CN116226965A

CN116226965A - Building waste estimation algorithm based on BIM

Info

Publication number: CN116226965A
Application number: CN202211690566.2A
Authority: CN
Inventors: 吴泽洲; 张淑惠; 赵祎彧; 江明阳; 何秋凤
Original assignee: Shenzhen University
Current assignee: Shenzhen University
Priority date: 2022-12-27
Filing date: 2022-12-27
Publication date: 2023-06-06

Abstract

The invention provides a building waste estimation algorithm based on BIM, and relates to the technical field of intelligent buildings; the algorithm comprises the following steps: s10, collecting point cloud data of a building, wherein the point cloud data comprise space geometric information of the building; s20, preprocessing point cloud data; s30, collecting material information of a building by utilizing ultrasonic waves; s40, fusing the material information and the space geometric information of the building, and performing reverse modeling to generate a BIM model; s50, estimating the waste yield of the building according to the BIM model; the beneficial effects of the invention are as follows: the method realizes the estimation of the production amount of the building demolition waste.

Description

Building waste estimation algorithm based on BIM

Technical Field

The invention relates to the technical field of intelligent buildings, in particular to a building waste estimation algorithm based on BIM.

Background

The level of urban level in China is rapidly improved, and continuous urban level promotes large-scale urban development and update transformation activities, and a large amount of construction wastes are generated while promoting social and economic development.

Demolishing waste is a main source of construction waste in China, and the proportion of demolishing waste is up to 70% -75%. The unit area production amount of demolishing waste is more than 50 times of that of newly built building waste. In addition, with the improvement of urban level and the enhancement of restriction of land resources, large-scale demolition and reconstruction of existing urban areas have become an important means for urban updating in urban construction represented by Beijing, shanghai, guangzhou, shenzhen and the like. For example, the area for reconstruction is nearly 1500 ten thousand m for the new city update unit demolition of Shenzhen city in recent three years ² . In the dismantling process, the whole building entity or the whole building entity is quickly 'flattened', the generated waste building materials are transported to a landfill or simply piled up after being simply sorted, and the dismantling and management mode greatly aggravates the difficulty of waste recycling, increases the management cost and aggravates the difficulty of environmental management. It is not difficult to foresee how to scientifically and reasonably estimate and predict the amount of construction waste generated and the composition of components to be removed, which is an urgent problem in the field of waste management research. The existing construction waste estimation methods and limitations are summarized mainly in the following table 1.

Disclosure of Invention

In order to overcome the defects of the prior art, the invention provides a building waste estimation algorithm based on B I M so as to realize the estimation of the generation amount of building demolition waste.

The technical scheme adopted for solving the technical problems is as follows: in a bim-based construction waste estimation algorithm, the improvement comprising the steps of:

s10, collecting point cloud data of a building, wherein the point cloud data comprise space geometric information of the building;

s20, preprocessing point cloud data;

s30, collecting material information of a building by utilizing ultrasonic waves;

s40, fusing the material information and the space geometric information of the building, and performing reverse modeling to generate a B I M model;

s50, estimating the waste yield of the building according to the BIM model.

Further, in step S10, the spatial geometry information includes external information of the building and high-altitude data.

Further, the step S20 includes the following steps:

using an algorithm to complete automatic splicing and manual splicing of cloud data of different scanning target points;

noise reduction processing is carried out on background points and mixed pixels in the point cloud data by using a self-filtering algorithm, and noise points in the point cloud data are removed;

and adjusting the volume of the point cloud model, and simplifying the point cloud data.

Further, for point cloud data containing holes, filling the point cloud data with the gaps by using a point cloud filling function;

the noise points comprise drift points, isolated points, redundant points and mixed points.

Further, the step S30 includes the following steps:

s301, receiving a material identification instruction, and transmitting an ultrasonic signal according to the material identification instruction;

s302, receiving a reflection signal generated by reflecting an ultrasonic signal by a target object, and calculating reflectivity according to the ultrasonic signal and the reflection signal;

s303, calculating the acoustic impedance of the target object according to the reflectivity;

s304, searching material information matched with the acoustic impedance of the target object in a preset material database.

Further, after the step S303, the method further includes the following steps:

s305, calculating delay time of the reflected signal according to the ultrasonic signal;

the step S304 includes: and searching material information matched with the acoustic impedance and the delay time of the target object in a preset material database.

