Javid et al., 2024 - Google Patents
Utilizing ensemble machine learning and gray wolf optimization to predict the compressive strength of silica fume mixturesJavid et al., 2024
- Document ID
- 1542478274602826422
- Author
- Javid A
- Toufigh V
- Publication year
- Publication venue
- Structural Concrete
External Links
Snippet
The concrete compressive strength is essential for the design and durability of concrete infrastructure. Silica fume (SF), as a cementitious material, has been shown to improve the durability and mechanical properties of concrete. This study aims to predict the compressive …
- 229910021487 silica fume 0 title abstract description 97
Classifications
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06F—ELECTRICAL DIGITAL DATA PROCESSING
- G06F17/00—Digital computing or data processing equipment or methods, specially adapted for specific functions
- G06F17/30—Information retrieval; Database structures therefor; File system structures therefor
- G06F17/30861—Retrieval from the Internet, e.g. browsers
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06F—ELECTRICAL DIGITAL DATA PROCESSING
- G06F17/00—Digital computing or data processing equipment or methods, specially adapted for specific functions
- G06F17/30—Information retrieval; Database structures therefor; File system structures therefor
- G06F17/30286—Information retrieval; Database structures therefor; File system structures therefor in structured data stores
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06F—ELECTRICAL DIGITAL DATA PROCESSING
- G06F19/00—Digital computing or data processing equipment or methods, specially adapted for specific applications
- G06F19/10—Bioinformatics, i.e. methods or systems for genetic or protein-related data processing in computational molecular biology
- G06F19/16—Bioinformatics, i.e. methods or systems for genetic or protein-related data processing in computational molecular biology for molecular structure, e.g. structure alignment, structural or functional relations, protein folding, domain topologies, drug targeting using structure data, involving two-dimensional or three-dimensional structures
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06F—ELECTRICAL DIGITAL DATA PROCESSING
- G06F9/00—Arrangements for programme control, e.g. control unit
- G06F9/06—Arrangements for programme control, e.g. control unit using stored programme, i.e. using internal store of processing equipment to receive and retain programme
-
- 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
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06N—COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS
- G06N99/00—Subject matter not provided for in other groups of this subclass
- G06N99/005—Learning machines, i.e. computer in which a programme is changed according to experience gained by the machine itself during a complete run
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06Q—DATA PROCESSING SYSTEMS OR METHODS, SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL, SUPERVISORY OR FORECASTING PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL, SUPERVISORY OR FORECASTING PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q10/00—Administration; Management
- G06Q10/06—Resources, workflows, human or project management, e.g. organising, planning, scheduling or allocating time, human or machine resources; Enterprise planning; Organisational models
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06Q—DATA PROCESSING SYSTEMS OR METHODS, SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL, SUPERVISORY OR FORECASTING PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL, SUPERVISORY OR FORECASTING PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q10/00—Administration; Management
- G06Q10/10—Office automation, e.g. computer aided management of electronic mail or groupware; Time management, e.g. calendars, reminders, meetings or time accounting
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| Ling et al. | Combination of Support Vector Machine and K-Fold cross validation to predict compressive strength of concrete in marine environment | |
| Tavana Amlashi et al. | Estimation of the compressive strength of green concretes containing rice husk ash: a comparison of different machine learning approaches | |
| Chou et al. | Optimizing the prediction accuracy of concrete compressive strength based on a comparison of data-mining techniques | |
| Javid et al. | Utilizing ensemble machine learning and gray wolf optimization to predict the compressive strength of silica fume mixtures | |
| Kazemi et al. | Active learning on stacked machine learning techniques for predicting compressive strength of alkali-activated ultra-high-performance concrete | |
| Khan et al. | Prediction of compressive strength of fly ash-based geopolymer concrete using supervised machine learning methods | |
| Shubham et al. | Efficient compressive strength prediction of concrete incorporating industrial wastes using deep neural network | |
| Gogineni et al. | Evaluating machine learning algorithms for predicting compressive strength of concrete with mineral admixture using long short-term memory (LSTM) Technique | |
| Tran | Data‐driven approach for investigating and predicting of compressive strength of fly ash–slag geopolymer concrete | |
| Zeyad et al. | Compressive strength of nano concrete materials under elevated temperatures using machine learning | |
| Zhang et al. | Analyzing chloride diffusion for durability predictions of concrete using contemporary machine learning strategies | |
| Gogineni et al. | Prediction of compressive strength of glass fiber-reinforced self-compacting concrete interpretable by machine learning algorithms | |
| Alghamdi | Determining the mix design method for normal strength concrete using machine learning | |
| Abdellatief et al. | AI driven prediction of early age compressive strength in ultra high performance fiber reinforced concrete | |
| Zhang et al. | Prediction of concrete compressive strength using a Deepforest-based model | |
| Onyelowe et al. | Prediction and validation of mechanical properties of self-compacting geopolymer concrete using combined machine learning methods a comparative and suitability assessment of the best analysis | |
| Pratap | Analysis of mechanical properties of fly ash and bauxite residue based geopolymer concrete using ANN, Random Forest and Counter propagation neural network | |
| Hosseini et al. | Predicting the compressive strength of sulfur concrete using soft computing techniques | |
| Tamuly et al. | Machine learning based conformal predictors for uncertainty-aware compressive strength estimation of concrete | |
| Zhao | Predicting compressive strength of ultra-high-performance concrete using Naive Bayes regression in novel approaches | |
| Shaaban et al. | Machine learning approaches for forecasting compressive strength of high-strength concrete | |
| Faraj et al. | Analytical and innovative modeling investigations on the performance of nanoparticle-modified self-compacting mortars | |
| Kilicarslan et al. | Integrated approach to assessing strength in slag-based geopolymer mortars: experimental study and modeling with advanced techniques | |
| Mahmood et al. | Multiscale modeling for accurate forecasting of concrete wear depth: a comprehensive study on mixture proportions and environmental factors | |
| Nikmehr et al. | Performance Assessment of One-Part Self-Compacted Geopolymer Concrete Containing Recycled Concrete Aggregate: A Critical Comparison Using Artificial Neural Network (ANN) and Linear Regression Models. |