CN113408927B - Big data-based prestressed construction quality evaluation method and system - Google Patents
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Abstract
The invention provides a method and a system for evaluating prestress construction quality based on big data, wherein the method is used for extracting design parameters to construct a lightweight digital model; carrying out trial production of the prefabricated prestressed component, carrying out on-site calculation verification on a single trial production result, and optimizing the performance and the technological parameters of materials and machines to reach the quality acceptance standard; producing a prestressed component, collecting construction process data in real time, extracting and/or calculating a quality evaluation index value according to the construction process data, matching the quality evaluation index value with a lightweight digital model, and quickly evaluating warehouse entry samples in a basic standardized sample warehouse; sampling is carried out according to a predetermined sampling rule, on-site measurement and post-construction detection are carried out on the samples marked in tracking monitoring, and verification is carried out by comparing construction process data, measurement data and post-construction detection data to finish quality evaluation. The invention has the promotion effect on the design and construction of the prestress engineering and the development and improvement of the quality acceptance technical level.
Description
Technical Field
The invention relates to the field of prestress, in particular to a prestress construction quality evaluation method and system based on big data.
Background
The prestress technology is widely applied to the construction of basic facilities such as geotechnical engineering, bridge engineering and the like due to the unique technical and economic advantages. In recent years, a great number of diseases and accidents in the prestressed engineering cause huge economic losses and serious social influences, but the special characteristics of the concealed engineering make scientific and effective evaluation difficult in construction quality control. At present, in the industry, along with the requirement upgrading of construction quality engineering, the control of prestress construction quality is emphasized by mechanisms at all levels, and control measures with different degrees are adopted for links from design, construction to quality acceptance and the like in the engineering construction process. Particularly, in the aspect of construction quality control, some evaluation methods and standards are gradually formed under the organizations of industry governing departments and governing departments in various places, and the implementation of the methods and the standards provides specific guidance suggestions for relevant units in work on one hand and also provides higher technical requirements for prestress construction quality evaluation on the other hand.
At present, in the aspect of prestress construction quality evaluation, the prestress construction quality evaluation is mainly performed domestically in a sampling detection mode after construction is finished. In the existing technical scheme, specifically, in the construction process of a prestressed engineering component, according to the sequence of process links, after a specific process is completed, quality testing personnel carry out data acquisition on site according to a predetermined sampling rule, then professional technicians carry out data analysis and processing, a detection result is formed according to a control standard, and finally whether the construction quality of the process meets the standard requirements or not is evaluated according to the detection result. If the process does not reach the standard, the reason which does not meet the standard needs to be analyzed and verified, a quality rectification scheme and specific measures are determined and fed back to construction site personnel, the construction personnel need to rectify and then recheck according to technical requirements, and after recheck reaches the standard, the next process link can be carried out again, and the like.
As a general quality control method in the field of engineering construction, sampling detection solves the construction quality problems in some specific ranges to a certain extent, but in the implementation process of some prestressed projects, due to the influence of specific working conditions and environments and the special professional specificity of the prestressed projects, the technical defects and shortcomings of the quality control method based on post-sampling detection are gradually highlighted, and specifically, the quality control method comprises the following points:
(1) Because the prestressed engineering components belong to solid engineering structures, generally belong to large fixed structures of engineering sites, and are usually formed in one step in the construction process, the prestressed engineering components cannot be sent to a sample to be detected according to conventional products, and detection personnel must go to the sites of engineering construction sites to acquire data. Normally, a time window required for detection work needs to be reserved in advance, and for this reason, construction must be suspended in order to wait for detection, and there is a problem that the detection work interferes with and affects normal construction.
(2) The basic premise of sampling detection is random sampling according to a preset rule so as to evaluate the overall quality condition, and the core and the premise of the method are to ensure the randomness of samples. However, because the construction site has many influencing factors and various uncontrollable situations often occur, each detection actually needs to be fully prepared, so that the meaning of randomness is basically lost in the spot inspection, and the data result is difficult to represent.
