A Geochemical Model for Analyzing the Mechanism of Stabilized Soil Incorporating Natural Pozzolan, Cement and Lime

Vu, Ba Thao; Tran, Van Quan; Nguyen, Quoc Dung; Ngo, Anh Quan; Nguyen, Huu Nam; Nguyen, Huy Vuong; Zhu, Hehua

doi:10.1007/978-3-319-97112-4_191

Cited by 2 publications

(4 citation statements)

References 3 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Using the ANN model [4][5][6][7][8][9][10][11][12], the prediction of stabilized dredged sediments compressive strength for training and testing part is shown in Figures 5(a) and 5(b) , respectively. Figure 5 shows that the ANN model's results and experimental results are almost identical for each sample in the training phase.…”

Section: Resultsmentioning

confidence: 99%

“…In addition, to confirm and verify the performance of the ANN model, an artificial neural network (ANN) was created and adjusted by back-propagation algorithm (BP) with Levenberg-Marquardt (LMA) algorithm. e compressive strength of stabilized dredged sediments has been predicted by ANN models with network structure [4][5][6][7][8][9][10][11][12]. e results show that the ANN model can accurately predict the compressive strength of stabilized dredged sediments with low water content.…”

Section: Resultsmentioning

confidence: 99%

“…Stabilization/solidification is the improvement of the physical dredged sediments properties such as compressive strength, liquid limit, plastic limit, viscosity, and permeability [1][2][3][4][5]. Some binders are commonly used such as Portland cement, lime, limestone, fly ash, slag, gypsum, phosphorus, and many other commercial products.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Compressive Strength Prediction of Stabilized Dredged Sediments Using Artificial Neural Network

Tran

2021

Advances in Civil Engineering

View full text Add to dashboard Cite

Stabilized dredged sediments are used as a backfilling material to reduce construction costs and a solution to environmental protection. Therefore, the compressive strength is an important criterion to determine the stabilized dredged sediments application such as road construction, building construction, and highway construction. Using the traditional method such as empirical approach and experimental methods, the determination of compressive strength of stabilized dredged sediments is difficult due to the complexity of this composite material. In this investigation, the artificial neural network (ANN) model is introduced to forecast the compressive strength. To perform the simulation, 51 experimental datasets were collected from the literature. The dataset consists of 4 input variables (water content, cement content, air foam content, and waste fishing net content) and output variable (compressive strength). Evaluation of the models was made and compared on training dataset (70% data) and testing dataset (30% remaining data) by the criteria of Pearson’s correlation coefficient (R), Mean Absolute Error (MAE), and Root Mean Square Error (RMSE). The results show that the ANN model can accurately predict the compressive strength of stabilized dredged sediments with low water content. The cement content is the most important input affecting the unconfined compressive strength. The important input affecting the unconfined compressive strength can be in the following order: cement content > air foam content > water content > waste fishing net.

show abstract

Section: Resultsmentioning

confidence: 99%

Section: Resultsmentioning

confidence: 99%

See 1 more Smart Citation

Compressive Strength Prediction of Stabilized Dredged Sediments Using Artificial Neural Network

Tran

2021

Advances in Civil Engineering

View full text Add to dashboard Cite

show abstract

“…In general, the increase of UCS by adding different compounds can be attributed to short-and long-term reactions. Pozzolanic reactions lead to the formation of calcium silicate hydrate (CSH) and calcium aluminate hydrate (CAH) gels that fill the voids and thus increase the UCS of the samples [51,52]. Based on the Figures 6 and 7, the optimum value of nano-ZnO and nano-SiO 2 in combination with 6% lime was reported at 1.5 and 2%, respectively.…”

Section: Effect Of Nanospheres On Unconfined Compressivementioning

confidence: 98%

Effect of Selected Nanospheres on the Mechanical Strength of Lime-Stabilized High-Plasticity Clay Soils

Ghorbani

Hasanzadehshooiili

Mohammadi

et al. 2019

Advances in Civil Engineering

View full text Add to dashboard Cite

The proper design of protective structures may start from improving the characteristics of soils. In order to obtain reasonable safety criteria, several research studies have recently been dedicated to enhancing complex civil engineering structural systems with the use of nanotechnology. Thus, the following paper investigates the effect of nanospheres, including nanosilica (nano-SiO2) and nano zinc oxide (nano-ZnO), on lime-stabilized high-plasticity clay soil. For this purpose, unconfined compressive strength (UCS) and California bearing ratio (CBR) tests were performed on samples. The results showed that the use of the selected nanospheres greatly increased the UCS of the samples compared to untreated soil. The UCS value of samples containing 6% lime and 1.5% nano-ZnO after 28 days of treatment increased by 5-fold compared to the UCS of untreated samples. In addition, the samples containing 6% lime and 2% nano-SiO2, with similar curing conditions, experienced a 5.3-fold increase in their UCS value compared to the untreated samples. These compounds were considered as the optimal amounts and showed the highest mechanical strength in both UCS and CBR tests. The same trend was achieved in the CBR test, in which the CBR value for the optimal mixtures containing nano-ZnO and nano-SiO2 was 14.8 and 16.6 times higher than that of high-plasticity clay soil, respectively. Finally, the results obtained from scanning electron microscopy (SEM) analysis revealed that the nanospheres caused a dense and compact matrix to form in the soil, which led to the enhancement of the mechanical strength of the treated samples.

show abstract

A Geochemical Model for Analyzing the Mechanism of Stabilized Soil Incorporating Natural Pozzolan, Cement and Lime

Cited by 2 publications

References 3 publications

Compressive Strength Prediction of Stabilized Dredged Sediments Using Artificial Neural Network

Compressive Strength Prediction of Stabilized Dredged Sediments Using Artificial Neural Network

Effect of Selected Nanospheres on the Mechanical Strength of Lime-Stabilized High-Plasticity Clay Soils

Contact Info

Product

Resources

About