Engineering Structures Research Articles

Mechanical properties and damping ratio of gradient polymer concrete for damping beam structure

AbstractDue to its higher overall performance than ordinary concrete, polymer concrete (PC) beam has been widely used in the field of structural engineering. However, it is widely recognized that PC cannot simultaneously achieve the highest mechanical strength and optimal damping performance to reduce vibration during its long‐term service lifetime. To address this contradiction, the effect of various diluents contents on the overall performance of PC was investigated first in this investigation. Experimental findings revealed that the bending strength of PC exhibits an initial increase followed by a decrease with an increment of diluents, while its damping ratio displays an inverse trend. To obtain excellent overall performance (higher flexural strength and damping ratio) of PC, gradient PC has been manufactured by horizontal partition casting process (HPCP) and stereoscopic layered casting process (SLCP). Experimental results demonstrated that the gradient PC manufactured by HPCP exhibited better overall performance compared to SLCP. Furthermore, the graphite and carbon fiber were incorporated into the above gradient PC, and experimental results revealed that the incorporated graphite and carbon fiber can further increase the overall performance of the above gradient PC. Similarly, the incorporation of glass fiber cloth and carbon fiber cloth, along with surface treatment, significantly enhanced the overall performance of PC. The significance of this study lies in enhancing the comprehensive performance of PC beam structures, thus broadening its utilization scope within structural engineering.

Procedimentos para modelagem BIM de lajes nervuradas de concreto armado em edificações

It is impossible not to admit that commercial computational tools for structural analysis and detailing have become indispensable in the various applications of Structural Engineering nowadays. The importance of these tools equipped with graphical interfaces based on the BIM (Building Information Modeling) environment is mainly due to the flexibility and ease in making common corrections and changes during the elaboration stage, as well as the proposition of different structural conceptions, not always innovative, but almost always bold, as in the case of reinforced concrete ribbed slabs. The use of ribbed slabs in reinforced concrete is an old practice in Structural Engineering, which has encouraged the adaptation of commercial computational tools, both in the development of structurally consistent mathematical formulations, as well as in the development of specific databases, equipped with information from mold manufacturers and materials intended for the execution of this type of slabs. It is highly efficient in building structures in which there are numerous problems of interference of structural elements with elements of installations, such as large-diameter pipes for wastewater, sewage, gas, fire, and even air conditioning ducts, exhaust and architectural elements. This efficiency is mainly due to the fact that large beams are not considered in their supports, making the lower face of the slab practically without obstacles to the building pipes, and is also due to the low average thickness of concrete used in the pavement compared to conventional rectangular supported slabs and flat mushroom slabs. However, it is not always possible to obtain an optimal final result, even with all the adaptations previously described, mainly due to executive and computational modeling problems. This work intends to present a possible structural modeling methodology, which provides results within the limits of the feasibility of the slab, both in the construction aspect of the computational structural model, as well as the possibility of using materials from manufacturers and labor, using a commercial computational tool for structural analysis and detailing.

Reliability polynomial chaos metamodel for the dynamic behaviour of reinforced concrete bridges

The approximation of complex engineering problems and mathematical regressions serves as the authentic inspiration behind the artificial intelligence metamodeling methods. Among these methods, polynomial chaos expansion, along with artificial neural networks, has emerged at the forefront and become the most practical technique. Previous studies have highlighted their robust capabilities in solving complex problems and their wide utilization across numerous applications, particularly in structural analysis, optimization design problems, and predictive models of uncertainty outcomes. The aim of this article is to present a methodology that introduces their implementation of for structural engineering, primarily focusing on reinforced concrete bridges. The proposed approach consists of demonstrating the applicability of the polynomial chaos to evaluate the dynamic behavior of two-span reinforced concrete bridges through a predictive model of natural vibration properties for eigenvalues modal analysis. Subsequently, response spectral method is conducted according to the Moroccan guide for bridge seismic design and the prescription of the EUROCODE 8 within the context of reliability assessment using Monte Carlo simulation. The efficacy of the proposed approach is illustrated by a comparison between the predicted vibration properties and the resulting values obtained through finite element modal analysis and artificial neural networks. The polynomial chaos process is based on a collected dataset of multiple reinforced concrete bridges sourced from technical studies offices and the Regional Administration of the East, affiliated with the Moroccan Ministry of Equipment and Water. Finally, this work contributes to the field by enhancing predictive modeling and reliability evaluation for bridge engineering using artificial intelligence metamodels.

