Article
Computer Science, Artificial Intelligence
Sarat Chandra Nayak, Subhranginee Das, Bijan Bihari Misra, Sung-Bae Cho
Summary: This article explores the estimation problem of compressive strength (CS) of concrete and proposes a forecasting method based on random vector functional link network (RVFLN). Comparative analysis shows that RVFLN outperforms other models in terms of performance. The study reveals that RVFLN has high capability in modeling and predicting CS data.
Article
Construction & Building Technology
Hany A. Dahish, M. S. Alfawzan, Bassam A. Tayeh, Maha A. Abusogi, Mudthir Bakri
Summary: This study investigates the impact of incorporating natural pozzolan (NP) and silica fume (SF) in cement-based mortars on the compressive strength. NP can replace up to 40% of the weight of cement or volume of sand in cement mortars. Addition of SF at 5% and 10% replacement levels improves the mechanical properties of NP-based cement mortars.
CASE STUDIES IN CONSTRUCTION MATERIALS
(2023)
Article
Construction & Building Technology
Alaa M. Morsy, Abd Elmoaty M. B. Abd Elmoaty, Abdelrhman B. Harraz
Summary: This paper developed an artificial neural network (ANN) model to predict the mechanical properties of engineered cementitious composites (ECC). The model showed outstanding predictive performance with accuracy near 100%. Additional evaluation using experimental data confirmed the accuracy of the model.
CASE STUDIES IN CONSTRUCTION MATERIALS
(2022)
Article
Chemistry, Multidisciplinary
Sina Rezvan, Mohammad Javad Moradi, Hamed Dabiri, Kambiz Daneshvar, Moses Karakouzian, Visar Farhangi
Summary: One practical solution to address the environmental impacts of plastic bottle waste is to incorporate them into concrete, a commonly used material in construction industry. Studies have shown that using polyethylene terephthalate (PET) fiber as reinforcement in concrete can significantly enhance its compressive strength. This research utilized a machine learning approach to investigate the benefits of PET fiber in concrete, providing guidance for engineers and stakeholders to utilize these recycled materials.
APPLIED SCIENCES-BASEL
(2023)
Article
Green & Sustainable Science & Technology
Mohammad Mehdi Roshani, Seyed Hamidreza Kargar, Visar Farhangi, Moses Karakouzian
Summary: Fly ash, as a supplementary pozzolanic material, can reduce the environmental impact of concrete by decreasing CO2 emissions. A novel method using artificial neural networks has been used to successfully predict the mechanical characteristics of concrete with added Fly ash.
Article
Computer Science, Artificial Intelligence
Panagiotis G. Asteris, Liborio Cavaleri, Hai-Bang Ly, Binh Thai Pham
Summary: This study investigates the use of artificial intelligence algorithms to predict the compressive strength of mortars, showing that AI techniques are able to reliably approximate the strength of mortars.
Article
Multidisciplinary Sciences
Muhammad Nasir, Uneb Gazder, Muhammad Umar Khan, Mehboob Rasul, Mohammed Maslehuddin, Omar S. Baghabra Al-Amoudi
Summary: This research investigates the effects of hot climatic conditions on the long-term strength development of concrete and the influence of casting temperature, curing regimes, and pozzolanic materials. Prediction models were developed using quadratic regression models and artificial neural networks (ANNs) to accurately predict the compressive strength of concrete. The study suggests that the ANN model can be applied for designing concrete of higher strengths under hot weather conditions.
ARABIAN JOURNAL FOR SCIENCE AND ENGINEERING
(2022)
Article
Mathematics, Interdisciplinary Applications
Ghodrat Rahchamani, Seyed Mojtaba Movahedifar, Amin Honarbakhsh
Summary: This paper proposes an automated approach based on machine learning strategies to predict the compressive strength of concrete. By analyzing experimental data, the proposed method accurately predicts the quality of concrete and the amount of raw materials used and achieves the best quality concrete with an error rate of less than 5%.
