Performance Assessment of Concrete and Steel Material Models in LS-DYNA for Enhanced Numerical Simulation, A State of the Art Review

Zhang, Chunwei

doi:10.1007/s11831-020-09483-5

Cited by 100 publications

(21 citation statements)

References 62 publications

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“…They developed a model to distinguish different safety conditions of scaffolds through the combination of finite element analysis (FEA) and machine learning. The system has the potential to estimate the status of reinforced concrete structures by introducing material performance [44] and structural behaviour [45]. The dynamic model is able to accurately reflect the real situation [46].…”

Section: Identification Of Unsafe Construction Environmentmentioning

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: Identification Of Unsafe Construction Environmentmentioning

confidence: 99%

Literature Review of Digital Twins Applications in Construction Workforce Safety

et al. 2020

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“…So the mining association rules is the implication such as " X = > Y ", in which X I , Y I , X Y = . The mining association rules X = > Y are well-founded in the transmit database and have support degree and confidence degree (Abedini and Zhang 2021 ; Huang et al 2020 ). Support degree expresses that s% transmits of D include X Y .…”

Section: Proposed Qoe-based Prediction Modelmentioning

confidence: 99%

A new QoE-based prediction model for evaluating virtual education systems with COVID-19 side effects using data mining

Tan

Lin

2021

Soft Comput

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Today, emerging technologies such as 5G Internet of things (IoT), virtual reality and cloud-edge computing have enhanced and upgraded higher education environments in universities, colleagues and research centers. Computer-assisted learning systems with aggregating IoT applications and smart devices have improved the e-learning systems by enabling remote monitoring and screening of the behavioral aspects of teaching and education scores of students. On the other side, educational data mining has improved the higher education systems by predicting and analyzing the behavioral aspects of teaching and education scores of students. Due to an unexpected and huge increase in the number of patients during coronavirus (COVID-19) pandemic, all universities, campuses, schools, research centers, many scientific collaborations and meetings have closed and forced to initiate online teaching, e-learning and virtual meeting. Due to importance of behavioral aspects of teaching and education between lecturers and students, prediction of quality of experience (QoE) in virtual education systems is a critical issue. This paper presents a new prediction model to detect technical aspects of teaching and e-learning in virtual education systems using data mining. Association rules mining and supervised techniques are applied to detect efficient QoE factors on virtual education systems. The experimental results described that the suggested prediction model meets the proper accuracy, precision and recall factors for predicting the behavioral aspects of teaching and e-learning for students in virtual education systems.

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“…Types of 3D printers for polymer printers include: Fused deposition modeling (FDM), Stereo lithography (SLA), Digital light processing (DLP), Selective laser sintering (SLS), and laminated objective manufacturing (LOM), Liquid Deposition Modeling (LDM) (ceramic 3D printer) [24,25]. 3D printers of FDM are one of the most common 3D printing methods in which extruded laments of thermoplastic polymers are used to produce the part [26][27][28][29][30]. The extruder is placed on a three-dimensional CNC table that moves in the x, y and z directions and places the molten polymer inside the extruder on the part [31,32].…”

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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Performance Assessment of Concrete and Steel Material Models in LS-DYNA for Enhanced Numerical Simulation, A State of the Art Review

Cited by 100 publications

References 62 publications

Literature Review of Digital Twins Applications in Construction Workforce Safety

Literature Review of Digital Twins Applications in Construction Workforce Safety

A new QoE-based prediction model for evaluating virtual education systems with COVID-19 side effects using data mining

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

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