluctuation amplitude is calculated as the hoisting dynamic disturbance coefficient. Wherein, the first duration is shorter than the second duration.

[0046] Specifically, the first duration is set based on the monitoring sensitivity requirements under high-risk levels, and is between 3 and 5 seconds. High-risk levels correspond to working conditions with complex geological conditions or large component deviations. During hoisting, the load fluctuation frequency is high and the amplitude changes drastically, requiring a shorter time window to capture instantaneous disturbance characteristics in a timely manner, avoiding the risk signal being masked by excessive data smoothing. The second duration is set based on the monitoring stability requirements under low-risk levels, and is between 10 and 20 seconds. Low-risk levels correspond to working conditions with uniform geological conditions and small component deviations. Load fluctuations are relatively gentle, and a longer time window can effectively filter out random noise interference, making the extracted instantaneous fluctuation amplitude more representative and reducing the probability of false alarms. In this embodiment of the invention, combined with field test data and engineering experience, the first duration is preferably 3 seconds, and the second duration is preferably 10 seconds. Those skilled in the art can adjust the duration appropriately according to the actual situation, which will not be elaborated here.

[0047] Specifically, acquiring stress wave signals embedded in the soil of the trench wall to determine the activation characteristic values ​​of soil microfractures includes, Energy calculation is performed on the stress wave signal acquired in real time by the piezoelectric sensor, and waveform segments with signal energy values ​​exceeding the preset energy threshold are identified and marked as micro-fracture events. The frequency of micro-fracture events was determined as the activation characteristic value of soil micro-fracture.

[0048] Specifically, by using piezoelectric sensors embedded in the soil of the trench wall to collect stress wave signals in real time, the elastic wave energy released during the initiation or propagation of microcracks caused by hoisting disturbances in the soil is converted into identifiable electrical signals. By calculating the signal energy value and threshold discrimination, the automatic identification and statistics of micro-fracture events in the soil are realized. The frequency of micro-fracture events is then quantified into micro-fracture activation characteristic values ​​of the soil. This overcomes the limitation of traditional surface observation methods in capturing the early fracture evolution trend of deep structures in the trench wall, enabling the safety assessment of hoisting operations to be predicted from the perspective of the stability of the internal structure of the soil, and timely detection of early signs of trench collapse risk.

[0049] Specifically, the preset energy threshold is determined comprehensively based on the physical and mechanical properties of the trench wall soil and the sensitivity characteristics of the piezoelectric sensor. For soil layers with rapid energy decay, such as soft clay or loose sand, the preset energy threshold is lowered accordingly to capture weak signals; for soil layers with long energy propagation, such as dense sand or hard clay, the preset energy threshold is raised accordingly to filter environmental noise interference. Those skilled in the art can optimize and adjust the specific value of the preset energy threshold through on-site calibration tests before engineering implementation to ensure the accuracy and reliability of micro-fracture event identification, which will not be elaborated further here.

[0050] Specifically, determining the transient disturbance coupling index based on the hoisting dynamic disturbance coefficient and the soil micro-fracture activation characteristic value includes, The ratio of the hoisting dynamic disturbance coefficient to the preset hoisting dynamic disturbance standard value is determined as the hoisting disturbance coefficient; The ratio of the soil micro-fracture activation characteristic value to the preset micro-fracture activation standard value is determined as the micro-fracture activation coefficient; The weighted sum of the hoisting disturbance coefficient and the micro-fracture activation coefficient is used to determine the transient disturbance coupling index.

[0051] Specifically, the standard value of hoisting dynamic disturbance is determined statistically based on the load fluctuation characteristics of the hoisting equipment under stable operating conditions. Several sets of working conditions with uniform geological conditions, stable hoisting speed, and no trench wall disturbance are selected as benchmark samples. Load fluctuation data at the bottom of the hook are collected for each sample during the hoisting process, and the average value of its instantaneous fluctuation amplitude is calculated as the disturbance level of the working condition. The average value of the disturbance levels of multiple working conditions is taken as the standard value of hoisting dynamic disturbance, which is used to characterize the benchmark reference value of load fluctuation under normal hoisting conditions.

[0052] Specifically, the micro-fracture activation standard value is determined statistically based on the stress wave signal characteristics of the trench wall soil under conditions of no or minimal disturbance during hoisting. During the period after the excavation of the foundation pit is completed but before hoisting operations are carried out, background stress wave signals are continuously collected by piezoelectric sensors embedded in the trench wall soil. The signal energy distribution and the frequency of micro-fracture events are analyzed, and the statistical average value of the frequency of micro-fracture events per unit time is used as the micro-fracture activation standard value to characterize the micro-fracture status of the trench wall soil under static stability.

