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The use of recycled coarse aggregate in concrete structures promotes environmental sustainability; however, performance of these structures might be negatively impacted when it is used as a replacement to traditional aggregate. This paper aims to simulate recycled concrete beams strengthened with carbon fiber-reinforced polymer (CFRP), to advance the modeling and use of recycled concrete structures.

To investigate the performance of beams with recycled coarse aggregate concrete (RCAC), finite element models (FEMs) were developed to simulate 12 preloaded RCAC beams, strengthened with two CFRP strengthening schemes. Details of the modeling are provided including the material models, boundary conditions, applied loads, analysis solver, mesh analysis and computational efficiency.

Using FEM, a parametric study was carried out to assess the influence of CFRP thickness on the strengthening efficiency. The FEM provided results in good agreement with those from the experiments with differences and standard deviation not exceeding 11.1% and 3.1%, respectively. It was found that increasing the CFRP laminate thickness improved the load-carrying capacity of the strengthened beams.

The developed models simulate the preloading and loading up to failure with/without CFRP strengthening for the investigated beams. Moreover, the models were validated against the experimental results of 12 beams in terms of crack pattern as well as load, deflection and strain.

The use of waste polyethylene terephthalate (PET) plastics derived from shredded bottles in concrete is not formalized yet, especially in reinforced members such as beams and columns. The disposal of plastic wastes in concrete is a viable alternative to manage those wastes while minimizing the environmental impacts associated to recycling, carbon dioxide emissions and energy consumption.

This paper evaluates the suitability of 2D deterministic and stochastic finite element (FE) modeling to predict the shear strength behavior of reinforced concrete (RC) beams without stirrups. Different concrete mixtures prepared with 1.5%–4.5% PET additions, by volume, are investigated.

Test results showed that the deterministic and stochastic FE approaches are accurate to assess the maximum load of RC beams at failure and corresponding midspan deflection. However, the crack patterns observed experimentally during the different stages of loading can only be reproduced using the stochastic FE approach. This later method accounts for the concrete heterogeneity due to PET additions, allowing a statistical simulation of the effect of mechanical properties (i.e. compressive strength, tensile strength and Young’s modulus) on the output FE parameters.

Data presented in this paper can be of interest to civil and structural engineers, aiming to predict the failure mechanisms of RC beams containing plastic wastes, while minimizing the experimental time and resources needed to estimate the variability effect of concrete properties on the performance of such structures.

The purpose of this study is to scrutinize the influence of individual learning styles on the effectiveness of augmented reality (AR)-based learning in structural engineering. There has been a lack of research examining the correlation between learning efficiency and learning style, particularly in the context of quantitatively assessing the efficacy of AR in structural engineering education.

Using Kolb’s experiential learning theory (ELT), a model that emphasizes learning through experience, students from the construction management department are assigned four learning styles (converging, assimilating, diverging and accommodating). Performance data were gathered, appraised, and compared through the three dimensions from the Knowledge, Attitude and Practices (KAP) survey model across four categories of Kolb’s learning styles in both text-graph (TG)-based and AR-based learning settings.

The findings indicate that AR-based materials positively impact structural engineering education by enhancing overall learning performance more than TG-based materials. It is also found that the learning style has a profound influence on learning effectiveness, with AR technology markedly improving the information retrieval processes, particularly for converging and assimilating learners, then diverging learners, with a less significant impact on accommodating learners.

These results corroborate prior research analyzing learners' outcomes with hypermedia and informational learning systems. It was found that learners with an “abstract” approach (convergers and assimilators) outperform those with a “concrete” approach (divergers and accommodators). This research emphasizes the importance of considering learning styles before integrating technologies into civil engineering education, thereby assisting software developers and educational institutions in creating more effective teaching materials tailored to specific learning styles.

The research aims to investigate the impact of thermo-mechanical aging on SBR under cyclic-loading. By conducting experimental analyses and developing a 3D finite element analysis (FEA) model, it seeks to understand chemical and physical changes during aging processes. This research provides insights into nonlinear mechanical behavior, stress softening and microstructural alterations in SBR compounds, improving material performance and guiding future strategies.

This study combines experimental analyses, including cyclic tensile loading, attenuated total reflection (ATR), spectroscopy and energy-dispersive X-ray spectroscopy (EDS) line scans, to investigate the effects of thermo-mechanical aging (TMA) on carbon-black (CB) reinforced styrene-butadiene rubber (SBR). It employs a 3D FEA model using the Abaqus/Implicit code to comprehend the nonlinear behavior and stress softening response, offering a holistic understanding of aging processes and mechanical behavior under cyclic-loading.