Article
Construction & Building Technology
Ketholyn J. Bespalhuk, Tiego J. C. de Oliveira, Joao V. P. Valverde, Rhudyeris A. Goncalves, Lucas Ferreira-Neto, Paula C. S. Souto, Josmary R. Silva, Nara C. de Souza
Summary: Cement is a commonly used construction material due to its cohesive properties, low cost, versatility, and moldability. The microstructure and properties of cementitious materials are affected by curing conditions and time. In this study, cylindrical specimens were fabricated to investigate the effect of curing conditions on the morphology and compressive strength of hardened pastes. The results showed that with increased curing time, the fractal dimensions of the hydrated cement increased, leading to an increase in compressive strength.
CONSTRUCTION AND BUILDING MATERIALS
(2023)
Article
Construction & Building Technology
Amir Ali Shahmansouri, Maziar Yazdani, Mehdi Hosseini, Habib Akbarzadeh Bengar, Hamid Farrokh Ghatte
Summary: The study demonstrates the potential of using artificial neural networks to predict the compressive strength and electrical resistivity of natural zeolitic concrete, significantly speeding up the process and improving accuracy. Experimental results from 324 different designs of natural zeolitic concrete specimens validate the accuracy and reliability of the model. This has important implications for cost reduction and time saving.
CONSTRUCTION AND BUILDING MATERIALS
(2022)
Article
Construction & Building Technology
Nzar Shakr Piro, Ahmed Salih Mohammed, Samir M. Hamad
Summary: This study establishes mathematical models for predicting the compressive strength and electrical resistivity of concrete with steel slag aggregate substitution by collecting and analyzing a large amount of literature data. The artificial neural network model performs the best in predicting both properties, with high coefficients of determination and small root mean square errors.
CONSTRUCTION AND BUILDING MATERIALS
(2022)
Article
Construction & Building Technology
N. Shanmugasundaram, S. Praveenkumar, K. Gayathiri, S. Divya
Summary: This study uses Artificial Neural Network (ANN) to predict the compressive strength of Engineered Cementitious Composite (ECC) by considering the mix proportions and physical properties of polyvinyl alcohol fibers (PVA). The results show that this method can effectively predict the performance of ECC.
CONSTRUCTION AND BUILDING MATERIALS
(2022)
Article
Construction & Building Technology
Salisa Chaiyaput, Nakib Arwaedo, Namthip Kingnoi, Trong Nghia-Nguyen, Jiratchaya Ayawanna
Summary: This experimental study evaluated the compressive strength of soil-cement samples under different curing conditions, revealing that the compressive strength of soft clay-cement samples was slightly higher than that of ball clay-cement samples, with the highest strength observed in samples cured with lime-saturated water.
CASE STUDIES IN CONSTRUCTION MATERIALS
(2022)
Article
Construction & Building Technology
Sanaz Ramzi, Mohammad Javad Moradi, Hamzeh Hajiloo
Summary: In this study, an artificial neural network (ANN) model was developed to predict the compressive strength of concrete containing supplementary cementitious materials (SCMs) at high temperatures. The model considered several parameters including aggregate types, SCM content, water-to-binder ratio, and temperature levels. Siliceous aggregates were found to have stronger bonds with cement paste compared to calcareous aggregates. The optimal SCM content for siliceous and calcareous concrete at high temperatures was determined to be 8% and 3% of silica fume (SF), respectively. The ANN model provided valuable insights into the behavior of concrete at high temperatures.
Article
Chemistry, Physical
Mouhamadou Amar, Mahfoud Benzerzour, Rachid Zentar, Nor-Edine Abriak
Summary: In this study, an artificial neural network (ANN) was used to forecast the compressive strength of waste-based concretes. By gathering data from various sources and training the model, the ANN showed a strong capacity for predicting the strength with high accuracy.