(3) Generally, the significance of the detection work is mainly reflected in the aspect of value utilization of data results, and the detection work is used for evaluating whether the current construction quality meets the standard or not, and more importantly, dynamic regulation and guidance are required to be performed on the construction process. However, due to the particularity of the pre-stress engineering process, the data provided by the post-sampling simplified method is delayed in acquisition, analysis and processing of the data, and in addition, some problems are judged and determined by the experience of professionals, so that the construction timeliness requirement of dynamic feedback is difficult to meet, and the working value of the construction cannot be fully reflected.
(4) In the field sampling detection process, the technical level and the matching degree of field personnel and the technical performance of detection equipment are limited, the technical level of personnel needs to be accumulated over the years, the detection equipment is high in use and maintenance cost, and a part of projects are difficult to implement in such a way due to high cost.
Disclosure of Invention
In order to overcome the defects in the prior art, the invention aims to provide a prestressed construction quality evaluation method and system based on big data.
In order to achieve the above object, the present invention provides a prestressed construction quality assessment method based on big data, comprising the steps of:
extracting design parameters according to the engineering project design file to construct a lightweight digital model;
carrying out trial production of the prefabricated prestressed component, measuring material size and performance parameters in the trial production process, carrying out comparative analysis on parameters of construction machines and tools, carrying out on-site calculation verification on a single trial production result, optimizing the performance and technological parameters of the material and the machines to reach quality acceptance standards, and solidifying the optimized parameters into the lightweight digital model to form a basic standardized sample library;
the method comprises the following steps of producing a prestressed component, collecting construction process data in real time in the production construction process of the prestressed component, dynamically analyzing and processing the construction process data, extracting and/or calculating a quality evaluation index value according to the construction process data, matching the quality evaluation index value with a lightweight digital model, entering database model training, and quickly evaluating warehouse entry samples in a basic standardized sample library;
sampling according to a predetermined sampling rule, carrying out field measurement and post-construction detection on a sample marked in tracking monitoring, comparing construction process data, measurement data and post-construction detection data, verifying, completing detection on the sampled sample, and realizing evaluation on the prestress construction quality.
And after the detection of the sampling samples is finished, the verification result is dynamically supplemented to the sample database for training and learning, a construction quality evaluation system which accords with the objective and actual conditions of a single work point is formed, and the dynamic evaluation of the construction quality is realized.
The method adopts a lightweight digital modeling technology, extracts design file information in a targeted manner, can be called quickly and accessed in time, can meet the construction quality control requirement, and can ensure higher working efficiency; and curing the process standard parameters, and providing objective and credible construction parameters and inspection standards, so that the process parameter execution standards in the construction process can be relied on. The construction data is collected and transmitted in real time through the on-site monitoring terminal, an objective and dynamic engineering entity big data sample library is formed, the working procedure construction quality is judged quickly, and normal on-site construction is not interfered; the tracking and monitoring data adopts an intelligent inspection technology so as to determine the sampling object, thereby ensuring the randomness of sampling and improving the representativeness of the data; the sampling inspection verifies the sample circulation regeneration technology, and forms a quality control standard which has pertinence and can be dynamically adjusted.
According to the optimized scheme of the prestress construction quality evaluation method, when a lightweight digital model is constructed, according to investigation, design and construction file materials of specific projects, project names, project profiles, project quantities, planned construction periods, project types, unit projects, subsection projects, project names, engineering materials, construction sequences, processes, independent component types, stress-strain theoretical values and technical parameters required by process acceptance control are extracted to serve as parameters for constructing the lightweight digital model.
According to the optimized scheme of the prestress construction quality evaluation method, after a lightweight digital model is constructed, test basic data are collected, a test scheme is formulated, a basic parameter test is developed under the same working condition, test result parameters and design parameters are checked, the design parameters are corrected through the test result parameters, process parameters and quality evaluation standards which are in line with the actual construction conditions are formulated, and the test production of prefabricated parts is developed according to the parameters determined by the basic parameter test. Test result parameters are obtained by relying on a basic test, design parameters are corrected according to the test result parameters, the influence of errors is reduced, an evaluation standard meeting the reality is formed, and the accuracy of the method is improved.
