Bridge deck concrete flatness detection and correction method

Abstract

The invention relates to the technical field of bridge engineering construction, in particular to a bridge deck concrete flatness detection and correction method, which comprises the following steps: synchronously acquiring bridge deck point cloud, elevation and crack data through a three-dimensional laser scanner, a laser profiler and an ultrasonic sensor, and fusing to generate a three-dimensional digital model; defects such as local protrusions and wave deformation are recognized based on an intelligent analysis algorithm, causes are analyzed, and correction process parameters such as milling, cladding, grouting or ultrathin layer pouring are generated in a targeted mode; operation is executed through equipment such as a finish milling machine, closed-loop control is realized by combining real-time monitoring of a laser profiler, and finally, a traceable digital file is formed through laser detection, inspection and acceptance of an unmanned aerial vehicle and data archiving. By means of the method, the pass percent of the flatness of the bridge floor can be increased to 94% or above, the maximum height difference is controlled within 3 mm, the maintenance period of the bridge is prolonged, and the maintenance cost is reduced.

Description

Technical Field

[0001] This invention relates to the field of bridge engineering construction technology, specifically to a method for detecting and correcting the flatness of bridge deck concrete. Background Technology

[0002] In bridge construction, the smoothness of the bridge deck concrete is a key factor affecting the bridge's performance. An uneven bridge deck can cause vehicles to experience bumpy rides, reducing driving comfort, increasing vehicle wear and fuel consumption, and in severe cases, even threatening driving safety. Furthermore, long-term unevenness can accelerate damage to the bridge structure, shorten the bridge's lifespan, and increase subsequent maintenance costs. Currently, traditional methods for bridge deck flatness testing and correction have many shortcomings. In terms of testing, the commonly used method is the ruler method, which is not only inefficient but also only obtains discrete, localized data, making it difficult to comprehensively and accurately reflect the overall flatness of the bridge deck. The testing error can typically reach 10mm. While continuous flatness gauges can improve testing efficiency to some extent, their ability to detect complex bridge deck structures and subtle defects remains limited. In the correction phase, traditional methods often rely on manual experience to determine the type of defect and then carry out targeted repairs. This approach is not only highly subjective but also unable to achieve real-time dynamic correction, lacking the systematic ability to address issues such as beam cross slope errors and pavement layer thickness deviations. Furthermore, the disconnect between the inspection and construction phases prevents timely adjustments to construction parameters based on real-time inspection data, resulting in a difficulty in effectively improving the bridge deck flatness pass rate; the pass rate of traditional methods is generally only around 80%. For example, in the early stages of the Longqian Viaduct project on the Ruicang Expressway, the traditional T-beam prefabrication process was adopted. Due to the large synchronization error of the jacks adjusted manually (the maximum height difference could reach 2.6cm) and the elastic deformation of the steel formwork during construction (deflection of 1.2mm), the pass rate of the flatness of the bridge deck was only 80%, and the maximum height difference reached 5mm, far exceeding the ≤4mm specified in Class I of the "Highway Engineering Quality Inspection and Evaluation Standard". Therefore, developing a method for treating the flatness of bridge deck concrete that integrates high-precision detection, real-time data analysis, and intelligent correction has become an important issue that urgently needs to be addressed in the field of bridge engineering. Summary of the Invention

[0003] To address the problems of low accuracy in bridge deck concrete flatness detection, outdated correction methods, and poor coordination between detection and construction in existing technologies, this invention provides a method for detecting and correcting bridge deck concrete flatness. By constructing a complete closed-loop control system for detection-analysis-correction, the pass rate of bridge deck flatness can be increased to over 94%, while the maximum height difference can be strictly controlled within 3mm, thereby significantly improving the performance and durability of bridges.

