Model fitting to concrete carbonation data with non-zero initial carbonation depth

Forsdyke, Jessica C.; Lees, Janet M.

doi:10.1617/s11527-023-02104-0

Cited by 14 publications

(4 citation statements)

References 23 publications

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“…It used to estimate dependent dichotomous variable. But linear probability model is the simplest method, it is not logically attractive model & it has some econometric problems like non-normality of the disturbances (Ui), heteroscedastic variances of the disturbances, and non-ful llment to measure of goodness of t. Therefore, linear probability model is not appropriate to test the statistical signi cance of estimated coe cients (Forsdyke & Lees, 2023) logit model guarantee that the estimated probabilities but that never steps outside the 0-1 interval. Thus, a binary logistic model is used to identify the determinants of agroforestry practices and to assess determine the probability of agroforestry practice (Bekuma et al, 2023).…”

Section: Methods Of Data Analysismentioning

confidence: 99%

Determinants of the Agroforestry Practices in Mana Districts, Oromyia Region, Ethiopia

Bekele

Yeshiwas

kassie

et al. 2023

Preprint

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Due to little adoption of the agroforestry practices, land degradation has become a serious pressing problem in various parts of the world in general and in study area in particular. Therefore, these studies aim to assess the determents of the agroforestry practices in the study area. Therefore, based on multi-stage sampling techniques 184 randomly selected sample households’ heads was determined. Descriptive and inferential statistics such as T-test, chi-square tests, and binary logit model was used to compare the mean difference between adopters and non-adopters households. The finding the study shows Senegal (L. Britton), home garden, and boundary planting are the most common agroforestry practices in the study area. The mean annual income for adopter farm households' heads was 1148743.00 birr, and for non-adopters, was 138675.00 birr. This implies that agroforestry practices make a significant contribution to the adopter's income. However, lack of farmland (27.7%), pests (16.3%), and low market access (15.2%) are major constraints that determine the adoption of the agroforestry practices in the study area. The results of the binary logistic model specify that age, farm size, and distance negatively affect the adoption of agroforestry practices in the study area. While perception were positively and significantly influence adoption of agroforestry practices in the study area, Therefore, the governmental & non-governmental organizations should have to develop new land policies to remove barriers to land access, tree tenure & an array of exotic tree species should be required to resist pests and drought conditions in study area.

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Section: Methods Of Data Analysismentioning

confidence: 99%

Determinants of the Agroforestry Practices in Mana Districts, Oromyia Region, Ethiopia

Bekele

Yeshiwas

kassie

et al. 2023

Preprint

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“…Forsdyke and Lees [76] evaluated the effect of preconditioning on the rate of carbonation. Four different curing regimes were applied.…”

Section: Effect Of Relative Humidity During Pre-conditioning On Carbo...mentioning

confidence: 99%

Report of RILEM TC 281-CCC: Insights into factors affecting the carbonation rate of concrete with SCMs revealed from data mining and machine learning approaches

Vollpracht,

Gluth,

Rogiers

et al. 2024

Preprint

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The RILEM TC 281–CCC ‘‘Carbonation of concrete with supplementary cementitious materials’’ conducted a study on the effects of supplementary cementitious materials (SCMs) on the carbonation rate of blended cement concretes and mortars. In this context, a comprehensive database has been established, consisting of 1044 concrete and mortar mixes with their associated carbonation depth data over time. The dataset comprises mix designs with a large variety of binders with up to 94% SCMs, collected from the literature as well as unpublished testing reports. The data includes chemical composition and physical properties of the raw materials, mix-designs, compressive strengths, curing and carbonation testing conditions. Natural carbonation was recorded for several years in many cases with both indoor and outdoor results. The database has been analysed to investigate the effects of binder composition and mix design, curing and preconditioning, and relative humidity on the carbonation rate. Furthermore, the accuracy of accelerated carbonation testing as well as possible correlations between compressive strength and carbonation resistance were evaluated. The analysis revealed that the w/CaOreactive ratio is a decisive factor for carbonation resistance, while curing and exposure conditions also influence carbonation. Under natural exposure conditions, the carbonation data exhibit significant variations. Nevertheless, probabilistic inference suggests that both accelerated and natural carbonation processes follow a square-root-of-time behavior, though accelerated and natural carbonation cannot be converted into each other without corrections. Additionally, a machine learning technique was employed to assess the influence of parameters governing the carbonation progress in concretes.

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Section: Introductionmentioning

confidence: 99%

“…It is possible to effectively evaluate the structural performance of concrete by predicting the degree of carbonation. This can further extend the structure's service life and protect the safety of relevant users [10][11][12][13]. ( ) Over the past century, some countries have made significant efforts to enhance the durability of concrete structures by undertaking extensive research and analysis on concrete carbonation.…”

Section: Introductionmentioning

confidence: 99%

A Review of Concrete Carbonation Depth Evaluation Models

Wang,

Yang,

Peng

et al. 2024

Coatings

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Carbonation is one of the critical issues affecting the durability of reinforced concrete. Evaluating the depth of concrete carbonation is of great significance for ensuring the quality and safety of construction projects. In recent years, various prediction algorithms have been developed for evaluating concrete carbonation depth. This article provides a detailed overview of the existing prediction models for concrete carbonation depth. According to the data processing methods used in the model, the existing prediction models can be divided into mathematical curve models and machine learning models. The machine learning models can be further divided into the following categories: artificial neural network model, decision tree model, support vector machine model, and combined models. The basic idea of the mathematical curve model is to directly establish the relationship between the carbonation depth and age of concrete by using certain function curves. The advantage of the mathematical curve model is that only a small amount of experimental data is needed for curve fitting, which is very convenient for engineering applications. The limitation of the curve model is that it can only consider the influence of some factors on the carbonation depth of concrete, and the prediction accuracy cannot be guaranteed. The advantage of using the machine learning model to predict the carbonation depth of concrete is that many factors can be considered at the same time. When there are sufficient experimental data, the trained machine learning model can give more accurate prediction results than the mathematical curve model. The main defect of the machine learning model is that it needs a lot of experimental data as training samples, so it is not as convenient as the mathematical curve model in engineering applications. A future research direction may be to combine a machine learning model with a mathematical curve model to evaluate the carbonation depth of concrete more accurately.

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Model fitting to concrete carbonation data with non-zero initial carbonation depth

Cited by 14 publications

References 23 publications

Determinants of the Agroforestry Practices in Mana Districts, Oromyia Region, Ethiopia

Determinants of the Agroforestry Practices in Mana Districts, Oromyia Region, Ethiopia

Report of RILEM TC 281-CCC: Insights into factors affecting the carbonation rate of concrete with SCMs revealed from data mining and machine learning approaches

A Review of Concrete Carbonation Depth Evaluation Models

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