Upscaling dispersivity for conservative solute transport in naturally fractured media

Jia, Sida; Dai, Zhenxue; Zhou, Zhichao; Ling, Hui; Yang, Zhengjian; Qi, Linlin; Wang, Zihao; Zhang, Xiaoying; Thanh, Hung Vo; Soltanian, Mohamad Reza

doi:10.1016/j.watres.2023.119844

Cited by 37 publications

(9 citation statements)

References 85 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Also, humidity has a significant effect on the engineering properties and stability of structures [41,42]. Clay minerals cause swelling and instability by absorbing water [41]. These minerals were very few in the studied samples.…”

Section: Geo-mechanical Properties Of Samplesmentioning

confidence: 92%

“…The type of cement and organic matter, the type and amount of minerals cause changes in the mechanical properties and stability of the slopes [39,40]. Also, humidity has a significant effect on the engineering properties and stability of structures [41,42]. Clay minerals cause swelling and instability by absorbing water [41].…”

Section: Geo-mechanical Properties Of Samplesmentioning

confidence: 99%

See 1 more Smart Citation

Prediction of Rock Tensile Strength Using Soft Computing and Statistical Methods

Zheng

Shen

Motahari

et al. 2023

Period. Polytech. Civil Eng.

View full text Add to dashboard Cite

The tensile strength of the rocks is one of the effective factors in the rupture of structure foundations and underground spaces, the stability of rocky slopes, and the ability to drill and explode in rocks. This research was conducted to estimate tensile strength using methods such as simple regression (SR), multivariate linear regression (MVLR), support vector regression (SVR) with radial basis kernel function, multilayer feed-forward artificial neural network (MFF-ANN), Gaussian process regression (GPR) using squared exponential kernel (SEK) function, and adaptive neuro-fuzzy inference system (ANFIS) based on Gaussian membership function. For this purpose, petrography, and engineering features of the limestone, sandstone, and argillaceous limestone samples in the south of Iran, were assessed. The results obtained from this study were compared with those of previous research, revealing a strong correlation (R2=0.95 to 1.00) between our findings and the published works. To estimate Brazilian tensile strength (BTS), the index properties including water absorption by weight, point load index (PLI), porosity%, P-wave velocity (Vp), and density were considered as inputs. Methods were compared using various criteria. The SVR precision (R=0.96) was higher than MFF-ANN (R=0.92), ANFIS (R=0.95), GPR (R=0.945), and MVLR (R=0.89) to estimate the tensile strength. The average BTS measured in the laboratory and predicted by all 5 methods is 6.62 and 6.71 MPa, respectively, which shows the very high precision of the investigated methods. Analysis of model criteria using statistical analysis for developed relationships revealed that there is sufficient accuracy to use the empirical equations.

show abstract

Section: Geo-mechanical Properties Of Samplesmentioning

confidence: 92%

Section: Geo-mechanical Properties Of Samplesmentioning

confidence: 99%

Prediction of Rock Tensile Strength Using Soft Computing and Statistical Methods

Zheng

Shen

Motahari

et al. 2023

Period. Polytech. Civil Eng.

View full text Add to dashboard Cite

show abstract

“…The main factors affecting transport properties in rock fractures include geometric heterogeneity, channel tortuosity, filling materials, and the presence of fracture contact areas (Jia et al., 2023; Stoll et al., 2019; Wang et al., 2020; Zheng et al., 2021; Zhou et al., 2023). Furthermore, both experimental and numerical research show that the complexity of solute transport processes within rock fractures intensifies as the applied hydraulic gradient increases (Crevacore et al., 2016; Dou et al., 2018; Nowamooz et al., 2013; Qian et al., 2011).…”

Section: Introductionmentioning

confidence: 99%

Can Geometric Parameters Enable Direct Prediction of Non‐Fickian Transport in Rock Fractures Across Diverse Flow Regimes?