[0004] The solution adopted by this invention to solve its technical problem is: a method for detecting and correcting the flatness of bridge deck concrete, comprising the following steps: Step 1, Multi-source data acquisition and fusion: The bridge deck point cloud, elevation and crack data are acquired simultaneously by a 3D laser scanner, laser profiler and ultrasonic sensor, and fused to generate a 3D digital model containing elevation, texture and crack information; Step 2, Intelligent Diagnosis and Defect Classification: Based on the intelligent analysis algorithm of multi-source sensing data, the flatness index is analyzed and the defect type and cause are automatically identified. The defects include local bulges, wave deformation, joint misalignment and interface voids. Step 3, Generation of Zoned Correction Strategy: Based on the defect type and spatial distribution, generate targeted correction process parameters, including milling, cladding, grouting or ultra-thin layer casting schemes. Step 4, Execution and Closed-Loop Control: The correction operation is performed by a precision milling machine, grouting equipment, and automatic film covering and curing machine, and the correction effect is monitored in real time by a laser profiler. If the standard is not met, a second correction is triggered. Step 5, Quality Acceptance and Data Archiving: Quality acceptance is carried out using laser profiler inspection and drone inspection, and all data throughout the process is stored in a digital system to form a traceable digital as-built archive.

[0005] Furthermore, the specific parameters for multi-source data acquisition in step 1 include: the scanning frequency of the 3D laser scanner is ≥100kHz, the sampling interval of the laser profilometer is 10cm, and the sampling frequency of the ultrasonic sensor is ≥40kHz; synchronous acquisition is achieved at the microsecond level through an IMU.

[0006] Furthermore, the intelligent analysis algorithm is based on a rule-based intelligent analysis algorithm for multi-source data. This algorithm is trained based on historical bridge deck defect data and can automatically identify and classify the following defect types: local bulges: height deviation greater than 3mm and wavelength less than 1m; wave deformation: wavelength between 1 and 3m and amplitude greater than 5mm; joint misalignment: height difference greater than 3mm; interface voids: area not less than 0.2㎡.

[0007] Furthermore, in step 3, the correction process parameters include, for different types of defects: For local protrusions (h≤8mm), fine milling is used with a milling depth of 3–5mm, followed by injection of nano-modified epoxy resin. For interface voids, use ground-penetrating radar to detect them before drilling and grouting. The grout fluidity should be no less than 300mm. For misaligned joints (Δh≥3mm), diamond wire saw is used to cut the joint, GFRP reinforcement is inserted, and UHPC ultra-thin layer is poured. For wave deformation, a laser-clad iron-based alloy layer is used followed by grinding, with a grinding accuracy Ra≤0.8μm.

[0008] Furthermore, the flatness indicators mentioned in step 2 include the International Roughness Index (IRI) and the maximum gap of a 3m straightedge; the input parameters of the intelligent analysis algorithm also include ambient temperature, humidity and the service life of the bridge, and the output defect causes include material shrinkage, construction error, load cumulative damage and temperature deformation.

[0009] Furthermore, in step 4, the precision milling machine is a Wirtgen W2000 model, and the milling depth error is controlled within ±0.5mm; the grouting equipment has a pressure feedback function, and the grouting pressure is not less than 0.5MPa; the automatic film covering and curing machine has a film overlap width of not less than 10cm, and sprays moisture after film covering.

[0010] Furthermore, the flatness index IRI value detected by the laser profilometer is no greater than 2.0 m / km, and the standard deviation is no more than 1.2 mm; the UAV inspection is equipped with an infrared thermal imager to detect temperature segregation areas, and areas with temperature deviations exceeding 10℃ are marked as potential defect areas.

[0011] Furthermore, the digital as-built archives include 3D point cloud comparison images before and after construction, correction process parameter records, material testing reports, flatness test data, and infrared thermal images. All data is uploaded to the cloud management system, supporting full lifecycle traceability and data analysis.

[0012] The beneficial effects of this invention are as follows: This invention achieves a three-dimensional digital model with an elevation accuracy of ±0.5mm and a minimum crack width of 0.2mm by simultaneously acquiring multi-source data from a three-dimensional laser scanner, a laser profiler, and an ultrasonic sensor, combined with microsecond-level synchronous control of an IMU and the fusion of ICP algorithm. This significantly improves the accuracy compared to the traditional 3m ruler detection. At the same time, the combination of global point cloud data and continuous elevation curves overcomes the defects of traditional detection, which are discrete and localized, and can comprehensively reflect the overall flatness of the bridge deck. The rule-based intelligent analysis algorithm, trained on a historical defect database, can automatically identify four types of defects, including local bulges and wave deformation. It correlates the causes with environmental parameters and the bridge's service life, reducing subjective bias compared to manual judgment and achieving a higher accuracy rate in defect classification. Targeted correction schemes enable targeted correction, improving correction efficiency. During execution, real-time monitoring and secondary correction mechanisms using laser profilers significantly improve the pass rate compared to traditional processes. Parametric operation avoids the fluctuations of manual construction, making the correction effect reproducible and verifiable. This method is applicable to precast T-beam bridges, as well as steel box girders, cast-in-place box girders, and other bridge types. It is suitable for both normal temperature areas and extreme climates ranging from -10℃ to 50℃. It can be quickly transferred by adjusting the equipment model and process parameters, providing a replicable and scalable technical paradigm for high-quality bridge construction. Attached Figure Description