[0053] Specifically, the weighted summation coefficient of the hoisting disturbance coefficient is set to 0.4, and the weighted summation coefficient of the micro-fracture activation coefficient is set to 0.6. The hoisting dynamic disturbance coefficient reflects the intensity of disturbance exerted on the trench wall by the external load input during hoisting, and is a driving factor inducing soil damage; the soil micro-fracture activation characteristic value directly characterizes the degree of evolution of internal structural damage in the soil, and is a core indicator for judging the stability state of the trench wall. Assigning a higher weight to the micro-fracture activation coefficient allows the transient disturbance coupling index to focus more on reflecting the actual response state of the soil, avoiding misjudgments caused by short-term fluctuations in the hoisting load.

[0054] Please see Figure 4 As shown, this is a logic block diagram of an embodiment of the present invention for determining whether there is a risk of collapse based on the transient disturbance coupling and disturbance threshold. The step of adjusting the subsequent hoisting speed range based on the risk level and transient disturbance coupling index includes: Obtain a preset disturbance threshold corresponding to the current risk level, and compare the transient disturbance coupling index with the preset disturbance threshold; If the transient disturbance coupling index is greater than the disturbance threshold, it is determined that there is a risk of trench collapse, and the subsequent hoisting speed range is adjusted according to the current risk level. If the current hoisting operation is of a high-risk level and there is a risk of trench collapse, the upper and lower limits of the first hoisting speed range are multiplied by the ratio of the preset disturbance threshold to the transient disturbance coupling index to obtain the adjusted hoisting speed range after reduction. If the current hoisting operation is of low risk level but there is a risk of trench collapse, adjust the current hoisting speed range to the first hoisting speed range.

[0055] Specifically, the transient disturbance coupling index is obtained by weighted summation of the hoisting disturbance coefficient and the micro-fracture activation coefficient, both of which are ratios of the actual monitored value to the standard value. The hoisting disturbance coefficient typically fluctuates around 1 under stable hoisting conditions. When significant swaying or jamming occurs during hoisting, this coefficient rises to the range of 1.2-1.5. Similarly, the micro-fracture activation coefficient is also around 1 under stable soil conditions. When internal soil damage intensifies, this coefficient can rise to 1.5-2.0 or even higher. After weighted calculation, the transient disturbance coupling index generally fluctuates within the range of 0.9-1.25 under normal operating conditions. When both hoisting disturbance and soil fracturing intensify simultaneously, the transient disturbance coupling index can rise to the range of 1.4-1.8, and in extreme cases, may reach above 2.0. Based on these distribution characteristics, the disturbance threshold can be set to [1.3, 1.6], preferably 1.4.

[0056] The technical solution of the present invention has been described above with reference to the preferred embodiments shown in the accompanying drawings. However, it will be readily understood by those skilled in the art that the scope of protection of the present invention is obviously not limited to these specific embodiments. Without departing from the principles of the present invention, those skilled in the art can make equivalent changes or substitutions to the relevant technical features, and the technical solutions after these changes or substitutions will all fall within the scope of protection of the present invention.

Claims

1. A method for managing construction quality of a foundation pit work, characterized by, include: Obtain the soil mechanical parameters within the area affected by the hoisting of precast components to determine the geological disturbance characteristic value; obtain the design shape parameters and measured shape parameters of the precast components to determine the component morphological characteristic value. The geological risk coefficient is determined based on the geological disturbance characteristic value, the component risk coefficient is determined based on the component morphology characteristic value, and the comprehensive hoisting risk value is determined based on the geological risk coefficient and the component risk coefficient. The risk level of the current lifting operation is determined based on the comprehensive lifting risk value and the dynamic lifting threshold, so as to match the corresponding lifting speed range; The hoisting process is controlled according to the aforementioned hoisting speed range. Load fluctuation data at the bottom of the hook is acquired using a sliding time window. The hoisting dynamic disturbance coefficient is determined based on the load fluctuation data, including... If the current hoisting operation is at a high risk level, the sliding time window will take the first duration; if the current hoisting operation is at a low risk level, the sliding time window will take the second duration. Time-domain analysis is performed on the real-time acquired load fluctuation data at the bottom of the hook. The maximum and minimum load values ​​within the sliding time window are extracted, and the difference between the maximum and minimum load values ​​is calculated as the instantaneous fluctuation amplitude. The instantaneous fluctuation amplitudes of several consecutive time windows are statistically analyzed, and the average value of each instantaneous fluctuation amplitude is calculated as the hoisting dynamic disturbance coefficient. Stress wave signals were collected from the soil embedded in the trench wall to determine the activation characteristics of soil micro-fractures. The transient disturbance coupling index is determined based on the hoisting dynamic disturbance coefficient and the soil micro-fracture activation characteristic value. The subsequent hoisting speed range is adjusted based on the risk level and the transient disturbance coupling index. The dynamic hoisting threshold is determined based on the component morphological characteristic value, and the size of the sliding time window is related to the risk level type; the first duration is less than the second duration.

