CORTEXA
← Browse
crossrefBuildings2025-08-25Cited by 5

Advanced Hybrid Modeling of Cementitious Composites Using Machine Learning and Finite Element Analysis Based on the CDP Model

Elif Ağcakoca, Sebghatullah Jueyendah, Zeynep Yaman, Yusuf Sümer, Mahyar Maali

This study aims to investigate the mechanical behavior of cement mortar and concrete through a hybrid approach that integrates artificial intelligence (AI) techniques with finite element modeling (FEM). Support Vector Machine (SVM) models with Radial Basis Function (RBF) and polynomial kernels, along with Multilayer Perceptron (MLP) neural networks, were employed to predict the compressive strength (Fc) and flexural strength (Fs) of cement mortar incorporating nano-silica (NS) and micro-silica (MS). The dataset comprises 89 samples characterized by six input parameters: water-to-cement ratio (W/C), sand-to-cement ratio (S/C), nano-silica-to-cement ratio (NS/C), micro-silica-to-cement ratio (MS/C), and curing age. Simultaneously, the axial compressive behavior of C20-grade concrete was numerically simulated using the Concrete Damage Plasticity (CDP) model in ABAQUS, with stress–strain responses benchmarked against the analytical models proposed by Mander, Hognestad, and Kent–Park. Due to the inherent limitations of the finite element software, it was not possible to define material models incorporating NS and MS; therefore, the simulations were conducted using the mechanical properties of conventional concrete. The SVM-RBF model demonstrated the highest predictive accuracy with RMSE values of 0.163 (R2 = 0.993) for Fs and 0.422 (R2 = 0.999) for Fc, while the Mander model showed the best agreement with experimental results among the FEM approaches. The study demonstrates that both the SVM-RBF and CDP-based modeling approaches serve as robust and complementary tools for accurately predicting the mechanical performance of cementitious composites. Furthermore, this research addresses the limitations of conventional FEM in capturing the effects of NS and MS, as well as the existing gap in integrated AI-FEM frameworks for blended cement mortars.

View free PDFSource page

Related papers

crossrefBuildings2024-12-20Cited by 7

Advanced Ensemble Machine-Learning Models for Predicting Splitting Tensile Strength in Silica Fume-Modified Concrete

Nadia Moneem Al-Abdaly, Mohammed E. Seno, Mustafa A. Thwaini, Hamza Imran, Krzysztof Adam Ostrowski, Kazimierz Furtak

The splitting tensile strength of concrete is crucial for structural integrity, as tensile stresses from load and environmental changes often lead to cracking. This study investigates the effectiveness of advanced ensemble machine-learning models, including LightGBM, GBRT, XGBoos…

View free PDFSource page
crossrefBuildings2025-04-11Cited by 2

Prediction of Shear Strength of Steel Fiber-Reinforced Concrete Beams with Stirrups Using Hybrid Machine Learning and Deep Learning Models

B. R. Kavya, A. S. Shrikanth, K. S. Sreekeshava

The shear behavior of beams cast with steel fiber reinforced concrete and provided with stirrups is a complex phenomenon that depends on various factors. In the present research effort, a hybrid support vector regression model combined with a particle swarm optimization algorithm…

View free PDFSource page
crossrefBuildings2024-06-20Cited by 1

Analyzing Land Shape Typologies in South Korean Apartment Complexes Using Machine Learning and Deep Learning Techniques

Sung-Bin Yoon, Sung-Eun Hwang

In South Korea, the configuration of land parcels within apartment complexes plays a pivotal role in optimizing land use and facility placement. Given the significant impact of land shape on architectural and urban planning outcomes, its analysis is essential. However, studies on…

View free PDFSource page
crossrefBuildings2024-06-14Cited by 10

Feasibility of Advanced Reflective Cracking Prediction and Detection for Pavement Management Systems Using Machine Learning and Image Detection

Sung-Pil Shin, Kyungnam Kim, Tri Ho Minh Le

This research manuscript presents a comprehensive investigation into the prediction and detection of reflective cracking in pavement infrastructure through a combination of machine learning approaches and advanced image detection techniques. Leveraging machine learning algorithms…

View free PDFSource page
crossrefBuildings2026-07-15

Temperature-Induced Error Compensation in Computer Vision-Based Displacement Measurement Using Deep Learning-Based Time Series Forecasting Model

Xiaoyan Liu, Cheng Zeng, Feng Li, Yongding Tian

Computer vision technology has emerged as a promising approach for multipoint displacement monitoring of civil infrastructure, owing to its inherent noncontact operation and remote measurement capabilities. However, its measurement accuracy is greatly affected by ambient temperat…

View free PDFSource page