[0013] Figure 1 This is the closed-loop control logic diagram of the present invention. Detailed Implementation

[0014] The present invention will be further described below with reference to the accompanying drawings and embodiments.

[0015] This invention provides a technical solution for detecting and correcting the flatness of bridge deck concrete: Example 1: Project Background: The Longqian Viaduct on the Ruicang Expressway is a two-way four-lane highway bridge, 1.2km long, with a bridge deck width of 12.5m and a design speed of 100km / h. Initially, the bridge was constructed using traditional methods, resulting in a pavement flatness pass rate of only 80% and a maximum height difference of 5mm, far exceeding the Class I standard (≤4mm) in the "Highway Engineering Quality Inspection and Evaluation Standard" (JTGF80 / 1-2017). After flatness testing and correction based on the method of this invention, the flatness pass rate increased to 94%, and the maximum height difference was controlled within 3mm.

[0016] The specific implementation steps of the bridge deck concrete flatness detection and correction method provided by this invention are as follows: Step 1: Multi-source data acquisition and fusion (1) Equipment selection and parameter settings: 3D laser scanner: Leica P40 model is selected, the scanning frequency is set to 120kHz (meeting the requirement of ≥100kHz), the point cloud density reaches 100 points / ㎡, and the fine texture of the bridge surface is captured; Laser profiler: Trimble SX10 model is used, the sampling interval is set to 10cm, and a profile is collected every 5m along the longitudinal direction of the bridge surface to cover the entire bridge area; Ultrasonic sensor: Panametrics-NDT5072PR model is selected, the sampling frequency is set to 50kHz (meeting the requirement of ≥40kHz), and it is used to detect cracks and interface voids inside the bridge surface; Synchronization control: the microsecond-level synchronization of the three devices is achieved through the built-in IMU (inertial measurement unit) to ensure that the point cloud, elevation and crack data are matched in the time dimension.

[0017] Before data collection, the bridge deck was pre-processed by an electro-hydraulic T-beam cross slope high-precision adjustment system: a hydraulic actuator (thrust 50kN, stroke ±50mm) was used in conjunction with an Omron ZX2-LD300 displacement sensor (resolution 0.01mm) to control the cross slope of the T-beam flange within the error range of ±0.02%, providing a basic flatness guarantee for data collection.

[0018] (2) Data acquisition process: The acquisition equipment is moved at a speed of 5km / h along the longitudinal direction of the bridge deck to acquire the following data simultaneously: The 3D laser scanner generates a full-domain point cloud model containing elevation and texture information, covering 100% of the bridge deck area; the laser profiler records continuous elevation curves simultaneously and calculates the International Roughness Index (IRI) and the maximum gap of the 3m straightedge; the ultrasonic sensor detects the crack depth (minimum identification 0.2mm) and the interface void range (minimum identification 0.2㎡); during the acquisition process, environmental parameters (temperature 25℃, humidity 60%) and the bridge service life (2 years) are recorded simultaneously.

[0019] (3) Data fusion processing: The point cloud registration algorithm (ICP algorithm) is used to fuse multi-source data to generate a three-dimensional digital model containing elevation (accuracy ±0.5mm), texture (resolution 0.1mm) and crack (minimum recognition width 0.2mm) information, with model error ≤1mm.

[0020] Step 2: Intelligent Diagnosis and Defect Classification (1) Deployment of intelligent analysis algorithm: Based on the database of similar bridge defects of Ruicang Expressway in the past 5 years (containing 1000+ defect cases), a rule-based intelligent analysis algorithm is trained. The input parameters of the algorithm include: elevation deviation, texture features, crack distribution in the three-dimensional model, as well as ambient temperature, humidity, and bridge service life.

