Response surface modelling of monte carlo fire data

Hasofer, A. M.; Qu, Jianlong

doi:10.1016/s0379-7112(02)00028-0

Cited by 17 publications

(11 citation statements)

References 21 publications

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“…It is usually used to establish a mapping relationship between inputs and outputs which are already known by the approach of data fitting, as given in Ref. [74].…”

Section: Ace Modeling Ace Nonparametric Regression Tech-mentioning

confidence: 99%

Compressive Strength Prediction of Alkali‐Activated Slag Concretes by Using Artificial Neural Network (ANN) and Alternating Conditional Expectation (ACE)

Qin

Guo

et al. 2022

Advances in Civil Engineering

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Compressive strength of alkali-activated slag (AAS) concrete is influenced by multi-factors in a nonlinear way. Both artificial neural network (ANN) and alternating conditional expectation (ACE) models of 3-day (3 d) and 28-day (28 d) compressive strength of AAS were established in this study by using the data reported in related literature, where alkali concentration of activator (Na2O%), modulus of activator (Ms), water/binder ratio (W/B), surface area of slag (SA), and basicity index of slag (Kb) were taken as input parameters. The models were employed later to predict 3 d and 28 d compressive strength of AAS concretes, respectively, and the results were validated by experimental work. The results show that both the ANN and the ACE models had adequate accuracy, no matter 3 d or 28 d compressive strength was considered. Compared to the 3 d compressive strength, due to data scattering that increased with the increase of data size, both the models did not yield a higher accuracy in the case of 28 d strength. However, also due to the increase in data size, both the models were more feasible to implement 28 d strength prediction as a result of sufficient learning and training during modeling. In addition, based on ACE analysis, the weight-influencing compressive strength of AAS decreased in a sequence of Na2O% > Ms > W/B > Kb > SA. If data size was sufficiently large, it was more suitable to establish an ANN model for compressive strength prediction of AAS concretes. Otherwise, ACE could be considered as an alternative to yield an acceptable result.

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“…It is usually used to establish a mapping relationship between inputs and outputs which are already known by the approach of data fitting, as given in Ref. [74].…”

Section: Ace Modeling Ace Nonparametric Regression Tech-mentioning

confidence: 99%

Compressive Strength Prediction of Alkali‐Activated Slag Concretes by Using Artificial Neural Network (ANN) and Alternating Conditional Expectation (ACE)

Qin

Guo

et al. 2022

Advances in Civil Engineering

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“…Because the relationship was not directly determined between the apparent diffusion coefficient for OH − for concrete and water-cement ratio, the soaking solution's pH value and cement proportion of C/(C + FA + CA) required an analysis by ACE (alternating conditional expectation) regression [27] of nonparametric regression where x 1 , x 2 , x 3 , and y are water-cement ratio, soaking solution's pH value, cement proportion, and ln(D OH − × 10 13 ), respectively. Searching for the transformation relations of φ 1 (x 1 ), φ 2 (x 2 ), φ 3 (x 3 ), and θ(y) which can satisfy the following mapping relation between input parameters of x 1 , x 2 , x 3 , and function of y gave…”

Section: Effects Of Water-cement Ratio Soaking Solution's Ph Value mentioning

confidence: 99%

Investigation on the Sulfuric Acid Corrosion Mechanism for Concrete in Soaking Environment

Min

Zhang

2018

Advances in Materials Science and Engineering

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is paper chooses the apparent diffusion coefficient for OH − (hydroxyl ion) for concrete as an index to evaluate the corrosion degree of concrete subjected to sulfuric acid. Based on the reaction boundary layer theory, a sulfuric acid corrosion model for concrete was established and verified through experiments. e experiment design and data processing of sulfuric acid corrosion tests for concrete were carried out using uniform test design and nonparametric regression. Effects of water-cement ratio and pH value are presented on the sulfuric acid corrosion mechanism for concrete. Test results show that when the pH value was 2.50, the sulfuric acid corrosion degree of concrete was the most serious. e boundary layer effect always existed in the sulfuric acid corrosion for concrete, and the corrosion process included rapid and stable corrosion stages. e apparent diffusion coefficient for OH − for concrete increased with the decrease of pH value and the increase of water-cement ratio and cement proportion.

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“…In the studies of Hasfer [6] and Song [2], the ACE regression method provided by the statistic software S-plus [7] is introduced to fit the RS function.…”

Section: Ace Regression Technologymentioning

confidence: 99%

Soil Nailing Optimization Design Based on Improved Response Surface

Qiu

Song

2013

AMR

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Gradient algorithm is difficult to obtain explicit analytic function of the optimization model, at the same time heuristic algorithm is computationally intensive with low speed and less efficient in soil nailing optimization. To overcome these problems, a new optimization method based on improved response surface (IRS) which constructed by uniform design (UD) and non-parametric regression (NR), is proposed. The soil nailing optimization is adopted by the combination of explicit analytic model based on IRS and composing program. The optimization process is explained and a soil nailing is optimized to verify the feasibility of the proposed method. The optimum results show that the introduction of UD and NR to construct the IRS calculate fast, do not need solving the specific analytic solution and can obtain global optimal solution.

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Response surface modelling of monte carlo fire data

Cited by 17 publications

References 21 publications

Compressive Strength Prediction of Alkali‐Activated Slag Concretes by Using Artificial Neural Network (ANN) and Alternating Conditional Expectation (ACE)

Compressive Strength Prediction of Alkali‐Activated Slag Concretes by Using Artificial Neural Network (ANN) and Alternating Conditional Expectation (ACE)

Investigation on the Sulfuric Acid Corrosion Mechanism for Concrete in Soaking Environment

Soil Nailing Optimization Design Based on Improved Response Surface

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