Comparison of Soil Classification Methods Using CPT Results

A series of cone penetration test were conducted in the southeast of Tehran to assess the liquefaction potential in this area. At the same time, after sounding of each cone penetration test, soil samples were also taken from different depths of boreholes to visually verify the soil classification. Seventy four samples from twenty boreholes were taken and their soil characteristics were obtained. To classify the soil layers, using recorded data, two various soil behaviour classification charts proposed by Robertson and Wride (1988), and Marr (1981) were examined which for some cases different results were obtained. In this paper validity of these procedures are investigated and discussed in details. These soil classification methods in some cases give a good results but there is a different between those charts and observed soil classification, particularly when the soil contain fines and therefore some modification must be applied

Investigation of Dynamic Behavior of Asphalt Core Dams

In this research, dynamic behavior of a rockfill dam with asphalt-concrete core has been studied utilizing numerical models and centrifuge model tests with material parameters determined by laboratory tests including static and cyclic triaxial tests and also wave velocity measurements. The case study selected is the Meyjaran asphalt core dam, recently constructed in Northern Iran, with 60 m height and 180 m crest length. The seismic response analyses have been performed using a non-linear three dimensional finite difference software under various hazard levels of earthquake loadings. Their results showed that the induced shear strains in the asphalt core are less than 1% during an earthquake with amax=0.25g and the asphalt core remains watertight. Also, the small scale physical models of the asphalt core dam have been tested on centrifuge, under impact loading and response accelerations and induced deformations were recorded by instruments installed within and on the models. The recorded data and observations of the centrifuge model tested at 80g acceleration showed that the induced deformations in the asphalt core under an impact load with a large acceleration of 7.6 m/s2 were very small. Comparing the results of centrifuge tests with the results of numerical dynamic analyses of a prototype dam indicated that the numerical results corresponded well with the data recorded during centrifuge tests

Three Dimensional Dynamic Analysis of Alborz Dam with Asphalt and Clay Cores

Alborz rockfill dam with a clay core is under construction in North of Iran, an area of heavy rainfall. Because of the difficulties in the construction of a clay core in a wet area, an alternative for the watertight element (asphalt core) was considered. During the design of Alborz dam, a dynamic response analysis of the asphalt core was performed using two-dimensional modeling based on the equivalent linear method. Considering the shortage of study on the seismic behavior of asphalt core dams and also the high level of risk of earthquakes in Iran, it was necessary that the dynamic behavior of this dam was studied using three-dimensional models. In this study, the dynamic response of Alborz dam for both variants of clay and asphalt cores has been investigated and three-dimensional dynamic (non-linear) analyses have been carried out using the explicit finite-difference program, FLAC3-D, under various hazard levels of earthquakes (DBL and MCL). The results obtained included: time histories of the response acceleration, displacement, shear stress and shear strains are presented in this paper. The dynamic response of the dam with a clay core and asphalt core are compared with each other

Modeling water flux in osmotic membrane bioreactor by adaptive network-based fuzzy inference system and artificial neural network.

Osmotic Membrane Bioreactor (OMBR) is an emerging technology for wastewater treatment with membrane fouling as a major challenge. This study aims to develop Adaptive Network-based Fuzzy Inference System (ANFIS) and Artificial Neural Network (ANN) models in simulating and predicting water flux in OMBR. Mixed liquor suspended solid (MLSS), electrical conductivity (EC) and dissolved oxygen (DO) were used as model inputs. Good prediction was demonstrated by both ANFIS models with R2 of 0.9755 and 0.9861, and ANN models with R2 of 0.9404 and 0.9817, for thin film composite (TFC) and cellulose triacetate (CTA) membranes, respectively. The root mean square error for TFC (0.2527) and CTA (0.1230) in ANFIS models was lower than in ANN models at 0.4049 and 0.1449. Sensitivity analysis showed that EC was the most important factor for both TFC and CTA membranes in ANN models, while EC (TFC) and MLSS (CTA) are key parameters in ANFIS models

Application of artificial neural network and multiple linear regression in modeling nutrient recovery in vermicompost under different conditions