Zhou,

Guo,

Jiao

et al. 2024

JGR Solid Earth

View full text Add to dashboard Cite

Anomalous solute migrations in fractured rocks are governed by geometric characteristics and flow regimes. Although existing inverse models can describe this behavior, the underlying physics for quantifying key transport coefficients remains largely unexplored. Here, we investigate the quantitative impacts of geometric heterogeneity and flow regimes on solute transport in rock fractures. We conduct numerical experiments to simulate water flow and conservative solute transport in 3D fractures with varying geometric features and Reynolds numbers. Our results show that the non‐Fickian transport is prevalent across the entire flow regime, with Darcy flows attributed to geometric heterogeneity and non‐Darcian flows influenced by additional eddy zones. We employ the mobile‐immobile (MIM) domain model and continuous time random walk (CTRW) model to inversely model simulated breakthrough curves. Inverse analyses demonstrate that both models effectively characterize anomalous transport behaviors. The fitted transport coefficients of the MIM model exhibit stronger quantitative relationships with aperture and roughness parameters, as well as Reynolds number, compared to the CTRW model. By incorporating parameterized transport coefficients, we propose physics‐ and statistics‐based models to directly predict anomalous transport behaviors under different flow regimes. These prediction models accurately reproduce solute transport processes of all simulated cases with acceptable errors. The feasibility of directly predicting solute transport under varying flow regimes using geometric information is thus validated. Our study not only supports the study of substance migration based on geometric structure features, but also serves as a foundation for investigating geological activities based on substance migration information.

show abstract

“…In recent years, the world of engineering has witnessed significant developments that have enabled experts to solve problems with higher accuracy and convenience [1][2][3]. These developments include a wide range of civil engineering domains such as geotechnical [4,5] and water [6,7] analysis. Focusing on the structural aspects of this field, engineers have benefitted from various technologies and simulation tools for analyzing the behavior of structures (and their particular elements) [8][9][10] under different loading conditions [11][12][13].…”

Section: Introductionmentioning

confidence: 99%

Estimating the Concrete Ultimate Strength Using a Hybridized Neural Machine Learning

Zhang

2023

Buildings

View full text Add to dashboard Cite

Concrete is a highly regarded construction material due to many advantages such as versatility, durability, fire resistance, and strength. Hence, having a prediction of the compressive strength of concrete (CSC) can be highly beneficial. The new generation of machine learning models has provided capable solutions to concrete-related simulations. This paper deals with predicting the CSC using a novel metaheuristic search scheme, namely the slime mold algorithm (SMA). The SMA retrofits an artificial neural network (ANN) to predict the CSC by incorporating the effect of mixture ingredients and curing age. The optimal configuration of the algorithm trained the ANN by taking the information of 824 specimens. The measured root mean square error (RMSE = 7.3831) and the Pearson correlation coefficient (R = 0.8937) indicated the excellent capability of the SMA in the assigned task. The same accuracy indicators (i.e., the RMSE of 8.1321 and R = 0.8902) revealed the competency of the developed SMA-ANN in predicting the CSC for 206 stranger specimens. In addition, the used method outperformed two benchmark algorithms of Henry gas solubility optimization (HGSO) and Harris hawks optimization (HHO) in both training and testing phases. The findings of this research pointed out the applicability of the SMA-ANN as a new substitute to burdensome laboratory tests for CSC estimation. Moreover, the provided solution is compared to some previous studies, and it is shown that the SMA-ANN enjoys higher accuracy. Therefore, an explicit mathematical formula is developed from this model to provide a convenient CSC predictive formula.

show abstract

Upscaling dispersivity for conservative solute transport in naturally fractured media

Cited by 37 publications

References 85 publications

Prediction of Rock Tensile Strength Using Soft Computing and Statistical Methods

Prediction of Rock Tensile Strength Using Soft Computing and Statistical Methods

Can Geometric Parameters Enable Direct Prediction of Non‐Fickian Transport in Rock Fractures Across Diverse Flow Regimes?

Estimating the Concrete Ultimate Strength Using a Hybridized Neural Machine Learning

Contact Info

Product

Resources

About