Further, the ultrasonic signal in the step S301 includes a plurality of ultrasonic signals with different frequencies;

the step S302 includes: receiving a plurality of reflected signals generated by reflecting a plurality of ultrasonic signals by a target object;

acquiring received sound wave frequencies of a plurality of reflected signals;

the reflectivity is calculated from the received acoustic frequency and the emitted acoustic frequency of the plurality of ultrasonic signals.

Further, the preset material database comprises various common materials, acoustic impedance corresponding to the common materials, delay time parameters and preset material attribute information, wherein the preset material attribute information comprises injection names, density, hardness, melting points, ductility, thermal conductivity and electric conductivity of the materials.

Further, the step S40 includes the following:

s401, describing a contour line of a structure, opening transcoded point cloud data in Revit software, starting reverse modeling, drawing a contour line of a model according to the point cloud, and drawing a contour of a component according to the point cloud data;

s402, creating a model elevation and an axis network, selecting a proper step value according to actual conditions and collected drawing data, analyzing according to point cloud data in a frame selection range, acquiring the distribution condition of the point cloud in the range in the vertical height direction, and generating the elevation;

s403, creating a model main body, collecting the image and physical attribute information of each component on site, drawing a contour line according to the point cloud data and determining the external dimension of the contour line;

when the model is created, the similar family type files in the family library of the existing Revit software are edited and adjusted, the component family files meeting the actual conditions of the building are created, and then the component family files are imported into the Revit software and adjusted to the corresponding positions according to the point cloud data and the contour lines.

Further, the step S50 is:

the function parameter type of the RevitAPI is used for setting, the volume information of each material in the BIM model is automatically obtained, a calculation formula is built, and further automatic estimation of the construction waste is realized;

step S50 includes:

s501, setting function parameter types through a RevitAPI, automatically acquiring material information of all components from a BIM model, and extracting material volume;

s502, carrying out secondary development on a BlM model through a RevitAPI according to the volume information of each material, establishing a calculation formula, predicting the amount of demolished construction waste, and adopting the following calculation formula:

V _dw ＝(V _ow ×F _vol )-V _ros -V _ra -V _rot ；

W _dw ＝V _dw ×ρ；

wherein F is _vol For the volume change factor of the waste, V _dw For disposal of waste amount V _OW V as raw waste amount _ros For recycling waste amount in the same place, V _ra To recover waste amount V _rot For the amount of waste reused in other sites.

The beneficial effects of the invention are as follows: the invention is helpful for management personnel to reasonably plan the quantity and production scale of construction waste transferring and allocating plants, absorption plants and resource utilization plants by predicting the production quantity of the existing construction demolishd waste.

Drawings

Fig. 1 is a flow chart of a building waste estimation algorithm based on BIM according to the present invention.

Fig. 2 is a schematic view of the wave propagation direction of a pulsed laser.

Detailed Description

The invention will be further described with reference to the drawings and examples.

The conception, specific structure, and technical effects produced by the present invention will be clearly and completely described below with reference to the embodiments and the drawings to fully understand the objects, features, and effects of the present invention. It is apparent that the described embodiments are only some embodiments of the present invention, but not all embodiments, and that other embodiments obtained by those skilled in the art without inventive effort are within the scope of the present invention based on the embodiments of the present invention. In addition, all the coupling/connection relationships referred to in the patent are not direct connection of the single-finger members, but rather, it means that a better coupling structure can be formed by adding or subtracting coupling aids depending on the specific implementation. The technical features in the invention can be interactively combined on the premise of no contradiction and conflict.

The invention discloses a building waste estimation algorithm based on BIM, which estimates the production amount of existing building demolition waste without a design drawing by combining a three-dimensional laser scanning technology and a BIM technology.

The BIM technology has great potential value in building construction management, such as archive management for ancient buildings, building maintenance and related service information management, quality management, assessment and monitoring, energy and space management, emergency management and reconstruction planning. Because the BIM model can integrate a large amount of building element information, in building demolition, timely updated information data provided in the BIM technology can not only reduce unnecessary errors for owners, but also reduce financial risks of the owners, bring huge benefits for the owners, for example, in the aspects of building demolition planning, demolition cost calculation, building rubble management, optimizing building demolition planning, data management and the like, the related application of the BIM technology can be carried out, and huge benefits are brought.

