Development of pressure-impulse models and residual capacity assessment of RC columns using high fidelity Arbitrary Lagrangian-Eulerian simulation

Zhang, Chunwei; Abedini, Masoud; Mehrmashhadi, Javad

doi:10.1016/j.engstruct.2020.111219

Cited by 64 publications

(13 citation statements)

References 28 publications

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“…When precursors are outside the acceptance criteria, the situation is identified as a hazard, and corresponding solutions are automatically generated to solve the hazard. With the aid of ontology, the response of structural members in different loads, such as seismic loads [80,81], blast loads [82,83], and impact loads [84,85], has the opportunity to be integrated, which can provide the knowledge basis for accident simulation.…”

Section: Construction Activity Visualisationmentioning

confidence: 99%

Literature Review of Digital Twins Applications in Construction Workforce Safety

et al. 2020

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For many decades, safety has been a challenge in the construction sector. Despite extensive efforts to improve overall safety, the sector’s casualty rate still remains high. In practice, dynamic and complex construction processes may lead to on-site risks and safety plans being overlooked, likely leading to a variety of safety accidents. Nowadays, under the guidance of the digital twins (DT) concept, the advent of state-of-the-art sensing and visualisation technologies has offered the possibility to improve construction health and safety in the workplace. To understand the research advances of these technologies, identify their gaps and challenges, and propose solutions to further advance the industry’s safety, we conducted and report a thorough review on the state-of-the-art technological studies, and elaborate upon the key findings in detail. For instance, despite DT being proven to be effective in improving construction workforce safety, the construction industry has yet to fully exploit and streamline these innovations in practice. Overall, this review provides insights into technological clustering, improvement strategies, as well as workforce safety, which can benefit from formulating effective digital technology paradigms.

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Section: Construction Activity Visualisationmentioning

confidence: 99%

Literature Review of Digital Twins Applications in Construction Workforce Safety

et al. 2020

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“…Recently, many studies have been performed on the mechanical properties of 3D polymer samples printed by FDM method, and these studies show the importance of paying attention to 3D printing parameters. Most recent research has focused on the effect of second-and third-group fabrication parameters on the behavior of samples printed with Acrylonitrile Butadiene Styrene (ABS) lament, while Polyactic Acid (PLA) lament, despite its unique properties, has received less attention from researchers [46][47][48][49][50]. For example, a group of researchers studied the effect of print parameters such as layer thickness, direction and printing angle on mechanical properties using ABS and PLA laments.…”

Section: Introductionmentioning

confidence: 99%

Effect of different parameters on the tensile properties of printed Polylactic acid samples by FDM: experimental design tested with MDs simulation

Farazin

Mohammadimehr

2021

Int J Adv Manuf Technol

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Fused Depositional Modeling (FDM) is one of the common methods for 3D printing of polymers, which is expanding in various industrial applications, scienti c researches, and engineering applications due to its ability to make complex parts. In this research, molecular dynamics (MDs) simulation has been used to predict the physical and mechanical properties. Then, the mechanical properties of the printed parts are obtained. The mechanical properties of 3D printed parts strongly depend on the correct selection of processing parameters. In this study, the effect of three important parameters such as in ll density, printing speed, and layer thickness are investigated on the tensile properties of PLA specimens. For this purpose, standard specimens with four in ll densities of 20%, 40%, 60% and 80%, two speeds of 20 mm/s, and 40 mm/s, and two thicknesses of 0.1 mm and 0.2 mm are printed and tested under quasistatic tensile test. In all printed specimens, the print angle is ± 45°. The experimental results show that the in ll density in comparison to the other two parameters has signi cant effect on mechanical properties such as modulus of elasticity, ultimate strength, and failure strain. According to these results, by increasing the in ll density, the stiffness and strength of the specimens increases considerably. At in ll density of 80%, the specimens has the highest stiffness and strength, but it exhibits a brittle behavior. Moreover, it can be deduced that by reducing the layer thickness although the modulus of elasticity increases a little, ductility is greatly affected.

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“…More technically, deep learning-based [63][64][65][66], machine learning [67][68][69], decision making-based theories, feature selection-based solutions [70][71][72], extremer machine learning solutions [73][74][75][76], as well as hybrid searching algorithms that enhanced conventional multilayer perceptron like harris hawks optimization [77,78], whale optimizer [79,80], bacterial foraging optimization [81], chaos enhanced grey wolf optimization [82], moth-flame optimizer [74,83], many-objective sizing optimization [84][85][86][87][88][89], Driven Robust Optimization [90], ant colony optimization [91], and global numerical optimization [92]. These techniques are successfully employed in different aspects such as building design [93][94][95][96][97][98][99][100], image processing/classification [101][102][103][104][105]…”

Section: Introductionmentioning

confidence: 99%

Suggesting a Stochastic Fractal Search Paradigm in Combination With Artificial Neural Network for Early Prediction of Cooling Load in Residential Buildings

Moayedi¹,

Mosavi²

2021

Preprint

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Early prediction of thermal loads plays an essential role in analyzing energy-efficient buildings' energy performance. On the other hand, stochastic algorithms have recently shown high proficiency in dealing with this issue. These are the reasons that this work is dedicated to evaluating an innovative hybrid method for predicting the cooling load (CL) in buildings with residential usage. The proposed model is a combination of artificial neural networks and stochastic fractal search (SFS-ANN). Two benchmark algorithms, namely the grasshopper optimization algorithm (GOA) and firefly algorithm (FA), are also considered to be compared with the SFS. The non-linear effect of eight independent factors on the CL is analyzed using each model's optimal structure. Evaluation of the results outlined that all three metaheuristic algorithms (with more than 90 % correlation) can adequately optimize the ANN. In this regard, this tool's prediction error declined by nearly 23, 18, and 36 % by applying the GOA, FA, and SFS techniques. Also, all used accuracy criteria indicated the superiority of the SFS over the benchmark schemes. Therefore, it is inferred that utilizing the SFS along with ANN provides a reliable hybrid model for the early prediction of CL.

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Development of pressure-impulse models and residual capacity assessment of RC columns using high fidelity Arbitrary Lagrangian-Eulerian simulation

Cited by 64 publications

References 28 publications

Literature Review of Digital Twins Applications in Construction Workforce Safety

Literature Review of Digital Twins Applications in Construction Workforce Safety

Effect of different parameters on the tensile properties of printed Polylactic acid samples by FDM: experimental design tested with MDs simulation

Suggesting a Stochastic Fractal Search Paradigm in Combination With Artificial Neural Network for Early Prediction of Cooling Load in Residential Buildings

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