The methodology for evaluating the fire resistance performance of concrete-filled steel tube columns by integrating conditional tabular generative adversarial networks and random oversampling

Artificial Intelligence (AI) technology has emerged as a powerful tool for addressing various complex issues within engineering structures. Accurately assessing the fire resistance performance of Concrete-Filled Steel Tube (CFST) columns poses a significant challenge for researchers. The scarcity of CFST column fire resistance test data presents a hurdle in the development of Machine Learning (ML) models. To tackle this issue, this study proposes a novel ML approach that integrates Conditional Tabular Generative Adversarial Networks (CTGAN) and Random Oversampling (ROS) for evaluating CFST column fire resistance performance. CTGAN technology is used to generate synthetic datasets to alleviate data scarcity issues, while ROS technique is used to balance the dataset. To validate the effectiveness of this method, detailed experimental tests using eight ML algorithms were conducted on both real and synthetic datasets, with results indicating superior performance on the synthetic dataset compared to the real dataset. The optimal model in the synthetic dataset achieves an Accuracy, Precision, Recall, and F1-Score of 0.972, 0.973, 0.972, and 0.972, respectively. Furthermore, extensive comparative analyses and practical applications of the proposed method were conducted, revealing its exceptional performance in evaluating the fire resistance of CFST columns. To facilitate ease of use and operation in practical engineering, a visual interface was developed. The introduction of this technique offers a novel solution for researchers and engineers in the field of structural fire engineering.

Research on the influence of impact damage on force identification for composite material

The invisible damage caused by low-velocity impacts are safety threats to engineering structures. Thus, impact force identification is crucial in the context of composite structures for both structure health monitoring (SHM) and composite structure design. This paper investigates the process of identifying impacts on composite structures subjected to low-velocity impact. Considering the damage evolution in the composite structure during impact, this paper explores the influence of impact damage on the accuracy of force identification. Impact experiments on carbon fiber reinforced polymer (CFRP) laminates were conducted to obtain impact force peaks and displacement responses. Furthermore, a validated finite element model (FEM) is established for more elaborate analysis on the mechanism. The findings reveal that the structural damage can lead to significant deviations in force identification if the damage is not considered. Finally, a neural network is employed to predict the force history taking impact damage into consideration. This research provides a reference for the composite structures design and health monitoring of engineering structures considering impact damage.

Nonlinear dynamics of wing-like structures using a momentum subspace-based Koiter-Newton reduction

Cantilevers find a wide range of applications in the design of scientific equipment and large-scale engineering structures such as aircraft wings. Analysis techniques based on linearization approximations are unable to capture the large amplitude oscillation behaviour of such structures and thus, necessitates development of dedicated nonlinear methods. In this work, the recent developments in the Koiter-Newton model reduction method are utilized to obtain nonlinear reduced order models (ROMs) from full finite element structural models in order to simulate large amplitude dynamics of cantilevers. The method describes a nonlinear system of governing equations comprising quadratic and cubic terms which are obtained as higher order derivatives of the in-plane strain energy. To ensure that the large rotations in cantilevers and the resultant foreshortening effect is also accounted for, a ROM updating algorithm is adopted where the ROM parameters are varied with the structural deflections. Linear eigenmodes of the structure are utilized to form the reduction subspace. To validate the methodology, the ROM solution is compared against experimental results and a convergence study is conducted to identify the number of modes needed to replicate the nonlinear response. Finally, a composite wingbox structure is considered for which time domain simulations are conducted and frequency response curves, obtained through a frequency sweep, are presented.

Investigation of additively manufactured metamaterials for load-bearing structural applications

The study explores the potential application of Triply Periodic Minimal Surface (TPMS) metamaterials in structural engineering and architecture. It evaluates parametric design possibilities and analysis approaches while examining the mechanical properties of five TPMS structures (Gyroid, Schwarz, Diamond, Split, and Neovius). Comparisons are made between Additively Manufactured (AM) samples and numerical simulations. Gyroid and Diamond structures, which display advantageous properties, undergo further investigation with parametric design for uniform and non-uniform lattices. The study proposes simplified analytical formulations for metamaterial design, along with Homogenization analysis and detailed FEM analysis. Compression tests reveal distinct failure modes and stress-strain behaviors for each TPMS. The research suggests further investigation into the influence of the 3D printing process, void detection, and thermomechanical analysis. Finally, the study provides insights into tailored applications of these lattice metamaterials in structural engineering.