The visualization and information integration functions in the BIM technology provide very favorable conditions for building demolishing, and in the process of building demolishing, the integrated building component information in the BIM technology can be utilized to extract the discharge amount and the discharge types of the building wastes, and in addition, the construction demolishing plan, the calculation of the treatment cost of the building wastes and the like can be performed on the basis of the BIM technology. Because the building type with the largest demolition amount is reinforced concrete structure building in the recent 30 years of development of the Chinese building industry, in the invention, the advantages of BIM technology are emphasized and utilized, the production amount of the demolition waste of the existing building is predicted by a reverse modeling technology, and the production amount of the demolition waste of the existing building is predicted by a three-dimensional laser scanning technology, so that management staff is facilitated to reasonably plan the quantity and production scale of building waste transferring and allocating plants, absorption plants and recycling plants; the building is selectively dismantled, so that the difficulty of waste recycling is reduced; the utilization rate of the building waste is improved, the reduction, reclamation and innocuity of the building waste are facilitated, and the building waste is converted into a usable building material in a comprehensive utilization mode.

The three-dimensional laser scanning technology solves the problem that single-point measurement data of a traditional measurement method are not comprehensive enough, and the technology has a wide application range by using an efficient and rapid technology. Meanwhile, the three-dimensional laser scanning technology is also a product of the mutual fusion development of a plurality of high and new technologies such as a computer technology, a laser measurement technology, a three-dimensional modeling technology and the like, and the three-dimensional laser scanning technology is technically characterized in that the three-dimensional laser scanning technology can be roughly summarized as follows, the advantages of large-area, high-efficiency and comprehensive acquisition of three-dimensional coordinates of the surface of a measured object can be realized, the defects of low acquisition efficiency and high labor intensity of a traditional measurement mode of data of a historical building and BIM technology guiding construction site are overcome, all three-dimensional coordinate data of the historical building and site data of BIM technology guiding construction can be quickly and comprehensively obtained, the time and the danger of field data acquisition are greatly reduced, main work is carried indoors, and building information required by the acquired data of the historical building can be generated, and a required building related achievement and a plurality of traditional tests: yield which cannot be achieved by the method; and the construction site data can be compared with the BIM design model to obtain a comprehensive quality detection report, so that the overall condition of construction quality can be conveniently grasped, and corresponding processing can be conveniently carried out on the reported condition.

Referring to fig. 1, a flow chart of a building waste estimation algorithm based on BIM provided by the present invention is shown, in this embodiment, the method includes steps S10-S50, specifically, the following steps are included:

in step S10, the spatial geometric information includes external information of the building and high-altitude data; the external data of the building comprises three-axis coordinates, reflectivity and color of each point on the surface of the object;

in the embodiment, space geometric information is obtained through three-dimensional laser scanning, different three-dimensional laser scanners are selected in a targeted manner for different parts, standing type laser radar is mainly used for scanning outdoors to obtain high data, standing type and hand-held type are combined for indoor scanning, a visual angle blind area is deep, a comprehensive and complete building is restored, and after the three-dimensional laser scanners are selected, scanning sites and control points are arranged; and setting scanning parameters of the three-dimensional laser scanner.

S20, preprocessing point cloud data;

the step S20 includes the steps of:

in this embodiment, the data stitching is classified into point cloud registration with and without target control according to nearest neighbor iterative registration algorithm (icp algorithm) data registration. The target registration is that a common point is obtained through the target, at least 3 common target points are arranged between two adjacent measuring stations, and the common target points are used as control points for conversion parameter calculation, so that automatic splicing is realized; and when the number of the targets is less than 3 or no targets are adopted, the method is implemented by selecting homonymous points with obvious characteristics and adopting a method of coarse registration and then fine registration. The coarse registration is to calculate initial point cloud transformation parameters through corresponding homonymous features; the fine registration is to improve the precision on the basis of the coarse registration and obtain the optimal transformation parameters.

