Structural Engineering Applications Research Articles

Enhancing bond strength prediction at UHPC-NC interface: A data-driven approach with augmentation and explainability

Existing concrete structures often have difficulty reaching their design service life due to aging, increased loads, and natural disasters, and they need to be repaired and strengthened or replaced. Ultra-high-performance concrete (UHPC), known for its ultra-high strength and excellent toughness, offers a promising solution for repairing and strengthening normal concrete (NC) structures. It is expected to be a viable solution when applied to the repair and strengthening of NC structures. A reliable interfacial bonding between the two materials (NC substrate and UHPC) determines the overall performance of this composite structure. In this study, a data-driven approach is proposed to predict the bond performance at the UHPC-NC interface as accurately as possible. To overcome the problem of limited experimental data, a data augmentation model is introduced, combining kernel density estimation (KDE) and tabular generative adversarial networks (TGAN). The optimal model is determined from six decision tree-based ensemble learning models applied to two strategies: "synthetic training - real testing" and "real training - real testing." Finally, a parametric analysis is performed using SHapley Additive exPlanations (SHAP) to elucidate the importance and sensitivity of different features related to bond strength. The findings illustrate that the suggested KDE-TGAN model effectively captures the distribution of the original dataset, enhancing both the accuracy and robustness of the bond strength prediction models. Furthermore, the model's ability to explain the importance and sensitivity of different features provides valuable insights into bond strength prediction. Thus, the proposed data augmentation model provides a reliable approach for modeling experimental data with small samples in structural engineering applications.

Investigation of scalable chiral metamaterial beams for combined stiffness and supplemental energy dissipation

Abstract Metamaterials have gained important interest in the research community attributable to advances in additive manufacturing enabling their fabrication at reasonable costs. The vast majority of their applications and demonstrations are at micro- and nano-scales, and challenges remained regarding the larger scale applications. In this paper, we are interested by the scalability of metamaterials, targeting structural engineering applications. To do so, we explore mechanisms capable of providing both bending stiffness and high-performance energy dissipation. Our study includes beams constructed with chiral topologies of different structural hierarchy orders, and we also explore three new topologies that we termed chiral friction, chiral-rectangular and chiral-hexagonal design to engineer the beams and the use of friction rods with tunable post-stress that inserted longitudinally through the beams to provide enhanced friction. The mechanical performance of the metamaterial beams is characterized through a series three-point bending tests. Of interest is to evaluate the bending stiffness, shape recoverability, and energy dissipation capabilities. We find that the chiral-hexagonal topology equipped with a non-stressed friction rod exhibit excellent energy dissipation capabilities, showing an improved loss factor by 11.9 times compared to the control beam using 68% of its materials density. Moreover, the use of the post-stress mechanism shows that it is possible to augment both its shape recovery and bending stiffness up to 99.3% and 47.1%, respectively. Overall, our investigation shows that it is possible to engineer scalable metamaterial beams targeting structural engineering applications, and that the use of topology optimization and strategically designed post-tensioning mechanism can allow tuning of mechanical performance.

Performance of self‐compacting alkali‐activated slag concrete‐filled cold‐formed steel tubular stub columns

AbstractThis study comprehensively investigates the self‐compacting alkali‐activated slag concrete‐filled cold‐formed steel tubes (SACFST) stub columns under axial compression. The innovative combination of alkali‐activated slag concrete (AASC) with concrete‐filled steel tubes (CFST) enhances both sustainability and structural performance. AASC, known for its eco‐friendly benefits compared to conventional concrete, was developed using industrial wastes, contributing to green construction practices. Based on Taguchi's L‐9 orthogonal array, nine mixes of self‐compacting alkali‐activated slag concrete (SASC) were developed, achieving impressive flowability exceeding 700 mm and compressive strengths ranging from 48 to 69 MPa. Additionally, split‐tensile strengths between 3.8 and 5.7 MPa, flexural strengths from 6.3 to 8.2 MPa, and a modulus of elasticity between 27.6 and 32.9 GPa were observed. Nine circular CFSTs containing SASC mixes were tested under axial compression, and a finite element model was developed to simulate their results. The experimental results were compared with standards from ACI 318, AIJ‐97, AISC‐360, AS 5100, and EC‐4, to evaluate the accuracy of axial capacity predictions. Among these standards, EC‐4 and AS 5100 provided the closest estimates, with mean PTest/PEC4 and PTest/P5100 ratios of 1.03 and 1.04, respectively, indicating a high level of accuracy. These findings highlight the potential of SACFST in advancing sustainable and resilient construction practices, laying the groundwork for future applications in structural engineering.

