Three-dimensional live-action model construction system for building structure demolition and reconstruction

Abstract

The invention relates to the technical field of data processing, in particular to a three-dimensional live-action model construction system for building structure demolition and reconstruction. The system extracts event combinations with obvious transfer correlation features for localization and analysis of dangerous sound sources. Through simulation of transmission of test waves in an initial acoustic slowness field, a most probable dangerous sound source position for generating a current event combination under each test wave is obtained by using an objective function optimization solution method. And further performing statistics on simulation information of all test waves, establishing a residual curve, screening out a brittleness event position based on feature extraction, and adjusting a slowness value in a current voxel through various parameters corresponding to the brittleness event position in an initial acoustic slowness field. Through the dynamic correction method, the obtained corrected acoustic slowness field can more effectively represent the specific change of the building structure in the dismantling process, so that the internal damage can be more clearly quantified.

Description

Technical Field

[0001] This invention relates to the field of data processing technology, specifically to a three-dimensional real-scene model construction system for the demolition and renovation of building structures. Background Technology

[0002] Existing 3D reality modeling systems for building structure demolition and renovation typically integrate 3D laser scanning with Building Information Modeling (BIM), such as using BIM models to provide automated cutting equipment with the geometric shape and spatial positioning guidance of components. However, such systems face a technical bottleneck in ensuring operational safety: the model can only represent the design geometry of the component and cannot perceive and assess in real time the dynamic evolution of its unknown internal stress state under long-term service and cutting disturbances. When the cutting process disturbs the existing stress field inside the component, it may lead to a sudden stress release, causing brittle failure or equipment rebound, posing a safety hazard.

[0003] Acoustic emission technology has been applied as a dynamic non-destructive monitoring method to explore the internal state of structures. The principle is that the formation and propagation of microcracks inside the material release energy in the form of high-frequency elastic waves, which can be captured by an array of sensors placed on the surface of the component.

[0004] However, when applying acoustic emission technology to the cutting monitoring of highly heterogeneous composite materials such as reinforced concrete, existing technologies face the problem of confusion between propagation path effects and sound source mechanisms. When elastic waves encounter media with vastly different acoustic properties, such as reinforcing steel and coarse aggregate, they undergo complex refraction and diffraction, resulting in severe signal waveform distortion. This makes it difficult for localization algorithms to distinguish whether an abnormal signal is generated by a dangerous sound source (such as a brittle crack) or by interference from complex propagation paths, leading to inaccurate localization results. Even if the sound source can be roughly located, existing technologies lack an effective method to interpret the inherent energy release pattern of the signal itself; that is, they cannot distinguish whether an event is a tensile fracture indicating macroscopic cracking or a frictional slip representing energy dissipation. This indiscriminate localization result makes risk assessment lack a basis and cannot provide reliable support for safety decisions. Summary of the Invention

[0005] To address the technical problem that existing technologies for constructing 3D models during building demolition and renovation cannot effectively locate hazardous sound sources or distinguish the types of hazards at the sound source locations, the present invention aims to provide a 3D real-scene model construction system for building demolition and renovation. The specific technical solution adopted is as follows:

[0006] This invention proposes a three-dimensional real-scene model construction system for the demolition and renovation of building structures. The system includes a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, it implements the following method:

[0007] The voltage signals of multiple piezoelectric acoustic sensors on building components are acquired during the cutting process; multiple discrete events under the corresponding sensors are selected based on the amplitude of the voltage signals.

[0008] By performing correlation analysis on the time differences of discrete events between different sensors and the distance between different sensors, event combinations with propagation relationships are obtained; there are no discrete events belonging to the same sensor in the event combinations;

[0009] A three-dimensional simulation is performed based on the prior parameters of the building components to obtain the initial acoustic slowness field. For each event combination, an objective function is constructed by using the residual between the simulated arrival time of the sensor position in the discrete event combination and the actual occurrence time of the discrete event. The objective function is optimized to obtain the sound source position and its generation time under each preset test wave. The residuals corresponding to the optimization solutions of all test waves are used to construct a residual curve. The sound source position and its generation time corresponding to the minimum value in the residual curve are used as the event position and event occurrence time of the event combination. The sharpness of the minimum value position on the residual curve is obtained.

[0010] Based on the energy and rise time of the first discrete event in the event combination on the voltage signal, and combined with the sharpness, it is determined whether the event location is a brittle event location; based on the difference between the event occurrence time of the brittle event location and the occurrence times of each discrete event in the corresponding event combination, the slowness value of the brittle event location in the initial acoustic slowness field is adjusted to obtain the corrected acoustic slowness field.

[0011] Furthermore, the method for filtering discrete events includes:

[0012] After filtering the voltage signal, each signal point is traversed sequentially according to the time sequence. If there is a signal point whose amplitude is greater than or equal to a preset amplitude threshold and continues to rise, and after reaching the peak, the signal points within the preset event duration range are continuously less than the amplitude threshold, then the corresponding event duration range is taken as the range of the discrete event.

