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Fulltext Effects of using pozzolan and Portland cement in the treatment of dispersive clay

Vakili AH, Selamat MR, Moayedi H

ScientificWorldJournal, 2013;2013:547615.
PMID: 23864828 DOI: 10.1155/2013/547615

Use of dispersive clay as construction material requires treatment such as by chemical addition. Treatments to dispersive clay using pozzolan and Portland cement, singly and simultaneously, were carried out in this study. When used alone, the optimum amount of pozzolan required to treat a fully dispersive clay sample was 5%, but the curing time to reduce dispersion potential, from 100% to 30% or less, was 3 month long. On the other hand, also when used alone, a 3% cement content was capable of reducing dispersion potential to almost zero percent in only 7 days; and a 2% cement content was capable of achieving similar result in 14 days. However, treatment by cement alone is costly and could jeopardize the long term performance. Thus, a combined 5% pozzolan and 1.5% cement content was found capable of reducing dispersion potential from 100% to zero percent in 14 days. The results indicate that although simultaneous treatment with pozzolan and cement would extend the required curing time in comparison to treatment by cement alone of a higher content, the task could still be carried out in a reasonable period of curing time while avoiding the drawbacks of using either pozzolan or cement alone.
Fulltext Investigating the effect of lignosulfonate on erosion rate of the embankments constructed with clayey sand

Koohpeyma HR, Vakili AH, Moayedi H, Panjsetooni A, Nazir R

ScientificWorldJournal, 2013;2013:587462.
PMID: 24459437 DOI: 10.1155/2013/587462

Internal erosion is known as the most important cause of dam failure after overtopping. It is important to improve the erosion resistance of the erodible soil by selecting an effective technique along with the reasonable costs. To prevent internal erosion of embankment dams the use of chemical stabilizers that reduce the soil erodibility potential is highly recommended. In the present study, a lignin-based chemical, known as lignosulfonate, is used to improve the erodibility of clayey sand specimen. The clayey sand was tested in various hydraulic heads in terms of internal erosion in its natural state as well as when it is mixed with the different percentages of lignosulfonate. The results show that erodibility of collected clayey sand is very high and is dramatically reduced by adding lignosulfonate. Adding 3% of lignosulfonate to clayey sand can reduce the coefficient of soil erosion from 0.01020 to 0.000017. It is also found that the qualitative erodibility of stabilized soil with 3% lignosulfonate is altered from the group of extremely rapid to the group of moderately slow.
Fulltext Spatial Landslide Susceptibility Assessment Based on Novel Neural-Metaheuristic Geographic Information System Based Ensembles

Moayedi H, Osouli A, Tien Bui D, Foong LK

Sensors (Basel), 2019 Oct 29;19(21).
PMID: 31671801 DOI: 10.3390/s19214698

Regular optimization techniques have been widely used in landslide-related problems. This paper outlines two novel optimizations of artificial neural network (ANN) using grey wolf optimization (GWO) and biogeography-based optimization (BBO) metaheuristic algorithms in the Ardabil province, Iran. To this end, these algorithms are synthesized with a multi-layer perceptron (MLP) neural network for optimizing its computational parameters. The used spatial database consists of fourteen landslide conditioning factors, namely elevation, slope aspect, land use, plan curvature, profile curvature, soil type, distance to river, distance to road, distance to fault, rainfall, slope degree, stream power index (SPI), topographic wetness index (TWI) and lithology. 70% of the identified landslides are randomly selected to train the proposed models and the remaining 30% is used to evaluate the accuracy of them. Also, the frequency ratio theory is used to analyze the spatial interaction between the landslide and conditioning factors. Obtained values of area under the receiver operating characteristic curve, as well as mean square error and mean absolute error showed that both GWO and BBO hybrid algorithms could efficiently improve the learning capability of the MLP. Besides, the BBO-based ensemble surpasses other implemented models.
Fulltext A Novel Swarm Intelligence-Harris Hawks Optimization for Spatial Assessment of Landslide Susceptibility

Bui DT, Moayedi H, Kalantar B, Osouli A, Pradhan B, Nguyen H, et al.

Sensors (Basel), 2019 Aug 17;19(16).
PMID: 31426552 DOI: 10.3390/s19163590

In this research, the novel metaheuristic algorithm Harris hawks optimization (HHO) is applied to landslide susceptibility analysis in Western Iran. To this end, the HHO is synthesized with an artificial neural network (ANN) to optimize its performance. A spatial database comprising 208 historical landslides, as well as 14 landslide conditioning factors-elevation, slope aspect, plan curvature, profile curvature, soil type, lithology, distance to the river, distance to the road, distance to the fault, land cover, slope degree, stream power index (SPI), topographic wetness index (TWI), and rainfall-is prepared to develop the ANN and HHO-ANN predictive tools. Mean square error and mean absolute error criteria are defined to measure the performance error of the models, and area under the receiving operating characteristic curve (AUROC) is used to evaluate the accuracy of the generated susceptibility maps. The findings showed that the HHO algorithm effectively improved the performance of ANN in both recognizing (AUROCANN = 0.731 and AUROCHHO-ANN = 0.777) and predicting (AUROCANN = 0.720 and AUROCHHO-ANN = 0.773) the landslide pattern.
Fulltext The impact of physical exercise on the fatigue symptoms in patients with multiple sclerosis: a systematic review and meta-analysis

Razazian N, Kazeminia M, Moayedi H, Daneshkhah A, Shohaimi S, Mohammadi M, et al.