Noise reduction processing is carried out on background points and mixed pixels in the point cloud data by using a self-filtering algorithm, and noise points in the point cloud data are removed; the noise points comprise drift points, isolated points, redundant points and mixed points;

In addition, for point cloud data containing holes, filling the point cloud data with the gaps by using a point cloud filling function;

the step S30 includes the steps of:

in this embodiment, the ultrasonic signal includes a plurality of ultrasonic signals of different frequencies;

when the ultrasonic signal includes a plurality of ultrasonic signals of different frequencies, the step S302 includes: receiving a plurality of reflected signals generated by reflecting a plurality of ultrasonic signals by a target object;

specifically, the received sound wave frequencies of a plurality of reflected signals are obtained; calculating reflectivity according to the received sound wave frequency and the sound wave frequency of the emission of the ultrasonic signals; in this embodiment, the process of measuring the depth and thickness of the reinforcing steel bar transmits ultrasonic waves through an ultrasonic wave transmitting device in the reinforced concrete structure. The ultrasonic receiver detects reflected waves from the steel bars inside the concrete structure.

In addition, in this embodiment, after the step S303, the following steps are further included:

In the above embodiment, the preset material database includes a plurality of common materials, acoustic impedance corresponding to the common materials, delay time parameters, and preset material attribute information, where the preset material attribute information includes injection name, density, hardness, melting point, ductility, thermal conductivity, and electrical conductivity of the materials.

S40, fusing the material information and the space geometric information of the building, and performing reverse modeling to generate a BIM model;

in this embodiment, the step S40 includes the following:

S50, estimating the waste yield of the building according to the BIM model;

in this embodiment, the step S50 is:

and setting the function parameter types of the RevitAPI, automatically acquiring the volume information of each material in the BIM model, and establishing a calculation formula, thereby realizing automatic estimation of the construction waste.

More specifically, in this embodiment, the step S50 includes:

s502, carrying out secondary development on the BIM model through a RevitAPI according to the volume information of each material, establishing a calculation formula, predicting the amount of demolished construction waste, and adopting the following calculation formula:

V _dw ＝(V _ow ×F _vo1 )-V _ros -V _ra -V _rot

W _dw ＝V _dw ×ρ；

With continued reference to fig. 1, the present invention provides a building waste estimation algorithm based on BIM, and further provides a specific embodiment, including steps S10 to S50, which are specifically as follows:

s10, acquiring point cloud data of a building through a three-dimensional laser scanner;

in this embodiment, the three-dimensional laser scanning technique is also called as a live-action copy technique, and by using a high-speed laser scanning measurement method, information such as (x, y, z) coordinates, reflectivity, color, etc. of each point on the surface of the object is rapidly acquired in a large area and high resolution, and 1 can be rapidly reconstructed from the large amount of dense point information: 1, providing accurate basis for subsequent internal processing, data analysis and other works. The method has the characteristics of rapidness, high benefit, non-contact, penetrability, dynamics, initiative, high density, high precision, digitization, automation, strong real-time performance and the like, can well solve the problem that the prior spatial information technology develops the neck bottle with real-time performance and accuracy, and is a brand new technical means for rapidly establishing a three-dimensional image model of an object.

In this embodiment, the main structure of the standing three-dimensional laser scanner is a range finder capable of continuously emitting laser at high speed and receiving reflected laser, and is matched with a group of reflection prisms capable of uniformly rotating by 360 degrees. The laser range finder continuously emits laser at a set frequency and receives the laser reflected back from the object surface, thereby completing ranging. Because each measuring point is not necessarily on the same level with the laser range finder, the distance obtained by measurement is the linear distance between two points, and the connecting line between the two points forms an included angle with the horizontal direction and the vertical direction respectively, namely a horizontal direction angle and a vertical direction angle. The scanner records the horizontal direction angle and the vertical direction angle of each measuring point, and the coordinate data of the scanning point relative to the measuring point can be calculated through the trigonometric function relation. The actual coordinates of the measuring station can be measured by using RTK and other instruments, so that the space coordinates of each scanning point can be back calculated. The laser scanner emits vertical scanner laser rays uninterruptedly, and the motor is used for 360-degree rotation in the horizontal direction, so that the scanning activity of the whole site is completed, and the point cloud data set is acquired.

An airborne laser radar (LiDAR, light Detection and Ranging) is a novel technology for rapidly acquiring high-precision three-dimensional information of the ground and the ground objects. The method is a detection technology integrating a laser technology, a high-dynamic carrier attitude measurement technology and a high-precision dynamic GPS differential positioning technology, and can acquire finer and more accurate topographic information compared with the traditional photogrammetry method.

