Effect of Axial Compression Ratio on Seismic Behavior of GFRP Reinforced Concrete Columns

Sun, Li; Yang, Zeyu; Jin, Qiao; Yan, Weidong

doi:10.1142/s0219455420400040

Cited by 56 publications

(10 citation statements)

References 20 publications

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“…In the United States, around 40% of bridges are over 50 years old, and more than 9% of them are rated as structurally deficient, which would draw a total cost for bridge rehabilitation of around $123 billion [3]. In addition to the need to design more robust structures under various loads [4,5], efficient structural monitoring is also important for aging infrastructure. Accurate structural health assessments are the basis for the decision-making of infrastructure maintenance, repair and rehabilitation.…”

Section: Introductionmentioning

confidence: 99%

Deep Learning-Based Applications for Safety Management in the AEC Industry: A Review

et al. 2021

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Safety is an essential topic to the architecture, engineering and construction (AEC) industry. However, traditional methods for structural health monitoring (SHM) and jobsite safety management (JSM) are not only inefficient, but also costly. In the past decade, scholars have developed a wide range of deep learning (DL) applications to address automated structure inspection and on-site safety monitoring, such as the identification of structural defects, deterioration patterns, unsafe workforce behaviors and latent risk factors. Although numerous studies have examined the effectiveness of the DL methodology, there has not been one comprehensive, systematic, evidence-based review of all individual articles that investigate the effectiveness of using DL in the SHM and JSM industry to date, nor has there been an examination of this body of evidence in regard to these methodological problems. Therefore, the objective of this paper is to disclose the state of the art of current research progress and determine the relevant gaps, challenges and future work. Methodically, CiteSpace was employed to summarize the research trends, advancements and frontiers of DL applications from 2010 to 2020. Next, an application-focused literature review was conducted, which led to a summary of research gaps, recommendations and future research directions. Overall, this review gains insight into SHM and JSM and aims to help researchers formulate more types of effective DL applications which have not been addressed sufficiently for the time being.

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Section: Introductionmentioning

confidence: 99%

Deep Learning-Based Applications for Safety Management in the AEC Industry: A Review

et al. 2021

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“…According to the size of the sample, the expansive soil was tested by a laser particle size analyzer, and the iron tailing sand was tested by the sieving method. It can be seen from the figure that the particle size of expansive soil is relatively small and the distribution range is mainly 2-10 μm, while the particle size of ITS is relatively large, mainly 100-1000 μm [40][41][42][43].…”

Section: Methodsmentioning

confidence: 99%

Study on Mechanical Properties of the Expansive Soil Treated with Iron Tailings Sand

Chu

Zhang

et al. 2021

Advances in Civil Engineering

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Aiming at researching shear strength parameters of expansive soil modified by industrial waste iron tailings sand, the enhancement of expansive soil is explored from macroscopic and microscopic aspects. After characterization and testing by various means, the results show that expansive soil modified by iron tailings sand will increase the maximum dry density of the improved soil and reduce its optimal moisture content, which is beneficial in tuning the moisture content at the construction site. In addition, iron tailings sand can improve the shear strength of expansive soils. The influence of iron tailings sand on cohesion increases first, then decreases, and reaches the peak value at 30%, while the effect on internal friction angle exhibits a continuously increasing trend. Furthermore, according to mercury intrusion tests and microangle analysis, the addition of iron tailings sand can reduce the tiny pores and enhance the occlusal force of the soil. Simultaneously, it increases the number of large pores, maximizing the macroscopic strengthening of iron tailings sand towards the expansive soil.

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“…[ 26 , 29 , 41 ]. They are scripted by designers in a computer recognizable format to control the design exploration, iteration, or evolution [ 42 , 43 , 44 ]. As for scripting, design constraints are mostly manual scripted; however, research has started to develop automatic methods.…”

Section: Generative Design and Building Information Modellingmentioning

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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Effect of Axial Compression Ratio on Seismic Behavior of GFRP Reinforced Concrete Columns

Cited by 56 publications

References 20 publications

Deep Learning-Based Applications for Safety Management in the AEC Industry: A Review

Deep Learning-Based Applications for Safety Management in the AEC Industry: A Review

Study on Mechanical Properties of the Expansive Soil Treated with Iron Tailings Sand

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

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