[0021] (2) Defect identification and classification results: The algorithm automatically identifies and classifies the following defects: Local protrusions: 8 locations were detected, with height deviations of 3-8mm and wavelengths of 0.3-0.8m, all meeting the standard of height deviation > 3mm and wavelength < 1m; Wave deformation: 3 locations were detected, with wavelengths of 1.5-2.8m and amplitudes of 6-8mm, meeting the standard of wavelengths of 1-3m and amplitudes > 5mm; Joint misalignment: 5 instances were detected, with a height difference of 3-5mm, which meets the standard of height difference > 3mm; Interface voids: Two voids were detected, with areas of 0.3㎡ and 0.5㎡ respectively, which meet the standard of area ≥ 0.2㎡.

[0022] (3) Defect cause analysis: The algorithm output causes: local bulges are related to uneven vibration of the paver during construction; wave deformation is caused by temperature deformation (high temperature difference in summer); joint misalignment is caused by T-beam installation error and elastic deformation of steel formwork; interface void is caused by defects in the construction of waterproof layer.

[0023] Step 3: Generation of Zonal Correction Strategy (1) Generate targeted correction process parameters based on defect type and distribution: Local protrusions (h≤8mm): Corrected by precision milling + nano-modified epoxy resin injection, milling depth 3-5mm (corresponding to protrusion height 3-8mm), nano-modified epoxy resin compressive strength ≥120MPa, bonding strength ≥4.5MPa; Wave deformation: Corrected by laser cladding of iron-based alloy layer + grinding, with cladding layer thickness of 1.0mm (FeCrNiMoB alloy, hardness HRC58-62) and grinding accuracy Ra≤0.8μm; Joint misalignment (Δh≥3mm): Correction is achieved by diamond wire saw cutting + GFRP reinforcement insertion + UHPC ultra-thin layer casting, with a cutting width of 5mm; GFRP reinforcement φ8mm@150mm (tensile strength≥800MPa), UHPC layer thickness 15mm (28d flexural strength≥25MPa); Interface voids: Correction is achieved by using ground-penetrating radar positioning + borehole grouting. The ground-penetrating radar frequency is 1GHz (positioning error ≤5cm), the borehole diameter is 10mm@400mm, the grouting material fluidity is ≥300mm, and the vertical expansion rate is ≥0.03%.

[0024] The above solution references research on the correlation between bridge deck defects and layer damage. For misaligned joint areas, GFRP reinforcement is implanted to enhance interlayer shear strength. For areas with interface voids, high-flowability grout is used to fill the voids, thus solving the interlayer delamination problem caused by waterproofing layer failure.

[0025] Step 4: Execution and Closed-Loop Control (1) Equipment deployment and operation: Precision milling: Wirtgen W2000 precision milling machine is used, and the milling depth error is controlled within ±0.5mm. For local protruding areas, milling is carried out in two stages (3mm for the first time and 2mm for the second time); Grouting: Grouting equipment with pressure feedback function is used, and the grouting pressure is maintained at 0.6MPa (≥0.5MPa). The fullness of the void area is judged by the pressure curve; Laser cladding: IPGYLR-10000 laser is used, the cladding speed is 500mm / min, and the interlayer temperature is controlled within 150℃; Curing: Automatic film curing machine is used for film covering, with an overlap width of 12cm (≥10cm). After film covering, the film is sprayed to keep it moist (humidity ≥90%). During the correction process, a laser-ultrasonic intelligent paving system is introduced for collaborative control: a 650nm cross laser projects a 1m×1m positioning grid (accuracy ±0.05mm), and a 40kHz ultrasonic sensor collects concrete rheological parameters in real time. The algorithm ( Dynamic elevation correction ensures milling and pouring accuracy of ±0.3mm. The application of laser ultrasonic pavers greatly improves paving quality. Elevation and flatness are monitored and controlled in real time. The paver is equipped with two motors on its travel track, which can control movement synchronously or asynchronously. The paver's built-in material placement beam is equipped with a high-frequency vibrator, enabling automatic material placement and vibration, saving labor and time in the material placement, vibration, and mechanical movement stages. Combined with a ride-on power trowel, the finishing quality is further improved, shortening construction time and significantly enhancing overall efficiency compared to traditional construction methods.