S20, preprocessing point cloud data;

the original data is preprocessed through the random software Z+F Laser Control of the scanner, and the Laser Control can rapidly process massive point cloud data to form complete three-dimensional point cloud data of a main teaching building. The Laser Control preprocessing software is divided into 5 big modules, namely Scanning, preprocessing, register, color and Postprocessing modules. The Scanning module is mainly used for controlling the scanner to work by using the software, the Preprocessing module is a Preprocessing module, and the mask tool of the software is mainly used for point cloud filtering. The Register module is a splicing module, provides multiple splicing modes, and comprises target paper based splicing, target ball based splicing, characteristic point based splicing, mixed splicing and surface splicing, wherein the Color module is mainly used for coloring point cloud to generate true Color point cloud, and the function of the Postprocessing module mainly comprises a measuring function, a slicing function, an orthographic image generation function and a point cloud roaming video production function. The engineering file is opened in Z+FLaserControl preprocessing software, mass point clouds can be supported, the scanning file can be completely loaded, and the size of the point cloud points can be adjusted.

The point cloud data preprocessing process comprises steps S201-S205:

s201, denoising the point cloud, filtering the point cloud by selecting a required filter, selecting one or more stations of data to filter in the module, and waiting for a period of time, wherein the filter does not delete the point cloud but shields the noise point, and if the processing result is unsatisfactory, the filter can be deleted, so that the point cloud can be restored.

S202, point cloud splicing is carried out, a target ball splicing mode is selected in a splicing module, firstly, the size and the type of the target ball are set, the situation that the size and the type of the target ball are different from those of the target ball used for field scanning is avoided, the target ball is found in a view, the target ball is selected, at the moment, a ball is fitted according to the point cloud of the target ball, whether the fitted ball accords with the point cloud of the target ball or not is checked, if so, all the target balls in the station are named according to the method, and attention is paid to the fact that the target balls in the same position in different stations are named. And the method is based on target ball splicing, all scanning data are unified to the same coordinate system, the splicing precision is better than 2mm, the point cloud data coordinate system conversion is realized by using control points distributed on site, and the conversion precision is better than 5mm.

S203, when scanning, a camera built in the scanner is adopted to take photos in the same step, and the scanner can take photos in all directions at each site, so that the point cloud is colored in the Color module, the data of all sites are selected, after the coloring is finished, the photos of each site are seen to be synthesized into a Color panoramic photo, the Color information of the panoramic image is mapped onto the point cloud at the position according to the actual position through algorithm calculation, at the moment, the gray white point cloud can be seen to be changed into the true Color point cloud, and each point in the point cloud has Color information.

S204, after coloring is completed, the whole point cloud of the building can be displayed in the point cloud window, wherein the point cloud can be thinned and displayed in consideration of the configuration performance of the computer, and the point cloud can be displayed in different colors, so that the data of different sites can be distinguished.

S205, data export: selecting the target point cloud, the point cloud data can be exported in various general formats including zfs, asc, ias, pts and the like, and the coordinates, color information and reflection intensity of the point cloud can be exported.

S30, collecting main material information of a building by ultrasonic waves;

the laser ultrasonic nondestructive testing technology is a method for realizing material detection by utilizing the action of pulse laser and a test piece to generate ultrasonic waves and detecting the propagation characteristics of the ultrasonic waves in the test piece and a signal processing technology. The gist of this process includes excitation and detection of the ultrasound signal. Wherein, different ultrasonic excitation mechanisms are generated according to the relation between the energy density of the pulse laser and the thermal damage threshold of the material surface; the thermal excitation of the laser pulse is a broadband excitation signal, and the generated ultrasonic waves have different types and frequency bands. Correspondingly, the pulse laser generator and the ultrasonic detection equipment for realizing the two processes in the test are key components of a laser ultrasonic detection system, and the combination of the detection equipment is flexibly matched according to the local conditions by combining the test environment and the specific condition of a test piece.

Under the action of the pulsed laser thermal excitation, the particles inside the material deviate from the equilibrium position, and during the return to the equilibrium position, an oscillating movement is generated, which in turn causes vibrations and strains of surrounding particles, in such a way that ultrasound propagates in the material. The fluctuations can be divided into: longitudinal waves, transverse waves, surface waves and Lamb waves in thin plates, as shown in fig. 2.