Testing and evaluation of PVCC nano layered reinforced concrete T-beam: Experimental study

This study examines the performance of reinforced concrete T-beams strengthened with PVCC nano layering and basalt fiber fabric wrapping. TP3, a PVCC nano-layered specimen with 1.2% PVA fiber, and TB2, a basalt fiber fabric-wrapped beam, outperform the other specimens. TP3 has a first fracture load of 112 kN and a maximum ultimate load of 165 kN, with 1.66 times the ductility and 1.51 times the stiffness of the control beam (T0). TP3 also has 1.61 times more energy absorption and the highest energy index, 1.46 times that of T0. TB2 can withstand a maximum ultimate load of 185 kN and has higher ductility, stiffness, energy absorption, and energy index than T0. The experimental results are validated by finite element analysis, which provides useful insights into strengthening procedures in structural engineering applications.

Enhancing mechanism of mechanical properties of lightweight and high-strength concrete prepared with autoclaved silicate lightweight aggregate

Lightweight aggregate concrete (LAC) possesses the merits of low density, excellent thermal insulation performance and outstanding seismic performance. However, the practical application in structural engineering is limited because its mechanical properties are weaker than those of ordinary aggregate concrete. It was found that the compressive strength of high-strength concrete prepared by autoclaved silicate lightweight aggregate (cloud concrete stone) was higher than that of ordinary aggregate high-strength concrete, and it showed significant lightweight and high-strength characteristics. To reveal the enhancing mechanism of the mechanical properties of high-strength concrete prepared by cloud concrete stone (CCS), this research examines the internal curing effect of CCS and the mechanical performances of the interfacial transition zones (ITZ). The influences of the elastic modulus of CCS, the shape of aggregate and fracture patterns on the mechanical properties of concrete are simulated via the discrete element method. The findings suggest that the internal curing effect of CCS delays the onset of capillary stress and mitigates autogenous shrinkage. The homogeneousness, elastic modulus, and fracture toughness of ITZ around CCS aggregate are superior than that ITZ around ordinary gravel in high-strength concrete. Furthermore, the small difference in elastic modulus between CCS aggregate and cement matrix is advantageous for preventing microcracks during compression tests. The round shape of the aggregate also mitigates the development of microcracks caused by the stress concentration effect. These three reasons contribute to the enhancement of mechanical properties in lightweight and high-strength concrete. The innovation of this research is that the intrinsic relationship between CCS aggregate and concrete’s mechanical properties is thoroughly revealed from the aspects of the characteristics of the interface transition zone, the morphology of the aggregate and the elastic modulus of aggregate. That provides a theoretical basis for the application of light aggregate in high-strength structural concrete.

High-modulus engineered cementitious composites: Design mechanism and performance characterization

Engineered cementitious composites features with high tensile performance while relatively weak compressive stiffness. This study endeavors to address the inherent trade-off between tensile properties and elastic modulus in conventional Engineered Cementitious Composites (ECC). Utilizing the distinctive characteristics of iron ore aggregates, known for their relatively stiff nature, smooth surface and rounded shape, an Iron Sand-based ECC (IS-ECC) emphasizing both high elastic modulus and ductility is formulated. Guided by micromechanical design theory and multiscale homogenization model, this study systematically explores the impacts of aggregate types, water-to-binder (w/b) ratios, sand-to-binder (s/b) ratios, and sand particle sizes on ECC properties. Compared with Quartz Sand-based ECC (QS-ECC), IS-ECC exhibits notably enhanced matrix fluidity and elastic modulus, reduced matrix toughness, and more robust strain-hardening behavior. The proposed three-level multiscale homogenization model accurately predicts the elastic modulus of ECC and provides insights into the underlying mechanism contributing to the enhanced elastic modulus of IS-ECC. With a resulting high elastic modulus of 33.3–48.6 GPa and superior tensile properties, IS-ECC holds promise for widespread applications in structural engineering.