[0026] (2) Real-time monitoring and secondary correction: During the correction process, the laser profiler monitors in real time with a sampling interval of 10cm. After correcting the three wave deformations, the IRI value of one location was found to be 2.2m / km (exceeding the standard of 2.0m / km), which triggered secondary grinding. Finally, the IRI value dropped to 1.8m / km, meeting the acceptance standard.

[0027] Step 5: Quality Acceptance and Data Archiving (1) The following methods are used for quality acceptance: Laser profiler test: 50 test points are randomly selected from the whole bridge. The IRI value is ≤1.9m / km and the standard deviation is ≤1.0mm, which meets the standard; UAV inspection: Equipped with FLIRT1020 infrared thermal imager, the temperature segregation area is detected. The maximum temperature deviation is 8℃ and there is no potential defect area.

[0028] (2) Data archiving: All data throughout the process is uploaded to the cloud management system, including: three-dimensional point cloud comparison before and after construction (deviation ≤1mm), correction process parameter records (milling depth, grouting pressure, etc.), material testing reports (performance indicators of nano-modified epoxy resin, UHPC, etc.), flatness test data (IRI value, standard deviation) and infrared thermal images. The data supports full life cycle traceability and can query any construction details through the bridge ID.

[0029] After the method provided by this invention is applied, the pass rate of bridge deck flatness of the Longqian Viaduct on the Ruicang Expressway has increased from 80% to 94%, and the maximum height difference is controlled within 3mm, meeting the Class I standard of the "Highway Engineering Quality Inspection and Evaluation Standard"; the bridge deck maintenance cycle has been extended to 15 years (about 8-10 years with traditional methods), and the annual maintenance cost has been reduced by about 180,000 yuan.

[0030] Example 2: Addressing the interface void (area 0.4㎡) and joint misalignment issues on a bridge on the Dongyong Expressway. The defects are specifically corrected using the method of this invention: Interface void correction: After positioning by ground-penetrating radar, borehole grouting was performed. The grouting material had a flowability of 320 mm and an injection pressure of 0.5 MPa. After 24 hours, the void rate was found to be reduced to 1% (≤2% standard). Joint misalignment correction: After diamond wire saw cutting, GFRP reinforcement is inserted, and a 50cm wide UHPC ultrathin layer (15mm thick) is poured. The misalignment height is tested and reduced to 0.3mm (≤0.5mm standard).

[0031] The acceptance results showed that the IRI value of the correction area was 1.7 m / km, with a standard deviation of 0.9 mm, which met the quality requirements.

[0032] Example 3: Based on the above examples, this example further illustrates the laser-ultrasonic intelligent paving system.

[0033] Based on traditional suspended truss split roller pavers, this system deeply integrates laser ranging, ultrasonic sensing, and advanced hydraulic control technologies to construct a bridge deck paving control system that combines high-precision positioning, real-time feedback, and intelligent adjustment. The ultrasonic-laser integrated paving system uses a 0.1mm resolution laser sensor and a 20kHz ultrasonic vibrator, adjusting the paving layer elevation in real time through a hydraulic closed-loop control system. The system response time is ≤0.5s, improving positioning accuracy by 40% compared to traditional methods. Laser-ultrasonic coordinated control achieves millimeter-level smoothness through the following steps: (1) Laser grid calibration: A cross laser (wavelength 650nm) projects a 1m×1m positioning grid, and its ranging accuracy of ±0.05mm establishes a millimeter-level spatial reference; (2) Ultrasonic vibration feedback: A 40kHz ultrasonic sensor collects concrete rheological parameters every 0.1s. When the slump deviation is >5%, the vibration intensity is automatically increased. The elevation correction is calculated by combining the laser ranging deviation ΔL and the ultrasonic slump deviation ΔU. △H = α·△L + β·△U In the formula: △H is the elevation correction amount (mm), △L is the laser ranging deviation (mm), △U is the ultrasonic slump deviation (%), and α=0.8 and β=0.2 are weighting coefficients.

[0034] (3) Dynamic elevation correction: After the laser ranging data and ultrasonic feedback are fused, the elevation of the paver hydraulic cylinder is adjusted according to the above formula.

[0035] The above description is only a preferred embodiment of the present invention and does not limit the present invention. Any modifications, equivalent substitutions and improvements made within the spirit and principles of the present invention should be included within the protection scope of the present invention.