In this embodiment, step S30 includes steps S301-S305:

s301, receiving a material identification instruction, and transmitting an ultrasonic signal according to the material identification instruction.

The electronic device controls the ultrasonic sensor to emit an ultrasonic signal according to the material identification instruction after receiving the material identification instruction, and in an embodiment, the ultrasonic sensor may include an ultrasonic signal emitter and an ultrasonic signal receiver, and the electronic device controls the signal emitter to emit the ultrasonic signal after receiving the material identification instruction.

S302, receiving a reflection signal generated by reflecting the ultrasonic signal by the target object, and calculating the reflectivity according to the ultrasonic signal and the reflection signal.

After the ultrasonic sensor of the electronic device transmits an ultrasonic signal, the ultrasonic signal is reflected by the obstacle and generates a reflected signal when contacting the obstacle, and the obstacle is a target object needing to identify the material. The electronic device then calculates the reflectivity from the emission point initial ultrasonic signal and the received reflected signal.

In an embodiment, the reflectivity is a proportion of the radiation energy of the reflected ultrasonic signal to the radiation energy of the initial ultrasonic signal, and may be expressed as a percentage. For example, the signal intensity of the initial ultrasonic signal and the signal intensity of the reflected signal may be acquired to calculate the reflectivity. The signal intensity may be represented by an amplitude, the amplitude may reflect the energy, and when the ultrasonic wave is reflected by different materials, the energy absorbed by the materials is different, and the more the ultrasonic wave is absorbed, the less the energy of the reflected signal is. The reflectivity can be calculated by acquiring the amplitude of the initial ultrasonic signal and the amplitude of the reflected signal.

S303, acquiring acoustic impedance of the display screen material.

In this embodiment, since the display screen includes an upper glass substrate, an ultrasonic sensor control circuit layer, a negative electrode layer, a light emitting layer, a piezoelectric material layer, a positive electrode layer, a fixing layer, and a lower glass substrate, it is necessary to obtain acoustic impedances of the upper substrate glass, specifically, materials of the upper substrate glass may be determined first, and then acoustic impedances corresponding to the upper substrate glass may be searched in a preset material database set in advance. The material of the upper substrate glass may be one of tempered glass, quartz glass, optical glass, and the like, which will not be described herein.

The acoustic impedance of the target object is calculated from the reflectivity and the acoustic impedance of the display screen material. After obtaining the reflectivity and acoustic impedance of the display screen material, the acoustic impedance of the target object may be calculated according to the following formula:

wherein eta is reflectivity, Z1 is acoustic impedance of the display screen material, and Z2 is acoustic impedance of the target object; the above formula is obtained after deformation:

and obtaining the acoustic impedance of the target object after calculation.

S304, calculating the delay time of the reflected signal according to the ultrasonic signal.

In practical use, indexes such as reflected signal intensity, reflected time delay and the like of the ultrasonic waves of different materials may be different, so that errors may exist if material information is searched only according to acoustic impedance. Therefore, before searching the material information in the preset material database, the delay time of the reflected signal can be further acquired. In an embodiment, the delay time may be a time interval between transmitting the ultrasonic signal and receiving the reflected signal, specifically, the timer may be controlled to start timing when the ultrasonic signal is transmitted, and stop timing after the reflected signal is received, where the time recorded on the timer is the delay time of the reflected signal.

S305, searching material information matched with the acoustic impedance and the delay time of the target object in a preset material database.

After the acoustic impedance and the delay time of the target object are obtained, a preset material matched with the acoustic impedance and the delay time can be searched in a preset material database, and material information of the preset material is obtained, wherein the material information can comprise material properties. Therefore, in this embodiment, a preset material database may be preset, where the preset material database includes a plurality of common materials and acoustic impedance and delay time parameters corresponding to the materials, and further, the preset material database may further include preset material attribute information, where the attribute information may include injection name, density, hardness, melting point, ductility, thermal conductivity, electrical conductivity, and so on, so that a user may know the material information of the target object in detail.