Machine-learning assistant DFT study of half-metallic full-Heusler alloy N2CaNa: Structural, electronic, mechanical, and thermodynamics properties

We studied the structural, electronic, mechanical, and thermodynamic properties of N2CaNa full Heusler alloys using density functional theory (DFT). Results for the structural analysis establish structural stability with a minimum formation energy of 29.9 eV. The compound is brittle and mechanically stable, having checked out with the Pugh criteria. The B/G ratio of bulk modulus B to shear modulus G for N2CaNa is 4.766, hence the material is ductile. N2CaNa alloy is ductile in nature. The Debye model correctly predicts the low-temperature dependence of heat capacity, which is proportional to Debye’s T3 law. Just like the Einstein model, it also recovers the Dulong–Petit law at high temperatures, suggesting the thermodynamic stability of the compounds at moderate temperatures. The results demonstrate potential N2CaNa for applications in spintronics, structural engineering, and other fields requiring materials with tailored properties.

Sustainable development of agro bio-mass waste based AA6061-Tungsten carbide hybrid reinforced composites: A comprehensive performance investigation

The Recycling, Reusing, and Reduction (3R) strategy to solid waste management is proposed for exploiting the functionalities of wastes in enhancing base material performance, which could contribute to produce a sustainable product and environment. This study investigates the impact of organic and synthetic ceramic reinforcements; coconut shell ash (CSA), and tungsten carbide (WC) at various concentrations (1, 2, and 3 wt. % each) on microstructural and performance characteristics of sustainable AA6061 based hybrid composites. The composites are produced via the stir casting process with mechanized stirring. The microstructural characteristics of the produced aluminium hybrid composites (AMHC) are evaluated by FESEM/EDAX, FT-IR and XRD studies to examine their elemental composition, morphology and crystalline nature. The mechanical properties are investigated by charpy impact, tensile, compressive and micro-harness studies. The density and porosity analysis are employed to ascertain their physical characteristics. Morphology investigations show that the selected reinforcing materials are present and uniformly dispersed across the matrix's surface. The produced composite with 4 wt. % of CSA and WC reinforcements has the ability to improve the hardness (75.5 %), tensile strength (42.87 %), and compressive strength (55.71 %) of the base Aluminium alloy. Overall, using organic and synthetic ceramic substances as reinforcements for AA6061 alloy can result in sustainable high-performance composites for structural engineering applications across fields.

Synthesis and characterization of new continuous phosphate glass fibers intended for structural engineering applications: Structure/property relationships

Nowadays, phosphate glass fibers (PGF) are considered quite competitive respective to conventional glass fibers. This work focus on the development of PGF fibers intended for structural engineering applications such as composite reinforcement for building and automotive fields. For this purpose, two series of phosphate glasses based on 52P2O5-24CaO-13MgO-(11-(X+Y+Z)) K2O-XAL2O3-YF2O3-ZTiO2; (X=1; 3; 5, Y=0; 5, Z=0; 1 mol %) were processed and transformed into phosphate glass fibers by melt spinning. The resulting fibers were characterized. XRD analysis confirmed the non-crystalline nature of phosphate glasses. In addition, the substitution of K2O by Al2O3 and by the combination of Al2O3, Fe2O3 and TiO2 in the composition of phosphate glass could lead to a significant increase in fiber density from 2,16 g/cm3 to 2,80 g/cm3. The stability of the produced phosphate glass fibers was examined using two methods: weight loss at 37°C and dissolution kinetics under different pH levels (4, 7, and 12). The results showed that the chemical resistance of the PGF fibers was improved with up to 99% increase respective to the original formulation. In addition, the mechanical properties of the spinnable phosphate glass manufactured by replacing K2O oxide by Al2O3 oxide were improved, such a substitution led to a maximum tensile strength and modulus of 2668 MPa and 140 GPa, respectively. Therefore, the tensile proprieties were improved by 75% compared to the original formulation. This comparative study between phosphate glass fibers (PGF) and traditional fibers highlight similar tensile strength but combined to notable enhancements in chemical stability through cation addition, expanding their potential use in composite and biomedical materials. Finally, a correlation analysis of mechanical performances was carried out. It was observed that the results obtained using the statistical methods were consistent with the experimental data.