[0013] Furthermore, the method for combining the events includes:

[0014] The discrete events are arranged sequentially according to the sensor location and time sequence to obtain a discrete event sequence. Each discrete event in the discrete event sequence is taken as a reference event. It is determined whether the occurrence time difference between the reference event and the discrete events corresponding to other sensors is within the expected time length condition. If the expected time length condition is met, it is determined that there is a correlation between the reference event and the corresponding discrete event. All discrete events are traversed, and the discrete events that are correlated with each other are formed into the event combination.

[0015] Furthermore, the expected time length condition is as follows:

[0016] Using the distance between the sensor corresponding to the benchmark event and other sensors as the numerator and the lowest sound wave propagation speed in the concrete medium as the denominator, the upper limit of the expected time length interval is obtained, and 0 is taken as the lower limit of the expected time length interval. The occurrence time of the discrete time in the correlation analysis is subtracted from the occurrence time of the benchmark event. If the difference in occurrence time obtained is within the expected time length interval, it is considered to meet the expected time length condition.

[0017] Furthermore, the method for obtaining the simulated arrival time includes:

[0018] The fast travel method is used as the numerical solver to solve for the shortest time from the location of the sound source to the location of the sensor in the initial acoustic slowness field. The solution result is the simulated arrival time.

[0019] Furthermore, the objective function takes the form of:

[0020] ;in, For test waves The location x of the sound source to be optimized and the generation time to be optimized The residual generated by the objective function; This represents the number of sensors included in the event combination. Let be the actual occurrence time of the discrete event corresponding to the i-th sensor in the event combination. For test waves The simulated arrival time from the sound source location x to the location of the i-th sensor.

[0021] Furthermore, the method for obtaining the sharpness includes:

[0022] Using the point corresponding to the minimum value in the residual curve as the center, find the nearest point to be analyzed to the left and right. The residual of the point to be analyzed is a preset multiple of the minimum residual. If there are two points to be analyzed, the product of the wave velocity difference of the test wave between the two points to be analyzed and the minimum residual is used as the sharpness. If there is no point to be analyzed on one side, the product of the wave velocity difference of the test wave between the point to be analyzed and the minimum point and the minimum residual is used as the sharpness.

[0023] Furthermore, the method for determining whether an event location is a fragile event location includes:

[0024] If the sharpness is greater than a preset sharpness threshold, the energy is less than a preset energy threshold, and the rise time is less than a preset time threshold, then the corresponding event position is set as a brittle event position.

[0025] Further, adjusting the slowness value of the brittle event location in the initial acoustic slowness field includes:

[0026] A set of equations is set up for each brittle event location. The equations for all brittle event locations are solved using algebraic reconstruction techniques to obtain the slowness adjustment for each voxel. This slowness adjustment is then added to the initial slowness value to obtain the adjusted slowness value for each voxel. The equations include the following forms: ;in Let be the length of the acoustic ray path propagating from the location of the k-th fragile event to the i-th sensor within the j-th voxel. It is the slowness value of the j-th voxel. For the number of voxels, Let be the actual occurrence time of the discrete event of the i-th sensor in the event combination corresponding to the k-th fragile event location. Let k be the time of occurrence of the event corresponding to the location of the kth fragile event. Let be the slowness adjustment amount of the j-th voxel.

[0027] Furthermore, after obtaining the corrected acoustic slowness field, the process also includes:

[0028] In the modified acoustic slowness field, the spatial voxel to which the brittle event location belongs is determined. The energy of the first discrete event in the event combination corresponding to all brittle event locations within the spatial voxel is accumulated to obtain the brittle damage accumulation energy of the spatial voxel. Based on the brittle damage accumulation energy, different damage levels are divided and the spatial voxel is stained and labeled.

[0029] The present invention has the following beneficial effects:

[0030] This invention uses voltage signals collected by piezoelectric acoustic sensors as the analysis object, extracting event combinations with obvious transmission correlation characteristics for the location and analysis of hazardous sound sources. In the process of sound source location and analysis, this invention simulates building components with different test wave velocities. By simulating the transmission of test waves in the initial acoustic slowness field, the most probable hazardous sound source location for each test wave combination is obtained using an objective function optimization method. This invention further statistically analyzes the simulation information of all test waves to establish residual curves. These residual curves use the test wave velocity as the independent variable and the occurrence time residual generated during the simulation as the dependent variable. Therefore, the minimum value information in the curve, especially the sharpness of the minimum value location, can characterize the event cause at that sound source location. If the energy release is concentrated and the sharpness is large, it indicates that the hazard type at the event location is a clear brittle event location. By screening brittle event locations, the signal anomalies of rapid tensile cracking of materials can be distinguished from the reasonable signal anomalies of aggregate frictional displacement, avoiding inaccurate location and differentiation caused by directly judging based on sensor signal amplitude. Furthermore, in the initial acoustic slowness field, the present invention adjusts the slowness value in the current voxel by adjusting the parameters corresponding to the location of the brittle event. Through a dynamic correction method, the obtained modified acoustic slowness field can more effectively characterize the specific changes in the building structure during the demolition process, and make the internal damage more clearly quantified. Attached Figure Description

[0031] To more clearly illustrate the technical solutions and advantages in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0032] Figure 1 This is a structural diagram of a three-dimensional real-scene model construction system for the demolition and renovation of building structures, provided in one embodiment of the present invention.

