Poorarbabi et al., 2020 - Google Patents
Concrete compressive strength prediction using neural networks based on non-destructive tests and a self-calibrated response surface methodologyPoorarbabi et al., 2020
- Document ID
- 14628890943042639740
- Author
- Poorarbabi A
- Ghasemi M
- Azhdary Moghaddam M
- Publication year
- Publication venue
- Journal of Nondestructive Evaluation
External Links
Snippet
An artificial neural network (ANN) model and response surface methodology (RSM) were established to estimate the compressive strength of concrete by using the combination of three non-destructive tests (NDT); rebound number, pulse velocity tests and resistance …
- 239000004567 concrete 0 title abstract description 66
Classifications
-
- G—PHYSICS
- G01—MEASURING; TESTING
- G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
- G01N33/00—Investigating or analysing materials by specific methods not covered by the preceding groups
- G01N33/48—Investigating or analysing materials by specific methods not covered by the preceding groups biological material, e.g. blood, urine; Haemocytometers
- G01N33/50—Chemical analysis of biological material, e.g. blood, urine; Testing involving biospecific ligand binding methods; Immunological testing
- G01N33/68—Chemical analysis of biological material, e.g. blood, urine; Testing involving biospecific ligand binding methods; Immunological testing involving proteins, peptides or amino acids
-
- G—PHYSICS
- G01—MEASURING; TESTING
- G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
- G01N33/00—Investigating or analysing materials by specific methods not covered by the preceding groups
- G01N33/38—Investigating or analysing materials by specific methods not covered by the preceding groups concrete; ceramics; glass; bricks
- G01N33/383—Concrete, cement
-
- G—PHYSICS
- G01—MEASURING; TESTING
- G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
- G01N29/00—Investigating or analysing materials by the use of ultrasonic, sonic or infrasonic waves; Visualisation of the interior of objects by transmitting ultrasonic or sonic waves through the object
- G01N29/04—Analysing solids
- G01N29/041—Analysing solids on the surface of the material, e.g. using Lamb, Rayleigh or shear waves
-
- G—PHYSICS
- G01—MEASURING; TESTING
- G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
- G01N2291/00—Indexing codes associated with group G01N29/00
- G01N2291/02—Indexing codes associated with the analysed material
- G01N2291/028—Material parameters
-
- G—PHYSICS
- G01—MEASURING; TESTING
- G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
- G01N29/00—Investigating or analysing materials by the use of ultrasonic, sonic or infrasonic waves; Visualisation of the interior of objects by transmitting ultrasonic or sonic waves through the object
- G01N29/04—Analysing solids
- G01N29/07—Analysing solids by measuring propagation velocity or propagation time of acoustic waves
-
- G—PHYSICS
- G01—MEASURING; TESTING
- G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
- G01N2291/00—Indexing codes associated with group G01N29/00
- G01N2291/04—Wave modes and trajectories
- G01N2291/042—Wave modes
-
- G—PHYSICS
- G01—MEASURING; TESTING
- G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
- G01N2203/00—Investigating strength properties of solid materials by application of mechanical stress
- G01N2203/02—Details not specific for a particular testing method
- G01N2203/0202—Control of the test
- G01N2203/0212—Theories, calculations
-
- G—PHYSICS
- G01—MEASURING; TESTING
- G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
- G01N2203/00—Investigating strength properties of solid materials by application of mechanical stress
- G01N2203/0058—Kind of property studied
-
- G—PHYSICS
- G01—MEASURING; TESTING
- G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
- G01N29/00—Investigating or analysing materials by the use of ultrasonic, sonic or infrasonic waves; Visualisation of the interior of objects by transmitting ultrasonic or sonic waves through the object
- G01N29/44—Processing the detected response signal, e.g. electronic circuits specially adapted therefor
- G01N29/4409—Processing the detected response signal, e.g. electronic circuits specially adapted therefor by comparison
-
- G—PHYSICS
- G01—MEASURING; TESTING
- G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
- G01N27/00—Investigating or analysing materials by the use of electric, electro-chemical, or magnetic means
- G01N27/72—Investigating or analysing materials by the use of electric, electro-chemical, or magnetic means by investigating magnetic variables
-
- G—PHYSICS
- G01—MEASURING; TESTING
- G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
- G01N2291/00—Indexing codes associated with group G01N29/00