GFRP-Reinforced Concrete Columns: State-of-the-Art, Behavior, and Research Needs

This comprehensive review paper delves into the utilization of Glass Fiber-Reinforced Polymer (GFRP) composites within the realm of concrete column reinforcement, spotlighting the surge in structural engineering applications that leverage GFRP instead of traditional steel to circumvent the latter’s corrosion issues. Despite a significant corpus of research on GFRP-reinforced structural members, questions about their compression behavior persist, making it a focal area of this review. This study evaluates the properties of GFRP bars and their impact on the structural behavior of concrete columns, addressing variables such as concrete type and strength, cross-sectional geometry, slenderness ratio, and reinforcement specifics under varied loading protocols. With a dataset spanning over 250 publications from 1988 to 2024, our findings reveal a marked increase in research interest, particularly in regions like China, Canada, and the United States, highlighting GFRP’s potential as a cost-effective and durable alternative to steel. However, gaps in current knowledge, especially concerning Ultra-High-Performance Concrete (UHPC) reinforced with GFRP, underscore the necessity for targeted research. Additionally, the contribution of GFRP rebars to compressive column capacity ranges from 5% to 40%, but current design codes and standards underestimate this, necessitating new models and design provisions that accurately reflect GFRP’s compressive behavior. Moreover, this review identifies other critical areas for future exploration, including the influence of cross-sectional geometry on structural behavior, the application of GFRP in seismic resistance, and the evaluation of the size effect on column strength. Furthermore, the paper calls for advanced studies on the long-term durability of GFRP-reinforced structures under various environmental conditions, environmental and economic impacts of GFRP usage, and the potential of Artificial Intelligence (AI) and Machine Learning (ML) in predicting the performance of GFRP-reinforced columns. Addressing these research gaps is crucial for developing more resilient and sustainable concrete structures, particularly in seismic zones and harsh environmental conditions, and fostering advancements in structural engineering through the adoption of innovative, efficient construction practices.

Effect of graphene on microstructure, strain hardening and corrosion behaviour of dual phase Mg-Li matrix processed through liquid metallurgy route

The significance of this study lies in the potential enhancement of magnesium (Mg)-based materials through the addition of graphene, aiming to improve their mechanical properties and corrosion resistance. This research investigates the development of graphene-Mg composites, employing a liquid metallurgy route to fabricate a dual-phase structured Mg-8Li composite. Mg-Li dual-phase composites are of interest due to their lightweight nature and promising mechanical properties, making them suitable for applications in aerospace, automotive, and structural engineering. Graphene was incorporated into the Mg matrix at varying percentages (0.2, 0.4 and 0.6 wt.%), and the resulting materials were subjected to microstructural, mechanical, and corrosion analyses. Microstructural characterization results reveals that the increase in graphene content results in decrement in grain size of α-Mg and β-Li up to ∼33 and ∼11.5% respectively. Likewise, additions of graphene improvise tensile and yield strength of base matrix up to ∼47 and ∼44% respectively. Strain hardening exponent (n) values of base material decrease from 0.21 to 0.17 with respect to graphene addition which confirms the effect of graphene that acts as effective barrier to slip that decreases the deformation. Corrosion analysis revealed a decrease in corrosion rate and increased pitting resistance with the addition of graphene, indicating improved corrosion resistance. Electrochemical impedance spectroscopy results depict that the composite with 0.4 wt.% graphene exhibits larger semi conducting loop with higher charge transfer resistance ( Rct) value of 128 Ω cm2.

Applications of Nano Graphene Oxides in Prosthodontics and Implant Dentistry- Current Trends and Future Outlook

The world of Material Science has constantly evolved with newer materials being introduced constantly. One such material is Graphene which possessed excellent electrical, mechanical, thermal, optical, and biological properties due to which it has been used extensively in the fields of Optoelectronics, Energy harvesting, Films and Coatings, Water Filtration, Structural Engineering applications, Thermal management Devices and Sensors. Graphene-based Nanoparticles have also shown promising results in biomedicine, Tissue engineering scaffolds, Biomarker detectors, Biosensors, and Drug Delivery systems. Furthermore, extensive investigation is being performed on graphene-based Nanomaterials for their use in Dentistry, as it is shown to have promising results when incorporated into various Dental Restorative and Prosthetic Materials. This narrative review aims to give an overview of the application of graphene derivatives in dentistry, particularly on their application in Prosthodontics and Dental Implantology based on available research data and clinical studies. Further research is imperative to fully explore the potential of graphene to ensure its safe usage in dental practice.