[0033] Figure 2 This is a flowchart illustrating the method implemented by a processor executing a computer program in a three-dimensional real-scene model construction system for the demolition and renovation of building structures, as provided in an embodiment of the present invention. Detailed Implementation

[0034] To further illustrate the technical means and effects adopted by the present invention to achieve its intended purpose, the following, in conjunction with the accompanying drawings and preferred embodiments, details the specific implementation, structure, features, and effects of a three-dimensional real-scene model construction system for building structure demolition and renovation proposed according to the present invention. In the following description, different "one embodiment" or "another embodiment" do not necessarily refer to the same embodiment. Furthermore, specific features, structures, or characteristics in one or more embodiments can be combined in any suitable form.

[0035] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention pertains.

[0036] The following description, in conjunction with the accompanying drawings, details the specific scheme of a three-dimensional real-scene model construction system for the demolition and renovation of building structures provided by this invention.

[0037] Please see Figure 1 The diagram illustrates a structural diagram of a three-dimensional real-scene model construction system for the demolition and renovation of building structures, according to an embodiment of the present invention. The system includes a memory 101, a processor 103, and a computer program 102 stored in the memory 101 and executable on the processor 103. Further, please refer to... Figure 2 It illustrates a flowchart of a method implemented by a processor executing a computer program in a three-dimensional real-scene model construction system for the demolition and renovation of building structures, according to an embodiment of the present invention. The method includes:

[0038] Step S1: Obtain the voltage signals of multiple piezoelectric acoustic sensors on the building component during the cutting process; filter out multiple discrete events under the corresponding sensors based on the voltage signal amplitude.

[0039] This invention aims to analyze the transmission correlation of acoustic wave information emitted by building components during demolition, and determine the location of the sound source and the final hazard type of the sound source based on time characteristics. Therefore, it is first necessary to acquire acoustic wave transmission information from the components. This invention employs piezoelectric acoustic sensors for information extraction, with an operating frequency range covering the typical frequency band of microcrack signals in concrete materials (e.g., 50kHz-400kHz). To construct an effective three-dimensional monitoring aperture, this invention uses four sensors, forming a non-coplanar spatial array. The sensor arrangement should avoid stress singularities such as the corners of the components and ensure that the array completely encloses the area to be cut. During installation, the contact surface between the sensor probe and the component surface must be smoothed and an acoustic coupling agent (such as vacuum grease) applied to ensure efficient transmission of elastic waves from the component to the sensor. The analog voltage signal acquired by the sensor is converted from analog to digital by a multi-channel synchronous data acquisition system, forming a digitized voltage signal. This voltage signal is a time-series signal, and its amplitude characterizes the transmission characteristics of the acoustic wave information.

[0040] It should be noted that in the specific implementation of this invention, considering that cutting equipment such as diamond wire saws generates strong mechanical vibration noise covering a wide frequency band due to friction with concrete and internal reinforcing steel during operation, and that high-frequency electromagnetic interference may also exist at the construction site, the amplitude of these noises is usually much larger than the weak elastic wave signals released when microcracks initiate and propagate within the component material. Therefore, a series of processing steps are necessary to separate these target signals from the strong noise background. This invention considers that the mechanical vibration noise generated by the cutting equipment is mainly concentrated in the low-frequency band (below 50kHz), while the electromagnetic interference at the site is usually in the high-frequency band (above 400kHz), which has a frequency band difference from the elastic wave signals generated by brittle cracks in the concrete material (usually in the range of 50kHz-400kHz). Therefore, a bandpass filtering method can be used to denoise the voltage signal. Specifically, a digital bandpass filter is applied to the voltage signal acquired by each sensor, such as a Butterworth filter or a Chebyshev filter, with a passband frequency range of 50kHz to 400kHz. The specific filtering algorithm is a well-known technique in the field of science and will not be described in detail here.

[0041] For building components, microcrack events generated at localized locations during demolition will manifest as a concentrated energy band in the signal. This signal manifestation is transient, discontinuous, and discrete. Therefore, this embodiment of the invention further filters out multiple discrete events collected by each sensor based on the amplitude of the voltage signal for subsequent location and qualitative analysis. Each acquired discrete event includes the corresponding signal segment in the voltage signal, the actual occurrence time corresponding to the event's starting point, and the rise time from the starting point to the peak point.