© 2020 Elsevier Ltd Vermicomposting is one of the best technologies for nutrient recovery from solid waste. This study aims to assess the efficiency of Artificial Neural Network (ANN) and Multiple Linear Regression (MLR) models in predicting nutrient recovery from solid waste under different vermicompost treatments. Seven chemical and biological indices were studied as input variables to predict total nitrogen (TN) and total phosphorus (TP) recovery. The developed ANN and MLR models were compared by statistical analysis including R-squared (R2), Adjusted-R2, Root Mean Square Error and Absolute Average Deviation. The results showed that vermicomposting increased TN and TP proportions in final products by 1.5 and 16 times. The ANN models provided better prediction for TN and TP with R2 of 0.9983 and 0.9991 respectively, compared with MLR models with R2 of 0.834 and 0.729. TN and C/N ratio were key factors for TP and TN prediction by ANN with percentages of 17.76 and 18.33

Degradation of phenol using US/periodate/nZVI system from aqueous solutions

In the present work, the degradation of phenol from aqueous solutions was investigated using periodate/zero valent iron nanoparticle (nZVI) in the presence of ultrasound at a batch reactor. The Experimental tests were carried out using pre-designated concentrations of nZVI, periodate, and pH ranging from 1-7 mM, 0.5-5 mM, 3-11 respectively. During the all experimental tests the ultrasonic reactor was operated at a fix frequency (40 kHz), temperature (33±1) and power (350 W). The results of nZVI/periodate/ultrasound system on degradation of phenol showed that the removal efficiency was indeed affected by the amount of free radicals produced to initiate the oxidative decomposition of phenol. also, by increasing the nZVI loading to 3 mM and periodate concentration to 3 mM, the efficiency of phenol removal was increased. Besides, the acidic pH (pH = 3) was found to be more effective than neutral and alkaline pH in degradation of phenol. © 2019 Global NEST Printed in Greece. All rights reserved

Data on using macro invertebrates to investigate the biological integrity of permanent streams located in a semi-arid region

Abstract The aquatic ecosystems are continuously endangered due to variety of hazardous chemicals containing different toxic agents which can be emitted from anthropogenic sources. Besides the increasing of human population, various kinds of contaminants enter into the surface water resources. The aim of the present study was to investigate the abundance and diversity of macro invertebrates in two permanent streams located in the northern part of Tehran. The biological integrity of the streams was determined by manual sampling approach at five points. The distances between the sampling points were at least 2 km. The bio indicator organisms, organic pollution, and dissolved oxygen were measured. The different types of benthic invertebrates such as riffle beetle, midge and caddish fly larvae, dragon fly, may fly and stone fly nymph, riffle beetle adult, pyralid caterpillar, leech, and pouch snail were identified. It can be concluded that, the identified benthic macro invertebrates can be served as appropriate biological indicator in the studied area. Keywords Biological integrity Tehran Macro invertebrate

Artificial neural network (ANN) approach for modelling of pile settlement of open-ended steel piles subjected to compression load

This study was devoted to examine pile bearing capacity and to provide a reliable model to simulate pile load-settlement behaviour using a new artificial neural network (ANN) method. To achieve the planned aim, experimental pile load test were carried out on model open-ended steel piles, with pile aspect ratios of 12, 17, and 25. An optimised second-order Levenberg-Marquardt (LM) training algorithm has been used in this process. The piles were driven in three sand densities; dense, medium, and loose. A statistical analysis test was conducted to explore the relative importance and the statistical contribution (Beta and Sig) values of the independent variables on the model output. Pile effective length, pile flexural rigidity, applied load, sand-pile friction angle and pile aspect ratio have been identified to be the most effective parameters on model output. To demonstrate the effectiveness of the proposed algorithm, a graphical comparison was performed between the implemented algorithm and the most conventional pile capacity design approaches. The proficiency metric indicators demonstrated an outstanding agreement between the measured and predicted pile-load settlement, thus yielding a correlation coefficient (R) and root mean square error (RMSE) of 0.99, 0.043 respectively, with a relatively insignificant mean square error level (MSE) of 0.0019. © 2018, © 2018 Informa UK Limited, trading as Taylor & Francis Group