According to the preferable scheme of the prestress construction quality evaluation method, in the test production process, the size, the shape and the performance parameters of a material are measured, the material comprises a prestress steel material, a matched anchorage device, a clamp and a connector, a prestress pipeline and pore grouting slurry, the performance parameters comprise mechanical performance parameters and slurry performance parameters, the performance parameters and the operation parameters of a construction machine are contrastively analyzed, the construction machine comprises a tensioning device, a grouting device and a vacuum pump, and then the field calculation verification is carried out on the single test production result.
The invention also provides a prestress construction quality evaluation system based on the prestress construction quality evaluation method, which comprises a digital component module, a basic test module, a big data platform and a process monitoring module;
the digital component module is used for carrying out digital modeling on the engineering component, setting model classification and presetting theoretical parameters according to the specific conditions and requirements of project implementation by extracting information in a paper design file of the engineering component, and forming a lightweight digital model of the project;
the basic experiment module comprises equipment, a device and supporting software for developing basic parameter tests, and consists of various stress and strain sensors, a data acquisition instrument and data analysis processing software; before formal construction, according to requirements of site construction equipment, materials and process characteristics, a modularized combination mode is adopted to complete verification of process parameters and acquisition of standard parameters according to a test organization scheme and a program;
the process monitoring module is used for tracking the field construction process, actively uploading field construction data to the big data platform in real time by adopting the technology of Internet of things, and receiving remote access and data tracing requests of the big data platform;
the big data platform is used for uniformly managing the lightweight digital model, the basic standard sample library and the construction monitoring data, and brings construction plans, data acquisition, analysis processing and result evaluation into integrated dynamic management through a preset construction quality evaluation framework.
Based on big data technology, the invention integrates lightweight digital modeling, basic parameter test, construction data tracking monitoring and sampling detection verification to form a set of systematic prestress construction quality evaluation method, and establishes a corresponding evaluation system according to the method, thereby solving the problems of large construction interference, poor randomness, insufficient representativeness, poor timeliness and high cost in the traditional mode of sampling inspection for quality control. The completeness system provided by the invention has a promoting effect on the development and improvement of the technical level of the design, construction and quality acceptance links of the prestressed engineering in the engineering construction industry.
Additional aspects and advantages of the invention will be set forth in part in the description which follows and, in part, will be obvious from the description, or may be learned by practice of the invention.
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The above and/or additional aspects and advantages of the present invention will become apparent and readily appreciated from the following description of the embodiments, taken in conjunction with the accompanying drawings of which:
fig. 1 is a schematic block diagram of the present invention.
FIG. 2 is a block flow diagram of the system of the present invention.
Detailed Description
Reference will now be made in detail to embodiments of the present invention, examples of which are illustrated in the accompanying drawings, wherein like or similar reference numerals refer to the same or similar elements or elements having the same or similar function throughout. The embodiments described below with reference to the accompanying drawings are illustrative only for the purpose of explaining the present invention, and are not to be construed as limiting the present invention.
In the description of the present invention, unless otherwise specified and limited, it is to be noted that the terms "mounted," "connected," and "connected" are to be interpreted broadly, and may be, for example, a mechanical connection or an electrical connection, a communication between two elements, a direct connection, or an indirect connection via an intermediate medium, and specific meanings of the terms may be understood by those skilled in the art according to specific situations.
The invention provides a pre-stress construction quality evaluation method based on big data, which is used for evaluating pre-stress construction quality, realizing in-situ control of pre-stress construction quality and preventing quality problems in advance. Under the condition of not interfering and influencing normal construction, scientific random sampling detection and dynamic and timely data result processing are realized; the method can realize objective evaluation of data results, can exert the effect of embodying detection work on construction quality control, embodies the effective value of detection data, reduces the construction quality control cost, and converts the traditional prestress construction quality evaluation mode from experience drive into data drive.
The evaluation method specifically comprises the following steps:
and extracting design parameters according to the engineering project design file to construct a lightweight digital model.