[0042] Preferably, in this embodiment of the invention, after filtering the voltage signal, each signal point is traversed sequentially according to the time sequence. If there is a signal point whose amplitude is greater than or equal to a preset amplitude threshold, and the signal points after reaching the peak value are continuously less than the amplitude threshold, then the range of included signal points is taken as the range of the discrete event. The event duration is set to 200 microseconds. That is, for a given signal point, starting from that signal point, the amplitude increase of subsequent signal points is monitored. After reaching the peak value, a decrease is observed. If all signal points within 200 microseconds after the peak value are less than the amplitude threshold, then the signal points between the starting signal point and the last signal point within the event duration range are considered as the signal points included in a discrete event. In other words, the discrete event in the voltage signal manifests as a band that first rises and then falls, and the occurrence time of the discrete event is the time corresponding to the starting signal point.

[0043] It should be noted that the statistical analysis and screening of the upward and downward trends of signal points in the signal are techniques well known to those skilled in the art, and will not be elaborated or limited here.

[0044] It should be noted that, in this embodiment of the invention, the amplitude threshold acquisition method includes: before the cutting operation officially begins, the system collects background noise data of the on-site environment for a period of time and calculates its root mean square (RMS) value. Three times this RMS value is used as the amplitude threshold.

[0045] It should be noted that the process implemented in this embodiment of the invention is a dynamic real-time analysis process. In this embodiment of the invention, a time window to be analyzed is set at real time. Within the time window to be analyzed, all discrete events generated by the voltage signals corresponding to all sensors are counted. Finally, the sensor position is used as the first priority sorting condition, and the occurrence time of the discrete event is used as the second priority sorting condition. All discrete events are encapsulated into a discrete event sequence for easy subsequent analysis.

[0046] Step S2: Perform correlation analysis on the time differences of discrete events between different sensors and the distance between different sensors to obtain event combinations with propagation relationships; there are no discrete events belonging to the same sensor in the event combinations.

[0047] Because of the different sensor locations, the sound waves generated by the unknown crack in the component as a sound source reach each sensor at different times. Therefore, for the same sound source, there should be a clear transmission chain between sensors, that is, there should be a clear transmission correlation between discrete events between sensors, mainly reflected in the time difference. Furthermore, if some discrete events, after time difference analysis, cannot be correlated with discrete events from other sensors, they can be considered noise events, and the collected effective information can be further refined. Therefore, this embodiment of the invention can perform correlation analysis on the time differences of discrete events between different sensors and the distance between different sensors to obtain event combinations with propagation relationships. Because the event combination reflects the transmission relationship between sensors, there are no discrete events belonging to the same sensor in the event combination; each discrete event corresponds to only one sensor. Since four sensors are set in this embodiment, the final event combination contains at least three discrete events corresponding to sensors. And since one sensor corresponds to multiple discrete events, multiple event combinations can be obtained, and each event combination can be regarded as the response result of a local hazardous sound source.

[0048] Preferably, in this embodiment of the invention, the event combination method includes:

[0049] Discrete events are arranged sequentially according to sensor location and time sequence to obtain a discrete event sequence. Each discrete event in the discrete event sequence is used as a reference event. It is determined whether the time difference between the reference event and the discrete events corresponding to other sensors is within the expected time length condition. If the expected time length condition is met, it is determined that there is a correlation between the reference event and the corresponding discrete event. All discrete events are traversed, and the discrete events with mutual correlation are combined to form the event combination. That is, the significance of the expected time length condition is that the normal event transmission pattern should be that the discrete event reflected by the later sensor should be later than the reference event compared to the previous reference sensor, and the time difference should be within a reasonable range. Specifically, in this embodiment of the invention, the expected time length condition is:

[0050] Using the distance between the sensor corresponding to the benchmark event and other sensors as the numerator and the lowest sound wave propagation speed in the concrete medium as the denominator, the upper limit of the expected time length interval is obtained, and 0 is taken as the lower limit of the expected time length interval. The occurrence time of the discrete time in the correlation analysis is subtracted from the occurrence time of the benchmark event. If the difference in occurrence time obtained is within the expected time length interval, it is considered to meet the expected time length condition.

[0051] Taking the first discrete event of the first sensor as the baseline event as an example, the expected time length condition is expressed by the formula: ,in Let h be the actual occurrence time of the h-th discrete event corresponding to the i-th sensor (excluding the first sensor). The actual occurrence time of the first discrete event of the first sensor. Let be the distance from the first sensor to the i-th sensor. This represents the lowest possible speed at which sound waves can propagate in concrete. It can be set to 3000 meters per second.

