Review on electromagnetic wave absorbing capacity improvement of cementitious material

Ma, Guowei; Sun, Junbo; Aslani, Farhad; Huang, Yimiao; Jiao, Fengyu

doi:10.1016/j.conbuildmat.2020.120907

Cited by 59 publications

(17 citation statements)

References 146 publications

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“…Fibre Bragg Grating technology (FBG) realizes real-time and convenient quality control for asphalt mixture compaction operation during lab experiments ( Figure 9 ). FGB technology present great benefits on monitoring the quality of concrete [ 88 , 89 , 90 ]. Sensing technologies also invite digital technologies and artificial intelligence into the construction industry to predict the mechanical properties under static and flexural fatigue loading [ 91 , 92 , 93 ].…”

Section: Qualitative Data Collection and Analysismentioning

confidence: 99%

A Governance Framework to Assist with the Adoption of Sensing Technologies in Construction

Arabshahi

Wang

et al. 2021

Sensors

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Sensing technologies present great improvements in construction performance including the safety, productivity, and quality. However, the corresponding applications in real projects are far behind compared with the academically research. This research aims to discover dominate influence factors in the sensing technologies adoption and ultimately develop a governance framework facilitating adoption processes. The framework is dedicated on general sensing technologies rather than single sensor in previous framework studies. To begin with, the influence factors of sensing technologies and other similar emerging technologies are summarised through a review. Then, a mixed methods design was employed to collect quantitative data through an online survey, and qualitative data through semi-structured interviews. Findings of the quantitative method reveal that the most widely implemented sensing technologies are GPS and visual sensing technology, but they’re still not adopted by all construction companies. Partial Least Squares Structural Equation Modelling reveals that supplier characteristics have the highest effect in all influence factors. Qualitative method was adopted to investigate perceptions of construction stakeholders on the major decision-making considerations in the adoption process. Ultimately, a triangulation analysis of findings from the literature review, online survey and interviews resulted in the governance framework development. The overarching contribution of this research focus on the general adoption of sensing technologies rather than the adoption of a specific sensor. Therefore, the governance framework can assist with the decision-making process of any sensing technology adoption in construction.

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Section: Qualitative Data Collection and Analysismentioning

confidence: 99%

A Governance Framework to Assist with the Adoption of Sensing Technologies in Construction

Arabshahi

Wang

et al. 2021

Sensors

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“…Firstly, upstream technologies such as computer visioning or sensor technology [ 110 , 111 , 112 , 113 ] can be integrated to intelligently capture site information and form design constraints. Secondly, downstream technologies such as 3D printing or robotic arms in construction [ 114 , 115 , 116 , 117 , 118 , 119 , 120 , 121 ] can be integrated to automatically construct the GD solutions generated beforehand. Moreover, GD-BIM demonstrates promising potential in smart engineering composites [ 122 , 123 , 124 ] as well as artificial intelligence aided optimization [ 125 , 126 , 127 , 128 ].…”

Section: Discussionmentioning

confidence: 99%

Generative Design in Building Information Modelling (BIM): Approaches and Requirements

Wang

et al. 2021

Sensors

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The integration of generative design (GD) and building information modelling (BIM), as a new technology consolidation, can facilitate the constructability of GD’s automatic design solutions, while improving BIM’s capability in the early design phase. Thus, there has been an increasing interest to study GD-BIM, with current focuses mainly on exploring applications and investigating tools. However, there are a lack of studies regarding methodological relationships and skill requirement based on different development objectives or GD properties; thus, the threshold of developing GD-BIM still seems high. This study conducts a critical review of current approaches for developing GD in BIM, and analyses methodological relationships, skill requirements, and improvement of GD-BIM development. Accordingly, novel perspectives of objective-oriented, GD component-based, and skill-driven GD-BIM development as well as reference guides are proposed. Finally, future research directions, challenges, and potential solutions are discussed. This research aims to guide designers in the building industry to properly determine approaches for developing GD-BIM and inspire researchers’ future studies.

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“…HSC can also be served as the main construction material accompanied by several advanced technologies, such as building information modelling, 3D printing technology, etc. [16][17][18][19][20][21][22][23]. Besides, solid waste materials, such as waste glass and recycled aggregate, have the potential to be applied in HSC to overcome the strength shortcomings of the waste itself [24][25][26][27][28][29][30][31].…”

Section: Introductionmentioning

confidence: 99%

Mechanical Performance Prediction for Sustainable High-Strength Concrete Using Bio-Inspired Neural Network

et al. 2022

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High-strength concrete (HSC) is a functional material possessing superior mechanical performance and considerable durability, which has been widely used in long-span bridges and high-rise buildings. Unconfined compressive strength (UCS) is one of the most crucial parameters for evaluating HSC performance. Previously, the mix design of HSC is based on the laboratory test results which is time and money consuming. Nowadays, the UCS can be predicted based on the existing database to guide the mix design with the development of machine learning (ML) such as back-propagation neural network (BPNN). However, the BPNN’s hyperparameters (the number of hidden layers, the number of neurons in each layer), which is commonly adjusted by the traditional trial and error method, usually influence the prediction accuracy. Therefore, in this study, BPNN is utilised to predict the UCS of HSC with the hyperparameters tuned by a bio-inspired beetle antennae search (BAS) algorithm. The database is established based on the results of 324 HSC samples from previous literature. The established BAS-BPNN model possesses excellent prediction reliability and accuracy as shown in the high correlation coefficient (R = 0.9893) and low Root-mean-square error (RMSE = 1.5158 MPa). By introducing the BAS algorithm, the prediction process can be totally automatical since the optimal hyperparameters of BPNN are obtained automatically. The established BPNN model has the benefit of being applied in practice to support the HSC mix design. In addition, sensitivity analysis is conducted to investigate the significance of input variables. Cement content is proved to influence the UCS most significantly while superplasticizer content has the least significance. However, owing to the dataset limitation and limited performance of ML models which affect the UCS prediction accuracy, further data collection and model update must be implemented.

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Review on electromagnetic wave absorbing capacity improvement of cementitious material

Cited by 59 publications

References 146 publications

A Governance Framework to Assist with the Adoption of Sensing Technologies in Construction

A Governance Framework to Assist with the Adoption of Sensing Technologies in Construction

Generative Design in Building Information Modelling (BIM): Approaches and Requirements

Mechanical Performance Prediction for Sustainable High-Strength Concrete Using Bio-Inspired Neural Network

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