Nd-Ions Role in Structural Engineering and Capacitive Contributions of NiO Nanosheets Clusters as Excellent Pseudocapacitor Electrode Material

Nd-Ions Role in Structural Engineering and Capacitive Contributions of NiO Nanosheets Clusters as Excellent Pseudocapacitor Electrode Material

Machine learning-based approach for assessing the seismic vulnerability of reinforced concrete frame buildings

Recent artificial intelligence (AI) advancements have significantly impacted the field of structural engineering by offering new solutions to enhance safety, efficiency, and cost-effectiveness in the design, construction, and maintenance of infrastructure and urban areas. This study has investigated a machine learning-based approach to estimate the seismic vulnerability of reinforced concrete (RC) building frames. The study utilized a probabilistic technique to assess the simulation of a 4-story RC building frame in the face of epistemic uncertainty. Data was collected using the Monte-Carlo approach, which created a machine learning (ML) model for predicting structural damage. The problem was formulated as both a regression-based and a classification-based model. An artificial neural network (ANN) feed-forward network architecture model was used to solve the classification problems. The optimal number of neurons in the hidden layer was determined to produce the best estimation model. Three different models were combined for the regression model, including LASSO regression, random forest, and gradient boosting, by implementing the stacking generalization. The investigation results indicate that the stacked predicted model exhibits less variance than other ML models. The classification and regression-based algorithms can forecast damage states with a high degree of accuracy, ranging from 87 to 95 percent. In conclusion, the study has demonstrated the effectiveness of machine learning techniques for predicting the seismic vulnerability of RC building frames. With the continued advancement of AI and big data, these methods will likely play a crucial role in the structural engineering field.

Lithiation: Advancing Material Synthesis and Structural Engineering for Emerging Applications.

Lithiation, a process of inserting lithium ions into a host material, is revolutionizing nanomaterials synthesis and structural engineering as well as enhancing their performance across emerging applications, particularly valuable for large-scale synthesis of high-quality low-dimensional nanomaterials. Through a systematic investigation of the synthetic strategies and structural changes induced by lithiation, this review aims to offer a comprehensive understanding of the development, potential, and challenges associated with this promising approach. First, the basic principles of lithiation/delithiation processes will be introduced. Then, the recent advancements in the lithiation-induced structure changes of nanomaterials, such as morphology tuning, phase transition, defect generation, etc., will be stressed, emphasizing the importance of lithiation in structural modulation of nanomaterials. With the tunable structures induced by the lithiation, the properties and performance in electrochemical, photochemical, electronic devices, bioapplications, etc. will be discussed, followed by outlining the current challenges and perspectives in this research area.

Hybrid Four Vector Intelligent Metaheuristic with Differential Evolution for Structural Single-Objective Engineering Optimization

Hybrid Four Vector Intelligent Metaheuristic with Differential Evolution for Structural Single-Objective Engineering Optimization

Influence of fly ash and granulated blast furnace slag on autogenous shrinkage and drying shrinkage of cement-based materials mixed with alkali accelerator and alkali-free accelerator

Shotcrete shrinkage and cracking are getting more threatening due to its high content of cementitious materials, small aggregate particle size and a large amount of accelerator, which poses a great threat to the construction safety of engineering structures. This paper studies the volume stability of cement mortar with different contents of fly ash (FA) and granulated blast furnace slag (GBFS) (10%/20%/30%) under the condition that the dosage of alkali liquid accelerator is 4% and that of the alkali-free liquid accelerator is 7%. This work is designed to provide a guidance for the basic research and practical application. By comparing the autogenous shrinkage, drying shrinkage, mass loss caused by drying condition, and internal humidity change of drying condition of cement mortar within 180 days of age. Combined with MIP analysis, XRD hydration product analysis, and SEM hydration product morphology analysis, the results show that both fly ash and granulated blast furnace slag can reduce the autogenous shrinkage and drying shrinkage of cement mortar mixed with accelerator to a certain extent, and the shrinkage reduction effect of FA is more prominent. The reduction effect of GBFS is more obvious at high dosages. Adding FA and high content of GBFS can increase the mass loss of cement mortar under drying shrinkage conditions. The high content of mineral admixtures will exacerbate the rate of decrease in the internal humidity of the cement mortar. The reasons for the reduction of autogenous shrinkage and drying shrinkage of cement mortar mixed with accelerators due to the addition of FA and GBFS are: the early activity of the two mineral admixtures is low, and the hydration process is slow. And the reduction of pore size with the range of 5-50nm leads to the reduction of capillary tensile stress and the weakening of the driving force for shrinkage deformation. Among them, the shrinkage reduction effect of fly ash is more pronounced. Therefore, mineral admixture can be used to replace a certain amount of cement in actual shotcrete construction, so as to increase the volume stability of shotcrete.