Claims

1. A method for detecting and correcting the flatness of bridge deck concrete, characterized in that, Includes the following steps: Step 1, Multi-source data acquisition and fusion: The bridge deck point cloud, elevation and crack data are acquired simultaneously by a 3D laser scanner, laser profiler and ultrasonic sensor, and fused to generate a 3D digital model containing elevation, texture and crack information; Step 2, Intelligent Diagnosis and Defect Classification: Based on the intelligent analysis algorithm of multi-source sensing data, the flatness index is analyzed and the defect type and cause are automatically identified. The defects include local bulges, wave deformation, joint misalignment and interface voids. Step 3, Generation of Zoned Correction Strategy: Based on the defect type and spatial distribution, generate targeted correction process parameters, including milling, cladding, grouting or ultra-thin layer casting schemes. Step 4, Execution and Closed-Loop Control: The correction operation is performed by a precision milling machine, grouting equipment, and automatic film covering and curing machine, and the correction effect is monitored in real time by a laser profiler. If the standard is not met, a second correction is triggered. Step 5, Quality Acceptance and Data Archiving: Quality acceptance is carried out using laser profiler inspection and drone inspection, and all data throughout the process is stored in a digital system to form a traceable digital as-built archive.

2. The method for detecting and correcting the flatness of bridge deck concrete according to claim 1, characterized in that, The specific parameters for multi-source data acquisition in step 1 include: the scanning frequency of the 3D laser scanner is ≥100kHz, the sampling interval of the laser profilometer is 10cm, and the sampling frequency of the ultrasonic sensor is ≥40kHz; synchronous acquisition is achieved at the microsecond level through an IMU.

3. The method for detecting and correcting the flatness of bridge deck concrete according to claim 1, characterized in that, The intelligent analysis algorithm is a rule-based intelligent analysis algorithm based on multi-source data. The algorithm is trained based on historical bridge deck defect data and can automatically identify and classify the following defect types: local bulge: height deviation greater than 3mm and wavelength less than 1m; wave deformation: wavelength between 1 and 3m and amplitude greater than 5mm; joint misalignment: height difference greater than 3mm; interface void: area not less than 0.2㎡.

4. A method for detecting and correcting the flatness of bridge deck concrete according to claim 1 or 3, characterized in that, In step 3, the correction process parameters include, for different types of defects: For local protrusions (h≤8mm), fine milling is used with a milling depth of 3–5mm, followed by injection of nano-modified epoxy resin. For interface voids, use ground-penetrating radar to detect them before drilling and grouting. The grout fluidity should be no less than 300mm. For misaligned joints (Δh≥3mm), diamond wire saw is used to cut the joint, GFRP reinforcement is inserted, and UHPC ultra-thin layer is poured. For wave deformation, a laser-clad iron-based alloy layer is used followed by grinding, with a grinding accuracy Ra≤0.8μm.

5. The method for detecting and correcting the flatness of bridge deck concrete according to claim 3, characterized in that, The flatness indicators mentioned in step 2 include the International Roughness Index (IRI) and the maximum gap of a 3m straightedge; the input parameters of the intelligent analysis algorithm also include ambient temperature, humidity and bridge service life, and the output defect causes include material shrinkage, construction error, load cumulative damage and temperature deformation.

6. The method for detecting and correcting the flatness of bridge deck concrete according to claim 1, characterized in that, In step 4, the precision milling machine is a Wirtgen W2000 model, and the milling depth error is controlled within ±0.5mm; the grouting equipment has a pressure feedback function, and the grouting pressure is not less than 0.5MPa; the automatic film covering and curing machine has a film overlap width of not less than 10cm, and sprays moisture after film covering.

7. The method for detecting and correcting the flatness of bridge deck concrete according to claim 1, characterized in that, The flatness index IRI value detected by the laser profilometer is no greater than 2.0 m / km, and the standard deviation is no more than 1.2 mm; the UAV inspection is equipped with an infrared thermal imager to detect temperature segregation areas, and areas with temperature deviations exceeding 10℃ are marked as potential defect areas.

8. The method for detecting and correcting the flatness of bridge deck concrete according to claim 1, characterized in that, The digital as-built archives include 3D point cloud comparison images before and after construction, records of correction process parameters, material testing reports, flatness test data, and infrared thermal images. All data is uploaded to the cloud management system, supporting full lifecycle traceability and data analysis.

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