On the Assessment of Reinforced Concrete (RC) Walls under Contact/Near-Contact Explosive Charges: A Deep Neural Network Approach

In recent years, the use of machine learning has been expanded to several fields, with promising advances in structural engineering applications. Deep neural network models have been implemented to predict the structural response of systems under conventional loading. Some of those neural network models are based on datasets containing images, test data, and/or data produced by using finite element models developed for a specific environment. While the accuracy of these models relies on the size and quality of the dataset, their use for blast analysis is rather limited, as publicly available data are scarce or restricted. Reinforced concrete (RC) walls or slabs under blast loading are commonly evaluated for flexural and shear behaviour, for which performance guidelines are widely available. While such response mechanisms are typically associated with the far-field range, the target response is controlled by local failure modes when blast loads are generated by contact or near-contact detonations. This paper introduces the implementation of a neural network model for the response prediction of RC walls subjected to contact and near-contact explosions. The model predicts the damage category (i.e., no damage, spall, and breach) associated with a given explosion scenario. The model is trained using experimental data from multiple test programmes available in open-source literature. It considers several parameters associated with the explosive charge (e.g., type, geometry, charge weight, and standoff) and RC target (e.g., material properties, geometry, and reinforcement). The model is able to accurately predict 81% of the total breached specimens, 66% of the total spalled specimens, and 71% of the full set of non-damaged specimens, with an overall accuracy of 72%, with precision and recall ranging from 60 to 76% and 66 to 81%, respectively. The current model is shown to be a significantly better predictor of the damage category than the semi-empirical approach outlined in UFC 3-340-02, making it a promising tool that can be improved with the inclusion of more experimental data.

Predicting the compressive strength of CFRP-confined concrete using deep learning

The application of externally bonded fiber-reinforced polymers (FRP) has revolutionized structural rehabilitation, offering cost-effective and rapid solutions for damaged structures. This study presents a novel deep learning approach for predicting the compressive strength of circular carbon-FRP-confined concrete columns. Leveraging an extensive database of 664 experimental results, the model incorporates monotonicity and smoothness constraints, with hyperparameters optimization using the OPTUNA framework. The proposed model demonstrates exceptional accuracy, achieving R² = 0.93, a20-index = 0.95, and MAPE = 7.89 % on unseen test data, consistently outperforming nine benchmark models including established design guidelines. Scenario-based analysis confirms the model's ability to capture known physical behaviors, such as the effects of concrete strength, column diameter, and FRP thickness on confinement effectiveness. The integration of physical constraints enhances the model's reliability and interpretability, bridging the gap between data-driven and physics-based approaches. This research contributes to the advancement of more accurate, economical, and reliable design guidelines for FRP-strengthened structures, while also demonstrating the potential of constrained deep learning in structural engineering applications.

A review of wavelet theory and its applications in structural and earthquake engineering

A review of wavelet theory and its applications in structural and earthquake engineering

Effect of Structures with Structured Surface Pad on Material Removal Rate in Chemical Mechanical Polishing

We investigated the impact of the designed contact area (DCA) and designed contact length (DCL) on material removal rates (MRR) when using a pad with a structured surface in chemical mechanical polishing. The structure of the structured surface pad (SSP) was precisely defined, and an examination was conducted to assess the influence of variations in the shape, size, and spacing of the unit figure (UF) on the MRR. The results revealed that maintaining the DCA constant while altering the UF shape to extend the DCL led to a 203% increase in the MRR. Furthermore, modifications in the UF size enhanced the MRR by approximately 630%. The relationship between the DCL and MRR was dependent on the DCA. The characteristics of the SSP, particularly the concentrated pressure and involvement of slurry particles at the edges of the contact area, indicated that an increase in the DCL could augment the active slurry particles. This study offers valuable insights into the pad figure structure, simultaneously advancing our understanding of the pad surface topography and its influence on material removal. By focusing on both structural engineering and practical applications, this study paves the way for future research and enables further exploration in this field.