Predicting Main Characteristics of Reinforced Concrete Buildings Using Machine Learning

This paper presents a comprehensive study of five machine learning (ML) algorithms for predicting key characteristics of Reinforced Concrete (RC) structural systems. A novel dataset, ModRes, consisting of 9723 examples derived from modal and response spectrum analyses on masonry-infilled three-dimensional RC buildings, was created for ML applications. The primary objective is to develop an ML model using five distinct algorithms from the literature, capable of concurrently predicting torsional irregularity, modal participating mass ratio (MPMR), and the fundamental period in a 3D environment, while accounting for the influence of infill walls. Additionally, the study aims to determine the applicability of pushover analysis (POA) without the need for extensive numerical modeling and analysis. This approach optimizes the preliminary design process with minimal computational effort, providing valuable insights into dynamic and torsional responses during seismic events. The Categorical Boosting algorithm demonstrated outstanding performance, achieving R2 values of 0.977 for torsional irregularity, 0.997 for the fundamental period, and 0.923 for MPMR on the test dataset. It also successfully predicted POA applicability with an error rate of only 1.36%. This study highlights the practical application of ML algorithms, underscoring their effectiveness in structural engineering.

Investigations on the finite strain behavior of standard PVB: experiment and modeling

AbstractThe numerical simulation of the residual load-bearing capacity of laminated glass is one of the most relevant topics of current research in structural glass engineering. The mechanical description of the behavior of the interlayer plays a crucial role. Several approaches and models already exist in automotive engineering and structural protection. Structural glass design assuming quasi-static loads misses a detailed model so far. The aim of this work is the mechanical description of the time-dependent behavior of Polyvinylbutyral (PVB) under large deformations considering quasi-static loading. The results of experiments on single-layer Standard PVB are presented, and a model of finite nonlinear viscoelasticity is derived. In particular, the formulation of phenomenologically motivated viscosity functions is described. Subsequently, the model parameters are calibrated on the experimental results, and the uniaxial stress state is simulated.

A lightweight visualization tool for protein unfolding by collision detection and elimination

The experiments involving protein denaturation and refolding serve as the foundation for predicting the three-dimensional spatial structures of proteins based on their amino acid sequences. Despite significant progress in protein structure engineering, exemplified by AlphaFold2 and OmegaFold, there remains a gap in understanding the folding pathways of polypeptide chains leading to their final structures. We developed a lightweight tool for protein unfolding visualization called PUV whose graphics design is mainly implemented by OpenGL. PUV leverages principles from molecular biology and physics, and achieves rapid visual dynamics simulation of protein polypeptide chain unfolding through mechanical force and atom-level collision detection and elimination. After a series of experimental validations, we believe that this method can provide essential support for investigating protein folding mechanisms and pathways.

An Interval Neural Network Method for Identifying Static Concentrated Loads in a Population of Structures

During the design and validation of structural engineering, the focus is on a population of similar structures, not just one. These structures face uncertainties from external environments and internal configurations, causing variability in responses under the same load. Identifying the real load from these dispersed responses is a significant challenge. This paper proposes an interval neural network (INN) method for identifying static concentrated loads, where the network parameters are internalized to create a new INN architecture. Additionally, the paper introduces an improved interval prediction quality loss function indicator named coverage and mean square criterion (CMSC), which balances the interval coverage rate and interval width of the identified load, ensuring that the median of the recognition interval is closer to the real load. The efficiency of the proposed method is assessed through three examples and validated through comparative research against other loss functions. Our research findings indicate that this approach enhances the interval accuracy, robustness, and generalization of load identification. This improvement is evident even when faced with challenges such as limited training data and significant noise interference.

Manipulating d-Band Center of Nickel by Single-Iodine-Atom Strategy for Boosted Alkaline Hydrogen Evolution Reaction.