As can be seen from the foregoing, in this embodiment, the material identification instruction may be received, the ultrasonic signal may be transmitted according to the material identification instruction, the reflection signal generated by the reflection of the ultrasonic signal by the target object may be received, the acoustic impedance of the display screen material may be obtained by calculating the reflectivity according to the ultrasonic signal and the reflection signal, the acoustic impedance of the target object may be calculated according to the reflectivity and the acoustic impedance of the display screen material, and the delay time of the reflection signal may be calculated according to the ultrasonic signal.

S40, reverse modeling is performed to generate a BIM model.

The reverse modeling technology can be simply understood as that under the use of a related surveying instrument, data acquisition is carried out on three-dimensional information of an actual object or a solid model, acquired three-dimensional point cloud data are processed and imported into related professional modeling software, reverse reconstruction is carried out on three-dimensional characteristics of an information acquisition object entity according to the processed point cloud data, and a series of application expansion processes are carried out on the basis of the model.

The data processed in the Couldcompare software needs to be subjected to data conversion format, so that the data format which can be identified and processed by the BIM mainstream modeling software Revit is obtained. The method adopted by the invention is that the formats of the point cloud data processed by the software, such as 'zfs', 'asc', 'las', 'pts', and the like, pass through the functional module of the link point cloud and are converted into the 'rcs' format file. The format file is directly linked and opened in Autodesk Revit software, and is used as a reference for reverse modeling in the software.

In this embodiment, step S40 includes steps S401 to S403, which are as follows:

s401, drawing a structural contour line. And opening the transcoded point cloud data in the Revit software, and formally starting the reverse modeling. First, a contour line of a model is drawn from a point cloud. The drawn contour line mainly has the effects of determining the boundary of a model and aligning and placing the created component (such as a wall body and the like) with reference; and drawing the outline of the component according to the point cloud, and collecting the size information of the component by means of functions such as measurement, marking and the like, and taking the size information as a size reference of the loaded door, window and other components. Views in the project browser can be selected or generated, two-dimensional plane, elevation view or split frame are adjusted to perform 'slicing' processing on the point cloud, and contour lines of the model are drawn.

S402, model elevation and axial network creation. The creation of the model main body is not limited by constraint conditions, the constraint conditions are used as modeling references in the model construction process, information such as size, position and the like is provided for model graphic elements, and meanwhile important acting elevations in the aspects of model dividing functions, areas and the like are used as the first step of establishing the constraint conditions in the modeling process, so that the functions of dividing floors, generating floor plan views, determining the positions of floor top plates and bottom plates, determining the positions of the top surfaces and the bottom surfaces of columns and walls and the like are achieved.

The point cloud data used in the modeling can be used in software by using a Window Select Points function, a proper step value is selected according to actual conditions and collected drawing data, a computer can analyze according to the point cloud data in a frame selection range, and the distribution condition of the point cloud in the range in the vertical height direction is obtained, so that elevation is automatically generated. The generated elevation is floor elevation, and the elevation data is required to be processed according to actual conditions to obtain the structural elevation of each floor during modeling.

S403, creating a model main body. And the creation work of the model main body can be formally developed after the drawing of the two constraint conditions of elevation and axis network prepared for the early modeling stage is completed.

The invention mainly relates to a main body structure comprising a wall body, a column, a beam and a floor slab, wherein the creation of a wall body door, a window and a stair is obtained by a reverse modeling method. Taking reverse working of the outer vertical wall as an example, the wall model creation needs to be based on point cloud, the function of 'creating the wall' is selected from actual conditions, the suitable wall family type is selected, the wall components can be edited to meet actual requirements, and a dialog box for wall creation and a simulated three-dimensional wall model are shown as the figure. The 'Rect' function can be selected when the fitting work of the point cloud is carried out, and proper project views and view angles are selected, so that the point cloud of the wall surface can be selected as much as possible when the same wall is created. After the selection is completed, fitting parameters such as shape tolerance values, critical point tolerance values and the like are required to be set and adjusted, and a wall model is automatically generated in software.

A complete building information model often contains a large number of accessory components represented by doors, windows, stairs and the like, and the parameters caused by different use degrees among the components of the same family are inconsistent. If modeling is based on a single component point cloud, not only is the workload heavy and repeated, but also a practical and feasible method is lacking technically.

The modeling work related to the accessory component model adopts the method that the information such as images and physical properties (such as shape and material and the like) of each component are acquired on site. Contour lines are drawn from the point cloud data and their outer dimensions are determined. When the model is created, the similar family type files in the existing Revit software family library can be edited and adjusted to create a member family file which meets the actual conditions of the building as far as possible, and then the file is imported into the Revit and adjusted to the corresponding position according to the point cloud and the contour line.