Specifically, because the amount of information contained in the design file is very large, for the construction quality evaluation of the prestress engineering, all information is not necessarily included in modeling, and therefore a lightweight digital model is adopted in the method. The lightweight digital model comprises project names, project profiles, project quantities, planning construction periods, project types, unit projects, subsection projects, project division names, project materials, construction sequences, processes, independent component types, stress-strain theoretical values and technical parameters required by process acceptance control according to investigation, design and construction file materials of engineering construction projects, and the lightweight digital model is constructed after the technical parameters are extracted.
Because the design parameters extracted in the digital modeling process are generally theoretical values, and part of parameter design is limited by specific material attributes and process levels, a series of basic parameter tests need to be developed before formal construction. Therefore, after the lightweight digital model is constructed, basic data of the test are collected, including but not limited to basic data of relevant reconnaissance design files, construction schemes, test operation conditions and the like, the test scheme is formulated, basic parameter tests are developed under the same working condition, test result parameters and design parameters are checked, the design parameters are corrected through the test result parameters, for example, the design parameters are replaced by the test result parameters, process parameters and quality evaluation standards conforming to actual construction conditions are formulated, and the checking method, the correcting method and the method for formulating the process parameters and the quality evaluation standards conforming to the actual construction conditions all adopt the existing methods and are carried out according to specific methods given by relevant engineering construction specifications.
And (4) carrying out trial production of the prefabricated prestressed component according to the parameters determined by the basic parameter test. In the pilot production process, the size appearance and performance parameters of materials are measured, wherein the materials comprise but are not limited to prestressed steel, matched anchors, clamps and connectors, prestressed pipelines and pore grouting slurry, and the size appearance comprises but is not limited to length, diameter and area; the performance parameters comprise mechanical performance parameters and slurry performance parameters, the mechanical performance parameters comprise but are not limited to elastic modulus, tensile strength, force transmission performance and anchoring performance, the slurry performance parameters comprise but are not limited to setting time, fluidity, bleeding rate, free expansion rate, compressive strength and flexural strength, the performance parameters and the operation parameters of construction machines are contrastively analyzed, the performance parameters and the operation parameters of the construction machines comprise but are not limited to tensioning equipment, grouting equipment and a vacuum pump, the performance parameters and the operation parameters of the construction machines comprise but are not limited to precision, loading speed, unloading speed, synchronous error, pressure stabilizing performance, stirring rotating speed and grouting pressure, then field calculation verification is carried out on a single test production result, the performance and the process parameters of optimized materials and machines meet quality acceptance standards, and the optimized parameters are solidified into the lightweight digital model to form a basic standardized sample library.
And then, prestressed component production is carried out, construction process data are collected in real time in the production construction process of the prestressed component, the construction process data are dynamically analyzed and processed, the dynamic analysis processing refers to analysis processing according to a specific external environment, the analysis processing is carried out by adopting the existing method, a quality evaluation index value is extracted and/or calculated according to the construction process data, the existing method is adopted for extraction and/or analysis, the quality evaluation index value is matched with a lightweight digital model, and the database model is trained. Meanwhile, the database entry samples are classified and marked to form a dynamic engineering big database.
And then sampling in the produced prestressed component according to a predetermined sampling rule, marking and tracking and monitoring the sampled sample, and specifically carrying out site measurement and post-construction detection on the sampled sample in the construction process. The post-construction detection refers to the detection related to the interior and exterior data related to the construction and a series of post-construction tests after the construction is finished, such as field recording, electronic data recording, effective prestress detection, grouting fullness detection and the like.
Because a clear logical relationship exists among the construction process data, the measurement data and the post-construction detection data, the construction process data, the measurement data and the post-construction detection data are compared for verification to check whether the relationship among the data is established or not, and the reasonable validity of the data is determined according to the relationship. And after the verification is finished, the detection of the sampling sample is finished, and the evaluation of the prestress construction quality is realized.
As the preferred scheme of the embodiment, the verification result can be dynamically supplemented into the sample database for training and learning, a construction quality evaluation system which accords with the objective and actual conditions of a single work point is formed, and dynamic evaluation of construction quality is realized.
As shown in fig. 1, the present application further provides a prestressed construction quality evaluation system, which includes a digital component module, a foundation test module, a big data platform, and a process monitoring module, and the prestressed construction quality evaluation system evaluates the prestressed construction quality according to the above prestressed construction quality evaluation method.