This study reveals significant insights into SBR behavior during thermo-mechanical aging. Findings include surface roughness variations, chemical alterations and microstructural changes. Notably, a partial recovery of stiffness was observed as a function of CB volume fraction. The developed 3D FEA model accurately depicts nonlinear behavior, stress softening and strain fields around CB particles in unstressed states, predicting hysteresis and energy dissipation in aged SBRs.

This research offers novel insights by comprehensively investigating the impact of thermo-mechanical aging on CB-reinforced-SBR. The fusion of experimental techniques with FEA simulations reveals time-dependent mechanical behavior and microstructural changes in SBR materials. The model serves as a valuable tool for predicting material responses under various conditions, advancing the design and engineering of SBR-based products across industries.

Construction material management is an important process in supporting construction operations that affect project performance. Previous studies attempt to identify factors influencing material management in different stages such as procurement, transportation and utilization. However, they lack a model to explain the relationship between influential factors and the effectiveness of material management. Therefore, this study aims to validate the variables and key factors influencing the effectiveness of material management processes.

A total of 42 variables were reviewed from literature in different stages of material management process. Survey questionnaires were used to collect data about participants' perceptions on these variables. The respondents were 200 project engineers and project managers from construction sites and offices in Vietnam. Then factor analysis techniques were undertaken to validate the structure of factor groups. Two methods of exploratory factor analysis (EFA) and confirmatory factor analysis (CFA) were, respectively, performed to evaluate and verify the model's fit.

Factors influencing the effectiveness of material management were grouped into nine main factors, which are procurement issues, site conditions, planning and handling on site, industrial environments, contractual issues, quality control, suppliers and manufacturers' issues, transportation in and out site and security on site.

The paper has several implications for theory and methodology related to material management. It features influential factors in association with the material management effectiveness. Therefore, senior managers can more fully understand the errors in their works and propose timely solutions to limit the unwanted risks.

This research contributes on theoretical development on factors influencing effectiveness of material management processes. The key findings on influential factors can be applied to measure the effectiveness of material management processes.

The use of continuous fiber-reinforced filaments improves the mechanical properties obtained with the fused filament fabrication (FFF) process. Yet, there is a lack of simulation tailored tools to assist in the design for additive manufacturing of continuous fiber composites. To build such models, a precise elastic model is required. As the porosity caused by interbead voids remains an important flaw of the process, this paper aims to build an elastic model integrating this aspect.

To study the amount of porosity, which could be a failure initiator, this study proposes a two step periodic homogenization method. The first step concerns the microscopic scale with a unit cell made of fiber and matrix. The second step is at the mesoscopic scale and combines the elastic material of the first step with the interbead voids. The void content has been set as a parameter of the model. The material models predicted with the periodic homogenization were compared with experimental results.

The comparison between periodic homogenization results and tensile test results shows a fair agreement between the experimental results and that of the numerical simulation, whatever the fibers’ orientations are. Moreover, a void content reduction has been observed by increasing the crossing angle from one layer to another. An empiric law giving the porosity according to this crossing angle was created. The model and the law can be further used for design evaluation and optimization of continuous fiber-reinforced FFF.

A new elastic model considering interbead voids and its variation with the crossing angle of the fibers has been built. It can be used in simulation tools to design high performance fused filament fabricated composite parts.

Among the various applications involving the use of microwave energy, its growing utility within the mining industry is particularly noteworthy. Conventional grinding processes are often overburdened by energy inefficiencies that are directly related to machine wear, pollution and rising project costs. In this work, we numerically investigate the effects of microwave pretreatment through a series of compression tests as a means to help mitigate these energy inefficiencies.

We investigate the effects of microwave pretreatment on various rock samples, as quantified by uniaxial compression tests. In particular, we assign sample heterogeneity based on a Gaussian statistical distribution and invoke a damage model for elemental tensile and compressive stresses based on the maximum tensile stress and the Mohr–Coulomb theories, respectively. We further couple the electromagnetic, thermal and solid displacement relations using finite element modeling.

(1) Increased power intensity during microwave pretreatment results in decreased axial compressive stress. (2) Leveraging statistics to induce variable compressive and tensile strength can greatly facilitate sample heterogeneity and prove necessary for damage modeling. (3) There exists a nonlinear trend to the reduction in smax with increasing power levels, implying an optimum energy output efficiency to create the maximum degradation-power cost relationship.

Previous research in this area has been largely limited to two-dimensional thermo-electric models. The onset of high-performance computing has allowed for the development of high-fidelity, three-dimensional models with coupled equations for electromagnetics, heat transfer and solid mechanics.