[0052] Step S3: Perform a three-dimensional simulation based on the prior parameters of the building components to obtain the initial acoustic slowness field; for each event combination, construct an objective function by using the residual between the simulated arrival time of the sensor position in the discrete event combination and the actual occurrence time of the discrete event using the test wave; optimize the objective function to obtain the sound source position and its generation time corresponding to each preset test wave; construct a residual curve from the residuals corresponding to the optimization solutions of all test waves; use the sound source position and its generation time corresponding to the minimum value in the residual curve as the event position and event occurrence time of the event combination; obtain the sharpness of the minimum value position on the residual curve.

[0053] Firstly, this embodiment of the invention can perform three-dimensional simulation based on the prior parameters of the building components to obtain the initial acoustic slowness field. This process can utilize existing technologies such as BIM models for prior parameter acquisition and three-dimensional modeling. Specifically, the system first reads the design information of the component to be cut, such as Building Information Modeling (BIM) or construction drawings, from which it obtains the concrete strength grade (e.g., C30) and the approximate layout of the reinforcing steel. Based on the concrete strength grade, the system queries the built-in material property database to obtain the theoretical average P-wave velocity corresponding to that grade of concrete. Subsequently, the system internally voxels the three-dimensional geometric model of the component, forming a three-dimensional mesh composed of a large number of cubic elements. The system assigns each voxel... An initial acoustic slowness is assigned, which is the reciprocal of the average wave velocity mentioned above. The set of slowness values ​​for all these voxels constitutes the initial acoustic slowness field. The initial acoustic slowness field is composed of voxels, each voxel representing a cubic unit, and each voxel corresponding to an initial slowness value.

[0054] Based on the initial acoustic slowness field, transmission simulation can be performed. This embodiment of the invention determines the location of abnormal sound sources such as cracks through test wave transmission simulation. First, this embodiment selects a batch of test waves belonging to the common P-wave velocity range in concrete. The P-wave velocity range can be between 3500 m / s and 4500 m / s, and can be gradually divided within this range using a preset step size to obtain all test wavelengths. In this embodiment, the step size is set to 10.

[0055] Since each event combination can be considered as a response of a sound source, this embodiment of the invention performs a transmission simulation for each event combination. First, it is necessary to assume a sound source location to be optimized and the time at which the sound source is generated. This assumed sound source location generates a test wave within the assumed time, which propagates along the components to each sensor in the event combination. Therefore, for each test wavelength, this embodiment of the invention employs an optimization method, constructing an objective function by using the residual between the simulated arrival time of the test wave at the sensor location in the discrete event combination and the actual occurrence time of the discrete events. That is, for a set of event locations and generation times, if this set of information is true, the difference between the time result simulated by the test wave and the actual occurrence time of the discrete events in the event combination should be small, indicating that this set of information is the "cause" that produces the "effect" of the event combination. Therefore, the objective function can be optimized to finally obtain the sound source location and its generation time corresponding to the minimum objective function.

[0056] Since there are multiple test waves, each test wave can generate a set of corresponding sound source locations and their generation times for the current event combination. Therefore, the residuals corresponding to the optimization solutions of all test waves can be used to construct a residual curve; the sound source location and generation time corresponding to the minimum value in the residual curve are used as the event location and event occurrence time of the event combination. Furthermore, at the minimum value location, the sharpness can be used as a reference for significant hazard factor characteristics. A rapidly tensioned microcrack occurring in a concrete matrix has an energy release process that approximates an ideal point source, and the residual curve will show a narrow and deep V-shaped trough, resulting in a large sharpness; conversely, if the sound source is a signal generated by the friction and displacement between coarse aggregate and mortar, the energy release process is more diffuse in space and time, and the corresponding residual curve will be flatter and wider, resulting in a lower sharpness.

[0057] Preferably, in this embodiment of the invention, the method for obtaining the simulated arrival time includes:

[0058] The fast travel method is used as the numerical solver to find the shortest time for the test wave to travel from the location of the sound source to the sensor location in the initial acoustic slowness field. The solution result is the simulated arrival time. This process is a well-known technique to those skilled in the art and will not be described in detail here.

[0059] Preferably, in this embodiment of the invention, the objective function takes the form of:

[0060] ;in, For test waves The location x of the sound source to be optimized and the generation time to be optimized are as follows: The residual generated by the objective function; This represents the number of sensors included in the event combination. Let be the actual occurrence time of the discrete event corresponding to the i-th sensor in the event combination. For test waves The simulated arrival time from the sound source location x to the location of the i-th sensor.

[0061] in, This refers to the measured propagation time from the sound source location to the sensor location. For the above formula, the smaller the final residual, the better the sound source location x and its generation time are. The more it matches the response factors reflected by the event combination.

[0062] In this embodiment of the invention, a nonlinear optimization algorithm is used to solve the above objective function, such as the Levenberg-Marquardt algorithm. The specific optimization algorithm is a well-known technique to those skilled in the art and will not be described in detail here.

