Related references
Note: Only part of the references are listed.
Article
Construction & Building Technology
Tan Li et al.
Summary: A BP-NN model was used to analyze the relationship between cube compressive strength and various strength indicators of concrete with large-sized recycled aggregates, optimizing the model with input parameters and predicting the mechanical properties. The study suggested a 30% incorporation rate for maximizing the utilization of recycled aggregates in concrete.
ADVANCES IN STRUCTURAL ENGINEERING
(2022)
Article
Engineering, Civil
Mohammad Sadegh Barkhordari et al.
Summary: This study utilizes artificial neural networks and optimization algorithms to determine the shear strength of reinforced concrete deep beams, and the results demonstrate that the PSO-ANN algorithm is more accurate than previous methods. The span of the beam and the ratio of the span to depth have the highest impact on predicting shear strength.
PERIODICA POLYTECHNICA-CIVIL ENGINEERING
(2022)
Article
Computer Science, Artificial Intelligence
Danial Jahed Armaghani et al.
Summary: A new technique, ANFIS-GMDH-ICA, is proposed for predicting pile bearing capacity, with higher accuracy compared to traditional models. This technology can be utilized in the field of foundation engineering and design.
ARTIFICIAL INTELLIGENCE REVIEW
(2022)
Article
Computer Science, Artificial Intelligence
Qui X. Lieu et al.
Summary: This article introduces a simple and effective adaptive surrogate model using deep neural network for structural reliability analysis. The approach enhances accuracy by adding important boundary points to the global model and achieves precise failure probability assessment with only a small number of experiments.
EXPERT SYSTEMS WITH APPLICATIONS
(2022)
Article
Construction & Building Technology
Mohammad Sadegh Barkhordari et al.
Summary: This study evaluated the effectiveness of using Timoshenko's beam theory to simulate the behavior of concrete shear walls and introduced a new constitutive model based on modified compression field theory. By comparing numerical model results with laboratory tests, it was found that the analysis procedure accurately predicted the overall and local responses of reinforced concrete walls.
MAGAZINE OF CONCRETE RESEARCH
(2021)
Article
Computer Science, Artificial Intelligence
Danial Jahed Armaghani et al.
Summary: This study investigates the use of artificial intelligence techniques to predict the compressive strength of cement-based mortars. Both ANN and ANFIS models were found to reliably approximate the strength of mortars, with ANFIS outperforming ANN but showing signs of overfitting during verification. The developed ANN model was introduced as the best predictive technique for solving the problem of mortar compressive strength.
NEURAL COMPUTING & APPLICATIONS
(2021)
Article
Engineering, Civil
Ashhad Imam et al.
Summary: In this study, a quaternary blend concrete was developed with the use of neural network models to estimate compressive strength. The optimized ANN with Bayesian regularization performed best, showing significant improvement compared to experimental observations.
JOURNAL OF STRUCTURAL INTEGRITY AND MAINTENANCE
(2021)
Article
Acoustics
Seunghye Lee et al.
Summary: In this study, deep learning techniques were used for structural damage detection of truss structures by training deep neural networks to recognize response patterns of undamaged and damaged structures. The results demonstrated that the proposed surrogate model was able to accurately detect damaged states.
Article
Construction & Building Technology
Tahereh Korouzhdeh et al.
Summary: The study found that the use of fine cement in concrete can significantly reduce porosity, increase flexural and compressive strength. A new multi-objective model combining artificial neural network and biogeography-based optimization can improve prediction accuracy. For concrete design, a cement-to-sand ratio of 2.75 is the optimal choice.
CONSTRUCTION AND BUILDING MATERIALS
(2021)
Article
Environmental Sciences
Mohammad Amin Hariri-Ardebili et al.
Summary: A hybrid Random Field-Polynomial Chaos Expansion surrogate model for uncertainty quantification and sensitivity assessment of dams is proposed in this study. The most sensitive spatial locations within dam body for different vibration modes are identified using Sobol's indices and correlation rank methods. The results of the hybrid model are validated using the classical random forest regression method, improving system identification and dynamic analysis outcomes.
Article
Green & Sustainable Science & Technology
Wafaa Mohamed Shaban et al.
Summary: A method for accurately predicting the compressive strength of strengthened recycled aggregate concrete was developed, integrating the SSA and DE algorithms to achieve better prediction performance. Sensitivity analysis showed that certain material contents and properties played vital roles in the model prediction.