The stress concentration factor (SCF) is commonly utilized to assess the fatigue life of a tubular T-joint in offshore structures. Parametric equations derived from experimental testing and finite element analysis (FEA) are utilized to estimate the SCF efficiently. The mathematical equations provide the SCF at the crown and saddle of tubular T-joints for various load scenarios. Offshore structures are subjected to a wide range of stresses from all directions, and the hotspot stress might occur anywhere along the brace. It is critical to incorporate stress distribution since using the single-point SCF equation can lead to inaccurate hotspot stress and fatigue life estimates. As far as we know, there are no equations available to determine the SCF around the axis of the brace.

A mathematical model based on the training weights and biases of artificial neural networks (ANNs) is presented to predict SCF. 625 FEA simulations were conducted to obtain SCF data to train the ANN.

Using real data, this ANN was used to create mathematical formulas for determining the SCF. The equations can calculate the SCF with a percentage error of less than 6%.

Engineers in practice can use the equations to compute the hotspot stress precisely and rapidly, thereby minimizing risks linked to fatigue failure of offshore structures and assuring their longevity and reliability. Our research contributes to enhancing the safety and reliability of offshore structures by facilitating more precise assessments of stress distribution.

Precisely determining the SCF for the fatigue life of offshore structures reduces the potential hazards associated with fatigue failure, thereby guaranteeing their longevity and reliability. The present study offers a systematic approach for using FEA and ANN to calculate the stress distribution along the weld toe and the SCF in T-joints since ANNs are better at approximating complex phenomena than standard data fitting techniques. Once a database of parametric equations is available, it can be used to rapidly approximate the SCF, unlike experimentation, which is costly and FEA, which is time consuming.

Performing accurate numerical simulations of electrical drives, the precise knowledge of the local magnetic material properties is of utmost importance. Due to the various manufacturing steps, e.g. heat treatment or cutting techniques, the magnetic material properties can strongly vary locally, and the assumption of homogenized global material parameters is no longer feasible. This paper aims to present the general methodology and two different solution strategies for determining the local magnetic material properties using reference and simulation data.

The general methodology combines methods based on measurement, numerical simulation and solving an inverse problem. Therefore, a sensor-actuator system is used to characterize electrical steel sheets locally. Based on the measurement data and results from the finite element simulation, the inverse problem is solved with two different solution strategies. The first one is a quasi Newton method (QNM) using Broyden's update formula to approximate the Jacobian and the second is an adjoint method. For comparison of both methods regarding convergence and efficiency, an artificial example with a linear material model is considered.

The QNM and the adjoint method show similar convergence behavior for two different cutting-edge effects. Furthermore, considering a priori information improved the convergence rate. However, no impact on the stability and the remaining error is observed.

The presented methodology enables a fast and simple determination of the local magnetic material properties of electrical steel sheets without the need for a large number of samples or special preparation procedures.

Intumescent coatings are nowadays a dominant passive system used to protect structural materials in case of fire. Due to their reactive swelling behaviour, intumescent coatings are particularly complex materials to be modelled and predicted, which can be extremely useful especially for performance-based fire safety designs. In addition, many parameters influence their performance, and this challenges the definition and quantification of their material properties. Several approaches and models of various complexities are proposed in the literature, and they are reviewed and analysed in a critical literature review.

Analytical, finite-difference and finite-element methods for modelling intumescent coatings are compared, followed by the definition and quantification of the main physical, thermal, and optical properties of intumescent coatings: swelled thickness, thermal conductivity and resistance, density, specific heat capacity, and emissivity/absorptivity.

The study highlights the scarce consideration of key influencing factors on the material properties, and the tendency to simplify the problem into effective thermo-physical properties, such as effective thermal conductivity. As a conclusion, the literature review underlines the lack of homogenisation of modelling approaches and material properties, as well as the need for a universal modelling method that can generally simulate the performance of intumescent coatings, combine the large amount of published experimental data, and reliably produce fire-safe performance-based designs.

Due to their limited applicability, high complexity and little comparability, the presented literature review does not focus on analysing and comparing different multi-component models, constituted of many model-specific input parameters. On the contrary, the presented literature review compares various approaches, models and thermo-physical properties which primarily focusses on solving the heat transfer problem through swelling intumescent systems.

The presented literature review analyses and discusses the various modelling approaches to describe and predict the behaviour of swelling intumescent coatings as fire protection for structural materials. Due to the vast variety of available commercial products and potential testing conditions, these data are rarely compared and combined to achieve an overall understanding on the response of intumescent coatings as fire protection measure. The study highlights the lack of information and homogenisation of various modelling approaches, and it underlines the research needs about several aspects related to the intumescent coating behaviour modelling, also providing some useful suggestions for future studies.