[0063] Preferably, in this embodiment of the invention, the method for obtaining sharpness includes:

[0064] Using the point corresponding to the minimum value in the residual curve as the center, find the nearest points to be analyzed to the left and right. The residual of each point to be analyzed is a preset multiple of the minimum residual. If two points to be analyzed exist, the product of the wave velocity difference between the two points and the minimum residual is taken as the sharpness. If no point to be analyzed exists on one side, i.e., the minimum point is exactly at the beginning or end of the curve, the product of the wave velocity difference between the point to be analyzed and the minimum point and the minimum residual is taken as the sharpness. For sharpness, the smaller the wave velocity difference, the smaller the minimum residual, indicating that the shape corresponding to the minimum point on the residual curve is sharper.

[0065] Step S4: Based on the energy and rise time of the first discrete event in the event combination on the voltage signal, and combined with the sharpness, determine whether the event location is a brittle event location; based on the difference between the event occurrence time of the brittle event location and the occurrence times of each discrete event in the corresponding event combination, adjust the slowness value of the brittle event location in the initial acoustic slowness field to obtain the corrected acoustic slowness field.

[0066] Through the above steps, each event combination can correspond to an event location and an event occurrence time. If the sound source corresponding to the event location is a brittle crack signal, it will exhibit characteristics of short rise time, low energy in the voltage signal, and high sharpness; if it is friction noise, it will exhibit characteristics of high energy in the voltage signal, long rise time, and low sharpness. The two types show obvious differences in the above three characteristics. Therefore, based on the energy and rise time corresponding to the first discrete event in the event combination in the voltage signal, combined with the sharpness, it can be determined whether the event location is a brittle event location. After the determination, all event locations can be divided into brittle event locations and ductile event locations. Among them, brittle event locations correspond to brittle crack types, and ductile event locations correspond to aggregate friction types. In order to accurately and effectively standardize the specific process during the demolition of building components, the brittle crack type can be focused on in subsequent processes.

[0067] The initial acoustic slowness field is merely an ideal model constructed based on homogenization guesses from design specifications. However, the locations and event types of each sound source identified in this embodiment of the invention can characterize the actual information of the building components. Determining the type of sound source event can be considered as a known sound source detection of the specific medium within the building component. Therefore, medium inversion can be performed based on the obtained location and qualitative information to dynamically correct and optimize the initial acoustic slowness field. The difference between the event occurrence time at the location of a brittle event and the occurrence times of each discrete event in the corresponding event combination can be considered as the measured propagation time of the sensor. This measured propagation time can be used to reflect the transmission characteristics of the current building component medium and can be used to adjust the slowness value of the brittle event location in the initial acoustic slowness field to obtain a corrected acoustic slowness field.

[0068] Preferably, in this embodiment of the invention, the method for determining whether an event location is a brittle event location includes:

[0069] If the sharpness is greater than a preset sharpness threshold, the energy is less than a preset energy threshold, and the rise time is less than a preset time threshold, then the corresponding event location is set as a brittle event location. It should be noted that the sharpness threshold, energy threshold, and time threshold can all be adaptively set based on the prior medium parameters stored in the building component BIM model. Implementers can choose these thresholds according to the medium type; however, this embodiment of the invention does not elaborate on or limit their selection.

[0070] Preferably, in this embodiment of the invention, adjusting the slowness value of the brittle event location in the initial acoustic slowness field includes:

[0071] A set of equations is set up for each brittle event location. The equations for all brittle event locations are solved using algebraic reconstruction techniques to obtain the slowness adjustment amount for each voxel. The slowness adjustment amount is added to the initial slowness value to obtain the adjusted slowness value for each voxel.

[0072] Taking one equation from a set of equations for an event location as an example, its form includes: ;in Let be the length of the acoustic ray path propagating from the location of the k-th fragile event to the i-th sensor within the j-th voxel. It is the slowness value of the j-th voxel. For the number of voxels, Let be the actual occurrence time of the discrete event of the i-th sensor in the event combination corresponding to the k-th fragile event location. Let k be the time of occurrence of the event corresponding to the location of the kth fragile event. Let be the slowness adjustment amount of the j-th voxel.

[0073] It should be noted that for a set of equations, the number of individual equations is equal to the number of sensors in the event combination corresponding to the event location, that is, each equation corresponds to one sensor.

[0074] In this embodiment of the invention, considering that solving the above equation is a typical tomographic inversion problem, algebraic reconstruction technology or other iterative algorithms can be used to solve it. These are technical means well known to those skilled in the art, and will not be elaborated here.

[0075] It should be noted that, in a specific implementation of one embodiment of the present invention, in order to avoid frequent correction of the three-dimensional model, the occurrence of fragile events can be counted in real time during the above process. If the number of counted events reaches 200, the model correction process will be restarted to avoid the meaningless calculation caused by frequent correction.

