The contribution of artificial intelligence to phase change materials in thermal energy storage: From prediction to optimization

S Liu, J Han, Y Shen, SY Khan, W Ji, H Jin, M Kumar - Renewable Energy, 2024 - Elsevier
Artificial Intelligence (AI) is leading the charge in revolutionizing research methodologies
within the field of latent heat storage (LHS) by using phase change materials (PCMs) and …

Chloride transport and intelligent repair processes in microencapsulated self-healing concrete: A review

H Zhu, Z Hu, K He, H Yang, D Kong, R Pan - Journal of Building …, 2024 - Elsevier
Chloride ingress and the resulting steel corrosion are primary contributors to deteriorating
concrete durability. Self-healing technology is promising for enhancing concrete durability …

[HTML][HTML] Machine learning-guided optimization of coarse aggregate mix proportion based on CO2 intensity index

Y Liu, J Zhang, S Zhang, AA Zhang, J Peng… - Journal of CO2 …, 2024 - Elsevier
Abstract Aggregate accounts for 60‐80% volume fraction of concrete, which has a great
influence on the CO 2 emission and performance of concrete. Apart from natural coarse …

Elucidating the evolution of pore structure, microstructural damage, and micromechanical response in cement pastes containing microencapsulated phase change …

R Paswan, S Das - Cement and Concrete Composites, 2024 - Elsevier
As climate variability intensifies the frequency and severity of freeze-thaw cycles, the
development of cementitious materials capable of withstanding these harsh conditions …

Integrating data imputation and augmentation with interpretable machine learning for efficient strength prediction of fly ash-based alkali-activated concretes

N Miyan, NMA Krishnan, S Das - Journal of Building Engineering, 2024 - Elsevier
Fly ash-based alkali-activated concrete (AAC) is renowned for its superior mechanical
performance and sustainability, presenting an attractive alternative to traditional Portland …

Meso-structural degradation and mechanical property evolution in cementitious mortars containing microencapsulated phase change materials under extended freeze …

R Paswan, S Das - Construction and Building Materials, 2024 - Elsevier
This paper explores the influence of incorporating microencapsulated Phase Change
Materials (MPCM) on the evolution of both mechanical behavior and meso-structural …

Data driven multi-objective design for low-carbon self-compacting concrete considering durability

B Cheng, L Mei, WJ Long, Q Luo, J Zhang… - Journal of Cleaner …, 2024 - Elsevier
Abstract Self-Compacting Concrete (SCC) offers remarkable benefits in modern
engineering. However, traditional SCC design faces challenges, necessitating a reduction in …

[HTML][HTML] Computationally effective machine learning approach for modular thermal energy storage design

D Singh, T Rugamba, H Katara, KS Grewal - Applied Energy, 2025 - Elsevier
This research presents an innovative approach that integrates computational fluid dynamics
(CFD) and machine learning (ML) for the design and optimization of thermal energy storage …

Part-scale microstructure prediction for laser powder bed fusion Ti-6Al-4V using a hybrid mechanistic and machine learning model

BC Whitney, AG Spangenberger, TM Rodgers… - Additive …, 2024 - Elsevier
Laser powder bed fusion (LPBF) Ti-6Al-4V is widely studied for use in structural applications
in aerospace and medical industries, but mechanical anisotropy and microstructural …

[HTML][HTML] Explainable artificial intelligence framework for FRP composites design

M Yossef, M Noureldin, A Alqabbany - Composite Structures, 2024 - Elsevier
Fiber-reinforced polymer (FRP) materials are integral to various industries, from automotive
and aerospace to infrastructure and construction. While FRP composite design guidelines …