Ni-based electrocatalysts have been predicted as highly potential candidates for hydrogen evolution reaction (HER); however, their applicability is hindered by an unfavorable d-band energy level (Ed). Moreover, precise d-band structural engineering of Ni-based materials is deterred by appropriative synthesis methods and experimental characterization. Herein, we meticulously synthesize a special single-iodine-atom structure (I-Ni@C) and characterize the Ed manipulation via resonant inelastic X-ray scattering (RIXS) spectroscopy to fill this gap. The complex catalytic mechanism has been elucidated via synchrotron radiation-based multitechniques (SRMS) including X-ray absorption fine structure (XAFS), in situ synchrotron radiation-based Fourier transform infrared (SR-FTIR) spectroscopy, and near ambient pressure X-ray photoelectron spectroscopy (NAP-XPS). In particular, RIXS is innovatively applied to reveal the precise regulation of Ni Ed of I-Ni@C. Consequently, the role of such single-iodine-atom strategy is confirmed to not only facilitate the moderate Ed of the Ni site for balancing the adsorption/desorption capacities of key intermediates but also act as a bridge to enhance the electronic interaction between Ni and the carbon shell for forming a localized polarized electric field conducive to H2O dissociation. As a result, I-Ni@C exhibits an enhanced alkaline hydrogen evolution performance with an overpotential of 78 mV at 10 mA/cm2 and superior stability, surpassing the majority of the reported Ni-based catalysts. Overall, this study has managed to successfully tailor the d-band center of materials from the SRMS perspective, which has crucial implications for nanotechnology, chemistry, catalysis, and other fields.

Cyclic frictional response of rough rock joints under shear disturbances: Laboratory experiment and numerical simulation

The cyclic frictional response of rock joints under shear disturbances is critical for understanding the stability and durability of rock engineering structures. Laboratory experiments and numerical simulations were conducted to examine the effects of varying cyclic shear displacement amplitudes, frequencies, and cycle numbers on the macroscopic and microscopic shear characteristics of rough rock joints. The experimental results reveal significant differences in shear strength between the first few cycle and subsequent cycles during the cyclic shear process. As the number of shear cycles increases, the asperities on the contact surface gradually sustain damage, leading to a reduction in normal displacement. During cyclic shear, the peak shear load exhibits a two-stage variation with the number of cycles: an initial sharp decline followed by a gradual increase as the cycles proceed. The peak shear strength shows no obvious pattern under different shear displacement amplitudes and frequencies in the early stages of cyclic shear. As cyclic shear progresses, the peak shear strength decreases with increasing shear displacement amplitude but increases with higher shear frequency. Numerical simulations indicate that significant plastic deformation and shear wear occur on the joint surface during the initial cycles. The growth of the wear area is primarily concentrated in regions of stress concentration. Additionally, the simulations reveal that the volume of shear wear increase nonlinearly with the number of cycles. This research provides new insights into the cyclic shear behavior of rough rock joints and offers valuable references for engineering applications.

Silicon Nanowires via Metal-Assisted Chemical Etching for Energy Storage Applications.

Silicon nanowires (SiNWs) have demonstrated great potential for energy storage due to their exceptional electrical conductivity, large surface area, and wide compositional range. Metal-assisted chemical etching (MACE) is a widely used top-down technique for fabricating silicon micro/nanostructures. SiNWs fabricated by MACE exhibit significant surface areas and diverse surface chemistry. Since the material composition and surface chemistry have a significant impact on the electrochemical energy storage performance, integrating SiNWs with diverse materials like porous carbon, metal oxides/sulfides, and polymers, can establish composites with excellent properties. Hence, it is imperative to meticulously fabricate SiNW-based materials with customizable morphologies and enhanced electrochemical energy-storage performance. This review provides an in-depth study of recent advancements in SiNW-based materials with enhanced performance for energy storage systems, such as supercapacitors (SCs) and lithium-ion batteries (LIBs). It includes a concise overview of the history, MACE synthesis, and characteristics of SiNWs. Further, it also explores the key elements that influence the MACE process of SiNWs and delves into structural engineering. Additionally, we introduce recent advances in SiNW-based materials for the design of high-performance energy-storage devices, namely SCs and LIBs. Finally, we present the crucial future prospects of SiNW-based materials for energy-storage applications.

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.