As shown in fig. 2, the digital component module is configured to perform digital modeling on the prestressed component, and set model classification and preset theoretical parameters according to specific conditions and requirements of project implementation by extracting information in a paper design file of the engineering component to form a lightweight digital model of the project for later flexible calling. The lightweight digital model at least comprises names of prestressed members, corresponding types of the prestressed members, prestressed materials, tensioning sequences, processes, forces and elongations.
The basic experiment module comprises equipment, a device and supporting software for carrying out basic parameter tests, and consists of various stress and strain sensors, a data acquisition instrument and data analysis and processing software, wherein the existing equipment, device and supporting software can be adopted for realizing. Before formal construction, according to requirements of site construction equipment, materials and process characteristics, verification of process parameters and acquisition of standard parameters are completed in a modular combination mode according to a test organization scheme and a program, tests can be specifically developed according to a test program specified by a standard, test data are collected, and reasonable process parameters and standard parameters are selected and determined through analysis and comparison of test data results.
The process monitoring module is used for tracking the field construction process, actively uploads field construction data to the big data platform in real time by adopting the technology of Internet of things, and can also receive remote access and data tracing requests of the big data platform.
The big data platform is used for uniformly managing the lightweight digital model, the basic standard sample library and the construction monitoring data, and brings construction plans, data acquisition, analysis processing and result evaluation into the dynamic management of integrity through a preset construction quality evaluation framework.
The process monitoring module in the embodiment is specifically realized by adopting the following structure:
and arranging an internet of things terminal on the construction machine of each prestressed member, wherein each internet of things terminal is in communication connection with the large data platform.
And a data acquisition module for acquiring the operation data of the construction machine, an edge calculation module for processing the data acquired by the data acquisition module, a data storage module and a communication module are integrated in each Internet of things terminal. The thing allies oneself with the terminal and passes through mechanical connecting device and install on construction equipment, adopts the modular design equipment mode, can match according to site operation machines pipeline structure fast to make the sensor in the thing allies oneself with the terminal can insert construction equipment steadily and reliably and acquire corresponding state and parameter, and adopt totally enclosed waterproof, dustproof protection casing to the abominable operating mode of job site.
The data acquisition module comprises a stress acquisition unit, a strain acquisition unit and a flow acquisition unit, wherein the stress acquisition unit, the strain acquisition unit and the flow acquisition unit are respectively connected with the edge calculation module, and each stress acquisition unit, each strain acquisition unit and each flow acquisition unit carry out real-time monitoring on signals of a sensor thereof and transmit monitored values to the edge calculation module after protocol coding so as to complete a data acquisition process.
The edge calculation module is used for field data processing and basic logic control and comprises a data processing unit and a logic control unit.
After receiving the data, the data processing unit decodes the received data codes, reversely locates data sources according to a coding protocol, repacks the data codes and sends the data codes to the logic control unit; when repackaging, pack the IO terminal's of code thing ID number, time data, electric quantity data, stress data, strain data and flow data in proper order.
The logic control unit performs logic operation in real time after receiving the data, and in this embodiment, only the stress data is used to determine whether the trigger threshold is reached, and if the trigger threshold is reached, the tracking module is triggered to track the data acquisition module corresponding to the data information. The method comprises the following specific steps: the logic control unit monitors whether stress data in the received data is larger than a trigger threshold value in real time, if yes, the received data is stored in a data storage module of the Internet of things terminal and sent to a big data platform, the sending state of the data is marked in the data storage module, if the sending is successful, the data is marked as sent, and if the sending is failed, the data is marked as unsent; the logic control unit judges whether unsent data exists in the data storage module in real time, and if unsent data exists in the data storage module, the unsent data is sent to the big data platform through the communication module; if not, no data is sent.
Therefore, in the present embodiment, the logic control unit has two roles:
data storage logic control: and screening the data to be stored, namely storing the data reaching the threshold triggering condition, wherein the function is to complete the local storage of the data on the Internet of things terminal.