BMC Neurol, 2020 Mar 13;20(1):93.
PMID: 32169035 DOI: 10.1186/s12883-020-01654-y

BACKGROUND: Despite many benefits of the physical activity on physical and mental health of patients with Multiple Sclerosis (MS), the activity level in these patients is still very limited, and they continue to suffer from impairment in functioning ability. The main aim of this study is thus to closely examine exercise's effect on fatigue of patients with MS worldwide, with particular interest on Iran based on a comprehensive systematic review and meta-analysis.
METHODS: The studies used in this systematic review were selected from the articles published from 1996 to 2019, in national and international databases including SID, Magiran, Iranmedex, Irandoc, Google Scholar, Cochrane, Embase, ScienceDirect, Scopus, PubMed and Web of Science (ISI). These databases were thoroughly searched, and the relevant ones were selected based on some plausible keywords to the aim of this study. Heterogeneity index between studies was determined using Cochran's test and I2. Due to heterogeneity in studies, the random effects model was used to estimate standardized mean difference.
RESULTS: From the systematic review, a meta-analysis was performed on 31 articles which were fulfilled the inclusion criteria. The sample including of 714 subjects was selected from the intervention group, and almost the same sample size of 720 individuals were selected in the control group. Based on the results derived from this meta-analysis, the standardized mean difference between the intervention group before and after the intervention was respectively estimated to be 23.8 ± 6.2 and 16.9 ± 3.2, which indicates that the physical exercise reduces fatigue in patients with MS.
CONCLUSION: The results of this study extracted from a detailed meta-analysis reveal and confirm that physical exercise significantly reduces fatigue in patients with MS. As a results, a regular exercise program is strongly recommended to be part of a rehabilitation program for these patients.
Predicting the sorption efficiency of heavy metal based on the biochar characteristics, metal sources, and environmental conditions using various novel hybrid machine learning models

Ke B, Nguyen H, Bui XN, Bui HB, Choi Y, Zhou J, et al.

Chemosphere, 2021 Aug;276:130204.
PMID: 34088091 DOI: 10.1016/j.chemosphere.2021.130204

Heavy metals in water and wastewater are taken into account as one of the most hazardous environmental issues that significantly impact human health. The use of biochar systems with different materials helped significantly remove heavy metals in the water, especially wastewater treatment systems. Nevertheless, heavy metal's sorption efficiency on the biochar systems is highly dependent on the biochar characteristics, metal sources, and environmental conditions. Therefore, this study implicates the feasibility of biochar systems in the heavy metal sorption in water/wastewater and the use of artificial intelligence (AI) models in investigating efficiency sorption of heavy metal on biochar. Accordingly, this work investigated and proposed 20 artificial intelligent models for forecasting the sorption efficiency of heavy metal onto biochar based on five machine learning algorithms and bagging technique (BA). Accordingly, support vector machine (SVM), random forest (RF), artificial neural network (ANN), M5Tree, and Gaussian process (GP) algorithms were used as the key algorithms for the aim of this study. Subsequently, the individual models were bagged with each other to generate new ensemble models. Finally, 20 intelligent models were developed and evaluated, including SVM, RF, M5Tree, GP, ANN, BA-SVM, BA-RF, BA-M5Tree, BA-GP, BA-ANN, SVM-RF, SVM-M5Tree, SVM-GP, SVM-ANN, RF-M5Tree, RF-GP, RF-ANN, M5Tree-GP, M5Tree-ANN, GP-ANN. Of those, the hybrid models (i.e., BA-SVM, BA-RF, BA-M5Tree, BA-GP, BA-ANN, SVM-RF, SVM-M5Tree, SVM-GP, SVM-ANN, RF-M5Tree, RF-GP, RF-ANN, M5Tree-GP, M5Tree-ANN, GP-ANN) are introduced as the novelty of this study for estimating the heavy metal's sorption efficiency on the biochar systems. Also, the biochar characteristics, metal sources, and environmental conditions were comprehensively assessed and used, and they are considered as a novelty of the study as well. For this aim, a dataset of sorption efficiency of heavy metal was collected and processed with 353 experimental tests. Various performance indexes were applied to evaluate the models, such as RMSE, R2, MAE, color intensity, Taylor diagram, box and whiskers plots. This study's findings revealed that AI models could predict heavy metal's sorption efficiency onto biochar with high reliability, and the efficiency of the ensemble models is higher than those of individual models. The results also reported that the SVM-ANN ensemble model is the most superior model among 20 developed models. The predictive model proposed that heavy metal's efficiency sorption on biochar can be accurately forecasted and early warning for the water pollution by heavy metal.