After the model creation work is completed, the whole model creation work is basically completed, and the physical properties, structural forms and position sizes necessary for the structures and the components of each component in the model are preliminarily provided. However, in order to make the model more realistic, the original appearance of the modeling object can be restored more accurately, and the parameters of the internal structure, the structural materials and the like of structural members such as walls, beams, columns, floors and the like are enriched; further improvements are needed in the physical properties of the accessory components, such as the color, texture, and decoration of the door and window. The information for modification and perfection in the editable type parameters is very rich, and the information can be further perfected according to actual conditions after the corresponding families are selected.

S50, estimating the generation amount of the building waste. And automatically acquiring the volume information of each material in the BIM according to the secondary development of the RevitAPI. The step S50 includes:

s501, setting function parameter types through a Revit API, automatically acquiring material information of all components from a BIM model, and extracting material volume;

s502, carrying out secondary development on the BIM model through a Revit API according to the volume information of each material, establishing a calculation formula, predicting the amount of demolished construction waste, and adopting the following calculation formula:

V _dw ＝(V _ow ×F _vo1 )-V _ros -V _ra -V _rot ；

W _dw ＝V _dw ×ρ；

wherein F is _vol For the volume change factor of the waste, V _dw For disposal of waste amount, V _OW V as raw waste amount _ros For recycling waste amount in the same place, V _ra To recover waste amount V _rot For the amount of waste reused in other sites.

In summary, the invention discloses an automatic estimation algorithm for the production amount of the existing building demolition waste without construction drawing and BIM model, which predicts the production amount of the existing building demolition waste by a three-dimensional laser scanning technology, and is helpful for management personnel to reasonably plan the quantity and production scale of building waste transfer and allocation plants, absorption plants and resource utilization plants; the building is selectively dismantled, so that the difficulty of waste recycling is reduced; the utilization rate of the building waste is improved, the reduction, reclamation and innocuity of the building waste are facilitated, and the building waste is converted into a usable building material in a comprehensive utilization mode.

While the preferred embodiment of the present invention has been described in detail, the present invention is not limited to the embodiments, and those skilled in the art can make various equivalent modifications or substitutions without departing from the spirit of the present invention, and these equivalent modifications or substitutions are included in the scope of the present invention as defined in the appended claims.

Claims

1. A building waste estimation algorithm based on BIM, the algorithm comprising the steps of:

s20, preprocessing point cloud data;

s50, estimating the waste yield of the building according to the BIM model.

2. The building waste estimation algorithm according to claim 1, wherein in step S10, the spatial geometrical information includes external information of the building and elevation data.

3. The building waste estimation algorithm according to claim 1, wherein the step S20 comprises the steps of:

4. A building waste estimation algorithm according to claim 3, wherein for point cloud data containing holes, point cloud filling functions are used to fill in the empty point cloud data;

5. The building waste estimation algorithm according to claim 1, wherein the step S30 comprises the steps of:

6. The building waste estimation algorithm according to claim 5, further comprising the following steps after the step S303:

7. The building waste estimation algorithm according to claim 5, wherein the ultrasonic signal in the step S301 includes a plurality of ultrasonic signals of different frequencies;

acquiring ultrasonic signal intensities of a plurality of reflected signals;

the reflectivity is calculated from the received acoustic frequency and the emitted acoustic frequency.

8. The building waste estimation algorithm according to claim 5, wherein the pre-set material database includes a plurality of common materials, acoustic resistances corresponding to the common materials, delay time parameters, and pre-set material attribute information including injection names, density, hardness, melting point, ductility, thermal conductivity, and electrical conductivity of the materials.

9. The building waste estimation algorithm according to claim 1, wherein the step S40 includes the following:

10. The building waste estimation algorithm according to claim 9, wherein the step S50 is:

the function parameter type of the Revit API is used for setting, the volume information of each material in the BIM model is automatically obtained, a calculation formula is built, and further automatic estimation of construction waste is realized;

step S50 includes:

V _dw ＝(V _ow ×F _vol )-V _ros -V _ra -V _rot ；

W _dw ＝V _dw ×ρ；