- G01N2291/01—Indexing codes associated with the measuring variable
- G01N2291/014—Resonance or resonant frequency
-
- G—PHYSICS
- G01—MEASURING; TESTING
- G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
- G01N29/00—Investigating or analysing materials by the use of ultrasonic, sonic or infrasonic waves; Visualisation of the interior of objects by transmitting ultrasonic or sonic waves through the object
- G01N29/22—Details, e.g. general constructional or apparatus details
-
- G—PHYSICS
- G01—MEASURING; TESTING
- G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
- G01N29/00—Investigating or analysing materials by the use of ultrasonic, sonic or infrasonic waves; Visualisation of the interior of objects by transmitting ultrasonic or sonic waves through the object
- G01N29/44—Processing the detected response signal, e.g. electronic circuits specially adapted therefor
- G01N29/4472—Mathematical theories or simulation
-
- G—PHYSICS
- G01—MEASURING; TESTING
- G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
- G01N3/00—Investigating strength properties of solid materials by application of mechanical stress
-
- G—PHYSICS
- G01—MEASURING; TESTING
- G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
- G01N2800/00—Detection or diagnosis of diseases
-
- G—PHYSICS
- G01—MEASURING; TESTING
- G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
- G01N9/00—Investigating density or specific gravity of materials; Analysing materials by determining density or specific gravity
Similar Documents
Publication | Publication Date | Title |
---|---|---|
Poorarbabi et al. | Concrete compressive strength prediction using neural networks based on non-destructive tests and a self-calibrated response surface methodology | |
Bungey et al. | Testing of concrete in structures | |
Breysse | Nondestructive evaluation of concrete strength: An historical review and a new perspective by combining NDT methods | |
Sbartaï et al. | Combining NDT techniques for improved evaluation of concrete properties | |
Huang et al. | Predicting Concrete Compressive Strength Using Ultrasonic Pulse Velocity and Rebound Number. | |
Hoła et al. | Application of artificial neural networks to determine concrete compressive strength based on non‐destructive tests | |
Mishra et al. | A comparative study of regression, neural network and neuro-fuzzy inference system for determining the compressive strength of brick–mortar masonry by fusing nondestructive testing data | |
Barham et al. | Mechanical and physical based artificial neural network models for the prediction of the unconfined compressive strength of rock | |
Razzaghi et al. | Point-load test and UPV for compressive strength prediction of recycled coarse aggregate concrete via generalized GMDH-class neural network | |
Breysse et al. | Strength assessment in reinforced concrete structures: from research to improved practices | |
Martínez-Molina et al. | Predicting concrete compressive strength and modulus of rupture using different NDT techniques | |
Breysse et al. | Non-destructive in situ strength assessment of concrete | |
Plati et al. | Incorporation of GPR data into genetic algorithms for assessing recycled pavements | |
Zaborac et al. | Crack-based shear strength assessment of reinforced concrete members using a fixed-crack continuum modeling approach | |
Amini et al. | Development of prediction models for mechanical properties and durability of concrete using combined nondestructive tests | |
Diaferio et al. | Correlation curves to characterize concrete strength by means of UPV tests | |
Dauji et al. | Estimating concrete strength from nondestructive testing with few core tests considering uncertainties | |
Arora et al. | Ensemble learning based compressive strength prediction of concrete structures through real-time non-destructive testing | |
Olanitori | Causes of structural failures of a building: Case study of a building at Oba-Ile, Akure | |
Bolborea et al. | Study Regarding the Evaluation of Prediction Models for Determining the Concrete Compressive Strength Using Non-Destructive Testing (NDT) Data: Validation Stage | |
Kazemifard et al. | NDT prediction of self-compacting concrete strength based on maturity method | |
Faroz et al. | Tri-level framework for realistic estimation of concrete strength using Bayesian data fusion of UPV and guided coring | |
Nimer et al. | Artificial neural networks and noncontact microwave NDT for evaluation of polypropylene fiber concrete | |
Kabashi et al. | Evaluation the compressive strength in concrete structures using the in-situ test methods | |
Alavi et al. | Challenges for the Development of Artificial Intelligence Models to Predict the Compressive Strength of Concrete Using Non-destructive Tests: A Review |