Breach diameter prediction of reinforced concrete wall under contact/near-contact explosives using deep neural network

In recent years, the use of Machine Learning (ML) has been expanded to several engineering fields with applications in structural engineering. Deep Neural Network (DNN) models have been widely implemented to predict the response of structural systems under conventional loading. The accuracy of these DNN models relies on the size of the dataset used, which can vary from hundreds to thousands of data points, typically formed by images, test data, and/or finite element models built for a specific environment. Such dependency becomes a limitation when DNN models are intended to be used in blast analysis and/or design, as data are typically scarce or restricted. This paper introduces the implementation of an ML/DNN model for the prediction of breach diameter of a reinforced concrete target subjected to contact and near-contact explosions. The DNN model predicts the breach diameter using experimentally collected concrete breach data. The DNN model considers several parameters related to the charge (e.g., type, geometry, orientation, casement properties) and wall target (e.g., material properties, geometry, reinforcement, fiber content), as well as proximity between the target and explosive. It was found that charge geometry, charge placement type, target thickness, scaled distance, and fiber content in the mix were important features affecting the breach diameter prediction.

Design-informed generative modelling of skeletal structures using structural optimization

Although various structural optimization techniques have a sound mathematical basis, the structural robustness and practical constructability of optimal designs pose a great challenge in the manufacturing stage. This paper presents an automated novel approach stemming from structural optimization and engineering principles, where discrete members of the structurally optimized designs are driven towards optimal utilization. The developed workflow unifies topology, layout and size optimization in a single parametric platform, which subsequently outputs a ready-to-manufacture CAD skeletal model which can be manufactured either additively or by assembly. All such outputs are checked and validated for structural requirements; strength, stiffness and stability in accordance with standard codes of practice. In the implementations, first, a topology-optimal model is generated and converted to a one-pixel-wide chain model using skeletonization. Herein, this paper uses a novel efficient method to extract the skeleton by using pixel-padding near the domain borders. Secondly, a spatial frame is extracted from the skeleton for its member size and layout optimization. Finally, the CAD model is generated using constructive solid geometry trees and the structural integrity of each member is assessed to ensure structural robustness prior to manufacturing. Various examples presented in the paper showcase the validity of the presented workflow across various structural engineering applications.

Inverse parameter identification and model updating for Cross-laminated Timber substructures

Finite Element (FE) model updating is crucial for identifying key parameters in structural design and improving predictive accuracy. Despite extensive research on advanced FE procedures approved for user applications, persistent disparities remain in real-world scenarios, especially for complex materials like wood. Capturing accurate mechanical characteristics with traditional models poses challenges in sustainability projects. This study introduces a derivative-free model updating procedure using a Single-Objective Optimisation (SOO) incorporating observed and predicted natural frequencies and vibration modes. The objective function optimises tuning parameters to minimise discrepancies between predicted and observed outcomes. The focus is on Cross-laminated Timber (CLT), a composite wooden structure gaining traction as a sustainable alternative to materials like reinforced concrete and steel. However, the mechanical properties of CLT can vary due to inherent variability in wood’s mechanical characteristics. This research identifies sensitive mechanical properties — longitudinal Young’s modulus, internal shear moduli, and rolling shear modulus of CLT — using a model updating procedure based on a comprehensive set of data from Experimental Modal Analysis (EMA). The study provides mathematical algebraic derivations of the updating procedure and a step-by-step implementation algorithm to facilitate practical application in structural engineering.

Indentation response of multi-phase nanocrystalline NbWTi refractory multi-principal element alloy

NbWTi refractory multi-principal element alloy has been synthesized using powder metallurgy route. 40 h of mechanical alloying resulted in a single-phase alloy and subsequent spark plasma sintering transformed it into a multi-phase structure (1 BCC and 2 FCC phases). The alloy demonstrated an extremely high hardness of 12.29 ± 0.05 GPa at a load of 1000 g, along with excellent “density-normalized” hardness and indentation size effect. An indentation fracture toughness of 6.81 ± 0.16 MPa.m1/2 was measured using the cracks generated. Ultra-high absolute hardness, high “density-normalized” hardness and associated fracture toughness signifies the potential of this alloy for next generation structural engineering applications.