Seismic behavior of an innovative hybrid beam-column connection for precast concrete structures

Ye, M.; Jiang, Jin; Chen, H.M.; Zhou, Hong; Song, Danxia

doi:10.1016/j.engstruct.2020.111436

Cited by 65 publications

(12 citation statements)

References 16 publications

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“…Many researchers have worked on the application of cement replacements, such as fly ash, silica fume and slag, in concrete mixes [1][2][3][4][5]. The effect of natural powders, such as pumice, rice husk ash (RHA) and perlite on concrete properties, have been also investigated [6][7][8][9]. Incorporating concrete with pumice led to a higher strength-to-weight ratio in comparison with concrete with cement [10][11][12][13][14][15].…”

Section: Introductionmentioning

confidence: 99%

Application of Artificial Intelligence to Evaluate the Fresh Properties of Self-Consolidating Concrete

et al. 2021

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This paper numerically investigates the required superplasticizer (SP) demand for self-consolidating concrete (SCC) as a valuable information source to obtain a durable SCC. In this regard, an adaptive neuro-fuzzy inference system (ANFIS) is integrated with three metaheuristic algorithms to evaluate a dataset from non-destructive tests. Hence, five different non-destructive testing methods, including J-ring test, V-funnel test, U-box test, 3 min slump value and 50 min slump (T50) value were performed. Then, three metaheuristic algorithms, namely particle swarm optimization (PSO), ant colony optimization (ACO) and differential evolution optimization (DEO), were considered to predict the SP demand of SCC mixtures. To compare the optimization algorithms, ANFIS parameters were kept constant (clusters = 10, train samples = 70% and test samples = 30%). The metaheuristic parameters were adjusted, and each algorithm was tuned to attain the best performance. In general, it was found that the ANFIS method is a good base to be combined with other optimization algorithms. The results indicated that hybrid algorithms (ANFIS-PSO, ANFIS-DEO and ANFIS-ACO) can be used as reliable prediction methods and considered as an alternative for experimental techniques. In order to perform a reliable analogy of the developed algorithms, three evaluation criteria were employed, including root mean square error (RMSE), Pearson correlation coefficient (r) and determination regression coefficient (R2). As a result, the ANFIS-PSO algorithm represented the most accurate prediction of SP demand with RMSE = 0.0633, r = 0.9387 and R2 = 0.9871 in the testing phase.

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Section: Introductionmentioning

confidence: 99%

Application of Artificial Intelligence to Evaluate the Fresh Properties of Self-Consolidating Concrete

et al. 2021

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“…Petrella et al [14] conducted the mechanical test on mortar containing waste porous glass and found the good adhesion of the glass to the cement paste through microstructural observations. Furthermore, the addition of admixtures, such as silica fume, fly ash, lithium compounds, and metakaolin was useful in preventing deleterious ASR expansion [15,16]. Adhikary et al [17] also illustrated that expanded glass produced by mixing waste glass and expansive agents was effective in prohibiting the ASR expansion.…”

Section: Introductionmentioning

confidence: 99%

Machine-Learning-Aided Prediction of Flexural Strength and ASR Expansion for Waste Glass Cementitious Composite

et al. 2021

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Waste glass (WG) is unsustainable due to its nonbiodegradable property. However, its main ingredient is silicon dioxide, which can be utilised as a supplementary cementitious material. Before reusing WG, the flexural strength (FS) and alkali–silica reaction (ASR) expansion of WG concrete are two essential properties that must be investigated. This study produced mortar containing activated glass powder using mechanical, chemical, and mechanical–chemical (combined) approaches. The results showed that mortar containing 30% WG powder using the combined method was optimal for improving the FS and mitigating the ASR expansion. The microstructure analysis was implemented to explore the activation effect on the glass powder and mortar. Moreover, a random forest (RF) model was proposed with hyperparameters tuned by beetle antennae search (BAS), aiming at predicting FS and ASR expansion precisely. A large database was established from the experimental results based on 549 samples prepared for the FS test and 183 samples produced for the expansion test. The BAS-RF model presented high correlation coefficients for both FS (0.9545) and ASR (0.9416) data sets, showing much higher accuracy than multiple linear regression and logistic regression. Finally, a sensitivity analysis was conducted to rank the variables based on importance. Apart from the curing time, the particle granularity and content of WG were demonstrated to be the most sensitive variable for FS and expansion, respectively.

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“…With the rising competition in the construction industry nowadays, construction corporations' profits have largely reduced, however, project owners' demands are increasing both in quality and efficiency aspects [1]. The construction's progress is an essential part of the construction technologies improvement [2][3][4]. Once project delay or rework occurs due to unsound construction management, it may result in profit decrease or even direct financial and credibility loss.…”

Section: Introductionmentioning

confidence: 99%

Automatic Scaffolding Workface Assessment for Activity Analysis through Machine Learning

et al. 2021

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Scaffolding serves as one construction trade with high importance. However, scaffolding suffers from low productivity and high cost in Australia. Activity Analysis is a continuous procedure of assessing and improving the amount of time that craft workers spend on one single construction trade, which is a functional method for monitoring onsite operation and analyzing conditions causing delays or productivity decline. Workface assessment is an initial step for activity analysis to manually record the time that workers spend on each activity category. This paper proposes a method of automatic scaffolding workface assessment using a 2D video camera to capture scaffolding activities and the model of key joints and skeleton extraction, as well as machine learning classifiers, were used for activity classification. Additionally, a case study was conducted and showed that the proposed method is a feasible and practical way for automatic scaffolding workface assessment.

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Seismic behavior of an innovative hybrid beam-column connection for precast concrete structures

Cited by 65 publications

References 16 publications

Application of Artificial Intelligence to Evaluate the Fresh Properties of Self-Consolidating Concrete

Application of Artificial Intelligence to Evaluate the Fresh Properties of Self-Consolidating Concrete

Machine-Learning-Aided Prediction of Flexural Strength and ASR Expansion for Waste Glass Cementitious Composite

Automatic Scaffolding Workface Assessment for Activity Analysis through Machine Learning

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