In this study, the shear behaviour of reinforced concrete (RC) beams that were retrofitted using precast panels of ultra-high performance fiber reinforced concrete (UHPFRC) is presented. The precast UHPFRC panels were glued to the side surfaces of RC beams using epoxy adhesive in two different configurations: (i) retrofitting two sides, and (ii) retrofitting three sides. Experimental tests on the adhesive bond were conducted to estimate the bond capacity between the UHPFRC and normal concrete. All the specimens were tested in shear under varying levels of shear span-to-depth ratio (a/d=1.0; 1.5). For both types of configuration, the retrofitted specimens exhibited a significant improvement in terms of stiffness, load carrying capacity and failure mode. In addition, the UHPFRC retrofitting panels glued in three-sides shifted the failure from brittle shear to a more ductile flexural failure with enhancing the shear capacity up to 70%. This was more noticeable in beams that were tested with a/d=1.5. An approach for the approximation of the failure capacity of the retrofitted RC beams was evolved using a multi-level regression of the data obtained from the experimental work. The predicted values of strength have been validated by comparing them with the available test data. In addition, a 3-D finite element model (FEM) was developed to estimate the failure load and overall behaviour of the retrofitted beams. The FEM of the retrofitted beams was conducted using the non-linear finite element software ABAQUS.
In this paper, experimental, numerical and analytical investigations were carried out to study the structural behavior of concrete-filled stainless steel tubular (CFSST) stub columns externally wrapped by carbon fiber reinforced polymer (CFRP) composites under eccentric compression loading. In the experimental work, twelve stub columns of 101 mm outer diameter and 2 mm thickness were tested. The main variables considered were the thickness of the CFRP wrap (tf ) and the load eccentricity to the outer diameter ratio (e/D). A 3D finite element (FE) simulation was developed for the CFRP-bonded CFSST stub columns using the well-known commercial FE program ABAQUS and validated against the experimental results. The validated FE models were further utilized to generate more data with different variables. From the experimental and numerical results, it was found that the CFRP wrapping effectively improves the ultimate strength of the CFRP-bonded CFSST stub columns. Finally, an analytical axial force-bending moment (P-M) interaction model was proposed. It provided conservative predictions when compared to the experimental and FE results.
This work explores the bending responses of functionally graded graphene platelet-reinforced ceramic–metal (FG-GPLRCM) plates on Kerr substrates with
First Name is required invalid characters Last Name is required invalid characters Email Address is required Invalid Email Address Invalid Email Address
No abstract is provided for this article.
Hydrated cement is one of the complex composite systems due to the presence of multi-scale phases with varying morphologies. Calcium silicate hydrate, which is the principal binder phase in the hydrated cement, is responsible for the stiffness, strength, and durability of Portland cement concrete. To understand the mechanical and durability behavior of concrete, it is important to investigate the interactions of hydrated cement phases with other materials at the nanoscale. In this regard, the molecular simulation of cement-based materials is an effective approach to study the properties and interactions of the cement system at the fundamental scale. Recently, many studies have been published regarding atomistic simulations to investigate the cement phases to define/explain the microscopic physical and chemical properties, thereby improving the macroscopic performance of hardened binders. The research in molecular simulation of cementitious systems involves researchers with multidisciplinary backgrounds, mainly in two areas: ① cement chemistry, where the hydration reactions govern most of the chemical and physical properties at the atomic scale; and ② computational materials science and engineering, where the bottom-up approach is required. The latter approach is still in its infancy, and as such, a study of the prevailing knowledge is useful, namely through an exhaustive literature review. This state-of-the-art report provides a comprehensive survey on studies that were conducted in this area and cites the important findings.
This paper presents a study on evaluation of seismic performance of shear-deficient beam column joints (BCJs) strengthened by ultra-high performance fiber reinforced concrete (UHPFRC). Normal concrete BCJs having deficiencies in resisting the seismic action were cast, strengthened with a thin layer of UHPFRC, and tested under seismic loading. Two different methods were used for strengthening the normal concrete BCJ specimens consisted of: i) sandblasting the normal concrete substrate surface of BCJs and in-situ casting of a 30 mm thick UHPFRC jacket and ii) bonding 30 mm thick prefabricated UHPFRC plates to seismically deficient BCJ using epoxy resins and special fillers. The performance of UHPFRC jacketing in strengthening the seismically deficient BCJs was experimentally evaluated under reverse cyclic loading using displacement control approach keeping column axial load constant at 150 kN. The analysis of test results showed that the first method of strengthening was highly effective in terms of shear capacity, deformation capacity, stiffness characteristics and energy dissipation capacity, as compared to the second method.
Accurate and timely lithium-ion battery discharge capacity prediction is vital for ensuring the safety, reliability, and performance of electric vehicles and grid energy storage systems. However, the aging mechanism of batteries is highly dynamic and complex, posing significant challenges for effective modeling. This paper presents a novel interpretable machine-learning approach that stands out by predicting discharge capacity under variable operating conditions using minimal input descriptors. Three nature-inspired hybrid machine learning algorithms were developed: Quantum-inspired Particle Swarm Optimization-Adaboost (QIPSO-ADB), Harris Hawks Optimization- Extreme Gradient Boosting Machine (HHO-GBM), and Sparrow Search Algorithm-Light Gradient Boosting Machine (SSA-LGBM). We demonstrate that discharge capacity across cycles can be accurately predicted using only temperature and cycle number, thus simplifying model inputs while maintaining high accuracy. Two model series were evaluated: Combo1 (C1), incorporating all input descriptors, and Combo2 (C2), using only temperature and cycle number. All three hybrid models demonstrated strong predictive performance under variable conditions. Notably, the HHO-GBM-C1 model achieved the highest prediction accuracy, with a mean absolute error (MAE) of 0.0816 and a correlation coefficient of 95.2 % during the testing phase. For the reduced-descriptor series, HHO-GBM-C2 achieved a low MAE of 0.1438 and a correlation coefficient of 85.99 %. Validation was performed using multiple samples from eVTOL and MIT public datasets, confirming the robustness and generalizability of the models across both variable and fixed operating conditions. These findings provide strategic insights for optimizing battery performance, contributing significantly to the development of reliable electric vehicles and sustainable energy storage solutions.
The use of corrosion inhibitors for steel rebar in concrete is one of the best-demonstrated methods to mitigate the corrosion process and extend the service life of reinforced concrete structures exposed to chloride laden environment. However, the research progress on corrosion inhibitors for reinforcing steel is slow and classical, as the traditional experimental methods revealed. Recently, the application of computational methods, such as density functional theory (DFT) and molecular dynamics (MD) simulation, to corrosion research has become an essential area for gaining more insights into the detailed mechanisms of corrosion as well as identifying ways and means of technological innovation. This paper presents a systematic review of the research progress and advances in the molecular simulation of corrosion inhibitors for steel in concrete. The DFT calculations conducted on different inhibitor molecules were reviewed, and the related theoretical chemical reactivity parameters along with the donor-acceptor process were discussed. In addition, the MD simulations of various molecule/surface adsorption systems were studied, considering different steel substrates. The valuable insights gained from the computational models were introduced and consolidated into the experimental inhibition efficiency. Finally, the challenges and future perspectives of the research topic are highlighted. This paper is believed to promote the utilization of theoretical methods in corrosion research of reinforcing steel for a deep understanding of the corrosion process at the atomic level and innovative design of corrosion-resistance materials.