Data transmission logic control: the data stored locally in the Internet of things terminal are transmitted, namely the local data are sent to the big data platform through the communication module, so that the uploading of the locally stored data of the Internet of things terminal is completed, and the logical control of real-time uploading and retransmission after failure is included.
The communication module is integrated in the internet of things terminal, is directly connected to the data acquisition module through the circuit board, and performs data and instruction interaction with the big data platform in a wireless communication mode. After the communication module receives the data, the module performs network configuration and actively performs handshake to the big data platform, the data is sent in a transparent transmission mode after connection is completed, the connection is disconnected after the data is sent out and returned by the big data platform, the received data is sent to the edge computing module, and the edge computing module marks the data which is sent successfully and stores the data in the data storage module; if the communication module fails to send data to the big data platform, the edge computing module marks the data which is not successfully sent and stores the data in the data storage module.
Each thing allies oneself with the inside power management module that still integrates of terminal, and power management module is used for whole system electric quantity management, and the power consumption is optimized, and safety control, power management module contain electric quantity monitoring unit, distribution management unit.
The electric quantity monitoring unit monitors the voltage in the main circuit according to an energy-saving management algorithm, usually once monitoring is carried out for 30 s-43200 s, and the collected voltage value is coded and then sent to the distribution management unit.
And the distribution management unit judges after receiving the acquired voltage data, if the voltage acquired last time is greater than a set value, if 3100, the distribution management unit enters a normal mode, all modules logically work normally, and when the voltage acquired last time is less than or equal to the set value, the distribution management unit enters an energy-saving mode, and the communication module does not supply power any more.
A tracking module is arranged in a big data platform, data information acquired by an internet of things terminal is acquired by adopting an automatic triggering and/or polling access mode, and a process is monitored for a construction event. When data information actively transmitted by the internet of things terminal reaches a trigger threshold value, automatically triggering a tracking function of a tracking module, and tracking the internet of things terminal corresponding to an ID number according to the ID number in the data information; through the ID number of the Internet of things terminal and the protocol coding problem link, the tracking module can remotely awaken the Internet of things terminal in a polling access mode to acquire real-time data and historical data.
And arranging a processing and publishing module in the big data platform for processing the monitoring data, reversely associating the information tracked by the tracking module with the construction operation project information through the event tracking result, counting the monitoring result according to preset parameter standards, wherein the associated content comprises personnel, machines, materials, processes and construction plans of construction operation.
Specifically, the processing and issuing module performs matching, analysis and judgment on the received data.
Matching: according to the ID number positioning data source of the Internet of things terminal, reversely associating construction machines, users, organizations and construction plans through protocol coding sequence numbers, and corresponding the data to components corresponding to the data in the construction plans and the specific positions and process links of the components. The matching algorithm can be matched by adopting the existing algorithm, such as a preset Bayesian model algorithm, a preset queuing model algorithm according to a construction plan, a preset condition model algorithm according to process parameters, an event model algorithm triggered according to the actions of users and machines and tools, and the like.
And (3) analysis: and performing parameter index calculation specified by relevant standard on the data. The parameter indexes specified by the relevant specification standards specifically comprise control stress, an elongation value, an anchoring retraction amount, actual effective prestress after anchoring, tension synchronism, load-holding time, initial fluidity and temperature of slurry, slurry pressing amount, and pressure stabilizing pressure and time.
And (3) judging: and judging whether the calculated parameter index is qualified or not according to a preset parameter standard.
In the description herein, references to the description of the term "one embodiment," "some embodiments," "an example," "a specific example," or "some examples," etc., mean that a particular feature, structure, material, or characteristic described in connection with the embodiment or example is included in at least one embodiment or example of the invention. In this specification, the schematic representations of the terms used above do not necessarily refer to the same embodiment or example. Furthermore, the particular features, structures, materials, or characteristics described may be combined in any suitable manner in any one or more embodiments or examples.
While embodiments of the present invention have been shown and described, it will be understood by those of ordinary skill in the art that: various changes, modifications, substitutions and alterations can be made to the embodiments without departing from the principles and spirit of the invention, the scope of which is defined by the claims and their equivalents.