2. The foundation work construction quality management method according to claim 1, characterized by, The process of obtaining soil mechanical parameters within the affected area of ​​precast component hoisting to determine geological disturbance characteristic values ​​includes, Several geological survey points are set up at preset intervals within the area affected by the hoisting of precast components, and the soil shear strength parameters and soil compression modulus parameters are obtained at each survey point. Calculate the coefficient of variation of the soil shear strength parameter and the coefficient of variation of the soil compression modulus parameter at each survey point. Then, perform a weighted sum of the coefficients of variation of the shear strength parameter and the coefficient of variation of the compression modulus parameter to determine the geological disturbance characteristic value.

3. The foundation work construction quality management method according to claim 1, characterized by, The process of obtaining the design and measured shape parameters of the prefabricated components to determine their morphological characteristic values ​​includes... Several test sections are selected along the length of the precast component at preset intervals, and the design and measured dimensions of each test section are obtained. Calculate the difference between the designed and measured dimensions of each test section to determine the local deviation value corresponding to each test section; The number of detection sections whose local deviation values ​​exceed a preset deviation threshold is counted, the average value of the local deviation values ​​corresponding to each detection section that exceeds the preset deviation threshold is calculated, and the component morphological feature value is determined based on the number of detection sections and the average value of the local deviation values.

4. The foundation work construction quality management method according to claim 1, characterized by, A geological risk coefficient is determined based on the geological disturbance characteristic values, a component risk coefficient is determined based on the component morphology characteristic values, and a comprehensive hoisting risk value is determined based on the geological risk coefficient and the component risk coefficient. The ratio of the geological disturbance characteristic value to the preset geological disturbance standard value is determined as the geological risk coefficient; The ratio of the component morphological feature value to the preset component morphological standard value is determined as the component risk coefficient; The comprehensive hoisting risk value is determined based on the geological risk coefficient and the component risk coefficient.

5. The foundation work construction quality management method according to claim 1, characterized by, The risk level of the current lifting operation is determined based on the comprehensive lifting risk value and the dynamic lifting threshold, in order to match the corresponding lifting speed range, including: The dynamic hoisting threshold is determined based on the component morphological characteristic values; Compare the overall lifting risk value with the dynamic lifting threshold; If the comprehensive lifting risk value is greater than or equal to the dynamic lifting threshold, the current lifting operation is determined to be of a high-risk level and matched with the first lifting speed range; If the overall lifting risk value is less than the dynamic lifting threshold, the current lifting operation is determined to be of low risk level, and the second lifting speed range is used. Wherein, the hoisting speed corresponding to the first hoisting speed range is lower than the hoisting speed corresponding to the second hoisting speed range.

6. The foundation work construction quality management method according to claim 5, characterized by, The determination of the dynamic hoisting threshold based on the component morphological feature values ​​includes, The ratio of the component morphological feature value to the preset component morphological standard value is calculated as an adjustment coefficient; The product of the preset base threshold and the adjustment coefficient is determined as the dynamic hoisting threshold.

7. The foundation work construction quality management method according to claim 1, characterized by, The process of acquiring stress wave signals embedded in the soil of the trench wall to determine the activation characteristic values ​​of soil microfractures includes, Energy calculation is performed on the stress wave signal acquired in real time by the piezoelectric sensor, and waveform segments with signal energy values ​​exceeding the preset energy threshold are identified and marked as micro-fracture events. The frequency of micro-fracture events was determined as the activation characteristic value of soil micro-fracture.

8. The method for quality management of foundation pit construction according to claim 1, characterized in that, The determination of the transient disturbance coupling index based on the hoisting dynamic disturbance coefficient and the soil micro-fracture activation characteristic value includes... The ratio of the hoisting dynamic disturbance coefficient to the preset hoisting dynamic disturbance standard value is determined as the hoisting disturbance coefficient; The ratio of the soil micro-fracture activation characteristic value to the preset micro-fracture activation standard value is determined as the micro-fracture activation coefficient; The weighted sum of the hoisting disturbance coefficient and the micro-fracture activation coefficient is used to determine the transient disturbance coupling index.

9. The method for quality management of foundation pit construction according to claim 1, characterized in that, The adjustment of the subsequent hoisting speed range based on risk level and transient disturbance coupling index includes, Obtain a preset disturbance threshold corresponding to the current risk level, and compare the transient disturbance coupling index with the preset disturbance threshold; If the transient disturbance coupling index is greater than the disturbance threshold, it is determined that there is a risk of trench collapse, and the subsequent hoisting speed range is adjusted according to the current risk level. If the current hoisting operation is of a high-risk level and there is a risk of trench collapse, the upper and lower limits of the first hoisting speed range are multiplied by the ratio of the preset disturbance threshold to the transient disturbance coupling index to obtain the adjusted hoisting speed range after reduction. If the current hoisting operation is of low risk level but there is a risk of trench collapse, adjust the current hoisting speed range to the first hoisting speed range.

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