JOURNAL OF CLEANER PRODUCTION
(2021)
Article
Construction & Building Technology
Vahab Toufigh et al.
Summary: Geopolymer concrete is an eco-friendly alternative to traditional concrete, but the lack of standard mix design is a major obstacle to its use in the construction industry. This study proposes a comprehensive model for predicting the compressive strength of fly ash-based geopolymer concrete, highlighting the importance of chemical compositions on the strength. The model can estimate the compressive strength of low calcium FAGC with acceptable accuracy, saving time and money in practical applications.
CONSTRUCTION AND BUILDING MATERIALS
(2021)
Article
Chemistry, Physical
Ayaz Ahmad et al.
Summary: Machine learning techniques are widely used in predicting the mechanical properties of concrete. By comparing individual algorithms with ensemble approaches such as bagging, it was found that the ensemble model outperforms decision trees and gene expression programming. Optimization of bagging can improve model accuracy, as demonstrated by various statistical indicators.
Article
Computer Science, Artificial Intelligence
Xingjian Li et al.
Summary: In-Network Ensemble (INE) proposes a novel learning paradigm that incorporates the diversity of multiple models through training a SINGLE deep neural network. INE outperforms state-of-the-art algorithms for deep ensemble learning in both supervised learning and transfer learning tasks, achieving improved accuracy.
ACM TRANSACTIONS ON INTELLIGENT SYSTEMS AND TECHNOLOGY
(2021)
Article
Engineering, Civil
Mohammad Sadegh Barkhordari et al.
Summary: The research utilizes a hybrid technique of artificial neural network (ANN) and Simulated Annealing (SA) to predict the response of reinforced concrete shear walls. By comparing 14 learning algorithms, the best ANN model shows high capability in predicting the responses of RC shear walls.
Article
Engineering, Multidisciplinary
Mohammad Khorshidi Paji et al.
Summary: The study explores the impact of fresh and salty water on concrete compressive strength and proposes two hybrid artificial intelligence models to predict concrete compressive strength with high accuracy. Various parameters were found to significantly affect compressive strength, and the neuro-swarm intelligence model outperformed the neuro-imperialism model in predicting concrete compressive strength values.
Article
Chemistry, Physical
Furqan Farooq et al.
Summary: Artificial intelligence and machine learning techniques were utilized to predict the strength of self-compacting concrete incorporating waste material. Various models including artificial neural network, support vector machine, and gene expression programming were employed based on input variables proportion. The study showed that the proposed machine learning models achieved high accuracy in predicting mechanical properties of SCC.
Article
Materials Science, Multidisciplinary
Hai-Van Thi Mai et al.
Summary: The study successfully predicted the compressive strength of concrete using artificial neuron network (ANN) and Monte Carlo simulations. The best-defined ANN structure was determined to be [8-24-1] with 24 neurons in the hidden layer. It was found that the age of sample and cement content are the two most crucial factors affecting the compressive strength of concrete using BFS and FA.
ADVANCES IN MATERIALS SCIENCE AND ENGINEERING
(2021)
Article
Construction & Building Technology
Ayaz Ahmad et al.
Summary: The utilization of recycled coarse aggregate in concrete is an effective way to reduce environmental pollution, but the presence of adhered mortar on its surface affects its properties. A suitable mix design can enable the coarse aggregate to achieve the desired strength and be used in various construction projects. Employing supervised machine learning algorithms, gene expression programming, and artificial neural network can effectively predict the compressive strength of concrete.
Article
Chemistry, Multidisciplinary
Danial Jahed Armaghani et al.
APPLIED SCIENCES-BASEL
(2020)
Article
Chemistry, Multidisciplinary
Ali Ashrafian et al.
APPLIED SCIENCES-BASEL
(2020)
Article
Construction & Building Technology
Khoa Tan Nguyen et al.
CONSTRUCTION AND BUILDING MATERIALS
(2020)
Article
Chemistry, Multidisciplinary
An Thao Huynh et al.
APPLIED SCIENCES-BASEL
(2020)
Review
Engineering, Civil
Sam Joel
CIVIL ENGINEERING JOURNAL-TEHRAN
(2020)
Article
Construction & Building Technology
Manolis Georgioudakis et al.
FRONTIERS IN BUILT ENVIRONMENT
(2020)
Article
Chemistry, Physical
Dong Van Dao et al.
Article
Construction & Building Technology
Hosein Naderpour et al.
JOURNAL OF BUILDING ENGINEERING
(2018)