Claims (5)
1. A prestressed construction quality evaluation method based on big data is characterized by comprising the following steps:
according to engineering project design files, extracting design parameters to construct a lightweight digital model, specifically: according to investigation, design and construction file materials of a specific project, extracting project names, project profiles, project quantities, project schedule, project types, unit projects, subsection projects, project-based project names, project materials, construction sequence, process, independent component types, stress-strain theoretical values and technical parameters required by process acceptance control as parameters for constructing a lightweight digital model;
carrying out trial production of the prefabricated prestressed component, measuring material size and performance parameters in the trial production process, carrying out comparative analysis on parameters of construction machines and tools, carrying out on-site calculation verification on a single trial production result, optimizing the performance and technological parameters of the material and the machines to reach quality acceptance standards, and solidifying the optimized parameters into the lightweight digital model to form a basic standardized sample library;
the method comprises the following steps of producing a prestressed component, collecting construction process data in real time in the production construction process of the prestressed component, dynamically analyzing and processing the construction process data, extracting and/or calculating a quality evaluation index value according to the construction process data, matching the quality evaluation index value with a lightweight digital model, entering database model training, rapidly evaluating warehouse entry samples in a basic standardized sample library, namely automatically calculating index parameters and an error range by a system according to inspection standards determined by related standard standards, judging whether the standard requirements are met or not, and grading the quality according to results;
sampling is carried out according to a predetermined sampling rule, on-site measurement and post-construction detection are carried out on the samples marked in tracking monitoring, and verification is carried out by comparing construction process data, measurement data and post-construction detection data, so that the detection of the sampled samples is completed, and the quality evaluation of the prestress construction is realized.
2. The big-data-based prestressed construction quality assessment method according to claim 1, characterized in that after the detection of the sampled samples is completed, the verification result is dynamically supplemented to the sample database for training and learning, so as to form a construction quality assessment system conforming to the objective practice of a single work point, and realize the dynamic assessment of construction quality.
3. The method for evaluating the quality of the prestressed construction based on the big data according to claim 1, wherein after the lightweight digital model is constructed, basic data of the test is collected, a test scheme is formulated, a basic parameter test is performed under the same working condition, test result parameters and design parameters are checked, the design parameters are corrected according to the test result parameters, process parameters and quality evaluation standards which are in line with actual construction conditions are formulated, and the test production of the prefabricated part is performed according to the parameters determined by the basic parameter test.
4. The method for evaluating the quality of prestressed construction based on big data according to claim 1, wherein in the trial production process, the dimensional shape and performance parameters of the material are measured, the material comprises prestressed steel, matched anchorage devices, clamps and connectors, prestressed pipelines and pore grouting slurry, the performance parameters comprise mechanical performance parameters and slurry performance parameters, the performance parameters and the operation parameters of the construction machine are contrastively analyzed, the construction machine comprises tension equipment, grouting equipment and a vacuum pump, and then the field calculation verification is carried out on the single trial production result.
5. A pre-stress construction quality evaluation system based on the pre-stress construction quality evaluation method based on big data according to any one of claims 1 to 4, which is characterized by comprising a digital component module, a foundation test module, a big data platform and a process monitoring module;
the digital component module is used for carrying out digital modeling on the engineering component, setting model classification and presetting theoretical parameters according to the specific conditions and requirements of project implementation by extracting information in a paper design file of the engineering component, and forming a lightweight digital model of the project;
the basic test module comprises equipment, a device and supporting software for developing basic parameter tests, and consists of various stress and strain sensors, a data acquisition instrument and data analysis and processing software; before formal construction, according to requirements of site construction machines, materials and process characteristics, the verification of process parameters and the acquisition of standard parameters are completed in a modular combination mode according to a test organization scheme and a program;
the process monitoring module is used for tracking the field construction process, actively uploading field construction data to the big data platform in real time by adopting the technology of Internet of things, and receiving remote access and data tracing requests of the big data platform;
the big data platform is used for uniformly managing the lightweight digital model, the basic standard sample library and the construction monitoring data, and brings construction plans, data acquisition, analysis processing and result evaluation into integrated dynamic management through a preset construction quality evaluation framework.
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