[0076] Preferably, in one embodiment of the present invention, considering that although the modified acoustic slowness field can characterize the sound wave transmission characteristics of the component in real time, it cannot visualize the location and degree of danger of brittle events in the three-dimensional model, in order to further facilitate the analysis of building components by the staff, the analysis results are further visualized, specifically including:

[0077] Firstly, to facilitate visualization and annotation, the modified acoustic slowness field can be spatially meshed and divided into a series of equal-sized, adjacent three-dimensional cubic units, i.e., spatial voxels. In this embodiment of the invention, the side length of the spatial voxel is set to 5 centimeters, and the spatial voxel is a cube structure.

[0078] Further, in the modified acoustic slowness field, the spatial voxel to which the brittle event location belongs is determined. The energies of the first discrete event in the event combinations corresponding to all brittle event locations within the spatial voxel are accumulated to obtain the brittle damage accumulation energy of the spatial voxel. Based on the brittle damage accumulation energy, different damage levels are classified and the spatial voxels are labeled. That is, the greater the brittle damage accumulation capacity, the more likely brittle microcracks are repeatedly occurring in the local area corresponding to the spatial voxel inside the component, releasing a large amount of strain energy. This directly indicates that the material load-bearing capacity in this area is deteriorating, and is a precursor to the formation or propagation of macroscopic cracks. Therefore, this embodiment of the invention can set an energy range. If the accumulated energy of brittle damage is outside the energy range, it indicates that the corresponding spatial voxel is a normal and safe area, and it can be left unmarked or kept transparent. If it is inside the energy range, it indicates that there is a certain risk of degradation, and the spatial voxel can be marked and rendered in yellow or semi-transparent state. If it exceeds the energy range, it indicates that the spatial voxel is in a dangerous position, and attention should be paid to this area during construction to avoid safety hazards. It can be rendered in red or opaque state, and an alarm signal can be fed back. Finally, by observing the appearance, growth, and spatial aggregation pattern of red spatial voxels in the 3D model (e.g., whether the red voxels are arranged in a linear or planar shape), the staff can directly determine the location, range, and potential macroscopic crack propagation direction of the internal high-risk area, thereby providing a direct decision-making basis for dynamically adjusting the cutting path and avoiding structural instability risks.

[0079] In summary, this invention uses voltage signals acquired by piezoelectric acoustic sensors as the analysis object, selecting event combinations with obvious transmission correlation characteristics for the location and analysis of hazardous sound sources. By simulating the transmission of test waves in the initial acoustic slowness field, the most probable hazardous sound source location for the current event combination under each test wave is obtained using an objective function optimization method. Furthermore, the simulation information of all test waves is statistically analyzed to establish residual curves. Based on feature extraction, brittle event locations are screened. In the initial acoustic slowness field, the slowness values ​​in the current voxel are adjusted by adjusting various parameters corresponding to the brittle event locations. Through dynamic correction, this invention obtains a corrected acoustic slowness field that can more effectively characterize the specific changes in the building structure during demolition, allowing for clearer quantification of internal damage.

[0080] It should be noted that the order of the above embodiments of the present invention is merely for descriptive purposes and does not represent the superiority or inferiority of the embodiments. The processes depicted in the accompanying drawings do not necessarily require a specific or sequential order to achieve the desired result. In some embodiments, multitasking and parallel processing are also possible or may be advantageous.

[0081] The various embodiments in this specification are described in a progressive manner. The same or similar parts between the various embodiments can be referred to each other. Each embodiment focuses on describing the differences from other embodiments.

Claims

1. A three-dimensional real-scene model construction system for the demolition and renovation of building structures, the system comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that, When the processor executes the computer program, it implements the following method: The voltage signals of multiple piezoelectric acoustic sensors on building components are acquired during the cutting process; multiple discrete events under the corresponding sensors are selected based on the amplitude of the voltage signals. By performing correlation analysis on the time differences of discrete events between different sensors and the distance between different sensors, event combinations with propagation relationships are obtained; there are no discrete events belonging to the same sensor in the event combinations; A three-dimensional simulation is performed based on the prior parameters of the building components to obtain the initial acoustic slowness field. For each event combination, an objective function is constructed by using the residual between the simulated arrival time of the sensor position in the discrete event combination and the actual occurrence time of the discrete event. The objective function is optimized to obtain the sound source position and its generation time under each preset test wave. The residuals corresponding to the optimization solutions of all test waves are used to construct a residual curve. The sound source position and its generation time corresponding to the minimum value in the residual curve are used as the event position and event occurrence time of the event combination. To obtain the sharpness of the location of the minimum value on the residual curve; Based on the energy and rise time of the first discrete event in the event combination on the voltage signal, and combined with the sharpness, it is determined whether the event location is a brittle event location; based on the difference between the event occurrence time of the brittle event location and the occurrence times of each discrete event in the corresponding event combination, the slowness value of the brittle event location in the initial acoustic slowness field is adjusted to obtain the corrected acoustic slowness field.