Article
Materials Science, Composites
Ali Payidar Akgungor, Ozer Sevim, Ilker Kalkan, Ilhami Demir
SCIENCE AND ENGINEERING OF COMPOSITE MATERIALS
(2018)
Article
Construction & Building Technology
Ilhami Demir, Ozer Sevim, Emrah Tekin
CONSTRUCTION AND BUILDING MATERIALS
(2018)
Article
Computer Science, Artificial Intelligence
Abdulkadir Karaci
NEURAL COMPUTING & APPLICATIONS
(2019)
Article
Engineering, Multidisciplinary
Ilhami Demir, Ozer Sevim, Ilker Kalkan
SADHANA-ACADEMY PROCEEDINGS IN ENGINEERING SCIENCES
(2018)
Article
Engineering, Multidisciplinary
Abdulkadir Karaci, Hasbi Yaprak, Osman Ozkaraca, Ilhami Demir, Osman Simsek
CMES-COMPUTER MODELING IN ENGINEERING & SCIENCES
(2019)
Article
Construction & Building Technology
Ozer Sevim, Ilhami Demir
CONSTRUCTION AND BUILDING MATERIALS
(2019)
Article
Construction & Building Technology
Ozer Sevim, Ilhami Demir
CEMENT & CONCRETE COMPOSITES
(2019)
Article
Computer Science, Artificial Intelligence
Abdulkadir Karaci
Summary: This study successfully classified COVID-19 with high precision using pre-trained VGG19 deep CNN architecture and the YOLOv3 detection algorithm, providing a pre-diagnostic system for healthcare centers. The proposed Cascade VGGCOV19-NET model outperforms other models and previous studies in COVID-19 detection, contributing to the literature on both YOLO-aided deep architecture and classification success.
NEURAL COMPUTING & APPLICATIONS
(2022)
Article
Construction & Building Technology
Ismail Raci Bayer, Ozer Sevim, Ilhami Demir
Summary: The demand for cement is increasing globally, leading to the depletion of natural resources. This study focuses on the use of marble waste as a replacement for cement and compares the effects of different cooling regimes on cementitious composites exposed to high temperatures. The results show that marble powder replacement can enhance the resistance of cementitious composites to high temperatures, leading to potential savings in cement usage and reductions in CO2 emissions.
Article
Construction & Building Technology
Ozer Sevim, Ilhami Demir, Erdinc H. Alakara, Selahattin Guzelkucuk, Ismail Raci Bayer
Summary: The physicochemical structure of the mixing water used in concrete significantly affects the properties of cementitious composites. Studies on the impact of magnetized water (MW) on FA/BFS-based cementitious composites are still in the early stage. This research investigates the influence of MW on the fresh and hardened properties of fly ash (FA)/blast furnace slag (BFS)-based cementitious composites. A total of 22 different mixture groups were produced using tap water (TW) and MW, with varying levels of FA/BFS. The results show that the properties of cementitious composite samples produced with MW are significantly improved, and it is recommended to use up to 25% FA/BFS in cementitious composites prepared with MW.
Article
Engineering, Multidisciplinary
Kemal Akyol, Abdulkadir Karaci, Muhammet Emin Titikci
Summary: Care4HIP is an embedded system solution designed to help drivers notice hearing-impaired people in traffic. The system calculates the locational information and direction of these people relative to the vehicle, using server and mobile device applications.
JOURNAL OF POLYTECHNIC-POLITEKNIK DERGISI
(2022)
Article
Computer Science, Theory & Methods
Abdulkadir Karaci, Kemal Akyol, Mehmet Ugur Turut
Summary: The study developed a machine learning-based system for real-time recognition of Turkish sign language, achieved high accuracy rates by extracting handcrafted features and applying cascade voting approach.
APPLIED COMPUTER SYSTEMS
(2021)
Article
Computer Science, Theory & Methods
Berk Ercin, Abdulkadir Karaci
Summary: The study proposes a machine learning system for real-time identification using gait features, achieving high classification accuracy through a voting approach combining the outputs of multiple classifiers. The method shows potential in identification using gait features.
APPLIED COMPUTER SYSTEMS
(2021)
Proceedings Paper
Computer Science, Artificial Intelligence
Abdulkadir Karaci
ARTIFICIAL INTELLIGENCE AND APPLIED MATHEMATICS IN ENGINEERING PROBLEMS
(2020)
Article
Engineering, Multidisciplinary
Ilhami Demir, Selahattin Guzelkucuk, Ozer Sevim
ENGINEERING SCIENCE AND TECHNOLOGY-AN INTERNATIONAL JOURNAL-JESTECH
(2018)