2. The three-dimensional real-scene model construction system for the demolition and renovation of building structures according to claim 1, characterized in that, The filtering method for discrete events includes: After filtering the voltage signal, each signal point is traversed sequentially according to the time sequence. If there is a signal point whose amplitude is greater than or equal to a preset amplitude threshold and continues to rise, and after reaching the peak, the signal points within the preset event duration range are continuously less than the amplitude threshold, then the corresponding event duration range is taken as the range of the discrete event.

3. The three-dimensional real-scene model construction system for the demolition and renovation of building structures according to claim 1, characterized in that, The methods for combining the events include: The discrete events are arranged sequentially according to the sensor location and time sequence to obtain a discrete event sequence. Each discrete event in the discrete event sequence is taken as a reference event. It is determined whether the occurrence time difference between the reference event and the discrete events corresponding to other sensors is within the expected time length condition. If the expected time length condition is met, it is determined that there is a correlation between the reference event and the corresponding discrete event. All discrete events are traversed, and the discrete events that are correlated with each other are formed into the event combination.

4. The three-dimensional real-scene model construction system for the demolition and renovation of building structures according to claim 3, characterized in that, The expected time length condition is: Using the distance between the sensor corresponding to the benchmark event and other sensors as the numerator and the lowest sound wave propagation speed in the concrete medium as the denominator, the upper limit of the expected time length interval is obtained, and 0 is taken as the lower limit of the expected time length interval. The occurrence time of the discrete time in the correlation analysis is subtracted from the occurrence time of the benchmark event. If the difference in occurrence time obtained is within the expected time length interval, it is considered to meet the expected time length condition.

5. A three-dimensional real-scene model construction system for the demolition and renovation of building structures according to claim 1, characterized in that, The method for obtaining the simulated arrival time includes: The fast travel method is used as the numerical solver to solve for the shortest time from the location of the sound source to the location of the sensor in the initial acoustic slowness field. The solution result is the simulated arrival time.

6. A three-dimensional real-scene model construction system for the demolition and renovation of building structures according to claim 1, characterized in that, The objective function takes the following forms: ;in, For test waves The location x of the sound source to be optimized and the generation time to be optimized The residual generated by the objective function; This represents the number of sensors included in the event combination. Let be the actual occurrence time of the discrete event corresponding to the i-th sensor in the event combination. For test waves The simulated arrival time from the sound source location x to the location of the i-th sensor.

7. A three-dimensional real-scene model construction system for the demolition and renovation of building structures according to claim 1, characterized in that, The method for obtaining the sharpness includes: Using the point corresponding to the minimum value in the residual curve as the center, find the nearest point to be analyzed to the left and right. The residual of the point to be analyzed is a preset multiple of the minimum residual. If there are two points to be analyzed, the product of the wave velocity difference of the test wave between the two points to be analyzed and the minimum residual is used as the sharpness. If there is no point to be analyzed on one side, the product of the wave velocity difference of the test wave between the point to be analyzed and the minimum point and the minimum residual is used as the sharpness.

8. A three-dimensional real-scene model construction system for the demolition and renovation of building structures according to claim 1, characterized in that, The method for determining whether an event location is a fragile event location includes: If the sharpness is greater than a preset sharpness threshold, the energy is less than a preset energy threshold, and the rise time is less than a preset time threshold, then the corresponding event position is set as a brittle event position.

9. A three-dimensional real-scene model construction system for the demolition and renovation of building structures according to claim 1, characterized in that, The adjustment of the slowness value of the location of the brittle event in the initial acoustic slowness field includes: A set of equations is set up for each brittle event location. The equations for all brittle event locations are solved using algebraic reconstruction techniques to obtain the slowness adjustment for each voxel. This slowness adjustment is then added to the initial slowness value to obtain the adjusted slowness value for each voxel. The equations include the following forms: ;in Let be the length of the acoustic ray path propagating from the location of the k-th fragile event to the i-th sensor within the j-th voxel. It is the slowness value of the j-th voxel. For the number of voxels, Let be the actual occurrence time of the discrete event of the i-th sensor in the event combination corresponding to the k-th fragile event location. Let k be the time of occurrence of the event corresponding to the location of the kth fragile event. Let be the slowness adjustment amount of the j-th voxel.

10. A three-dimensional real-scene model construction system for the demolition and renovation of building structures according to claim 1, characterized in that, After obtaining the corrected acoustic slowness field, the process also includes: In the modified acoustic slowness field, the spatial voxel to which the brittle event location belongs is determined. The energy of the first discrete event in the event combination corresponding to all brittle event locations within the spatial voxel is accumulated to obtain the brittle damage accumulation energy of the spatial voxel. Based on the brittle damage accumulation energy, different damage levels are divided and the spatial voxel is stained and labeled.

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