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National Civil Engineering Olympiad (The first step), R
Ranked 1st in B.Sc.
shahid behesti university
Danial Mohammadzadeh S. is currently lecture. He received a master's degree in civil engineering in field of Geotechnical Engineering from Ferdowsi university of Mashhad and has 7 years of experience and training in building construction, Geotechnical Engineering, and environmental analyses projects. He has authored over 43publications in archival journals, and conference proceedings, 11 patent and 11 research project. He is a lecturer in Ferdowsi University of Mashhad in Iran, Technical and vocational university of Khorasan Razavi Province (Montazei Technical University of Mashhad) , Iran, Eqbal Institute of Higher Education Lahoori and an associate member of ASCE. In addition, he is member of the editorial board 3 journals and 7 international conferences. He was Mayor Advisor in Mashhadd. He has received a number of award certificates for researching, patent and research project and also he is Supreme Consultant Action of Khorasan Razavi Inventor Association and he is Head of Departm
Concrete, as one of the essential construction materials, is responsible for a vast amount of emissions. Using recycled materials and gray water can considerably contribute to the sustainability aspect of concrete production. Thus, finding a proper replacement for fresh water in the production of concrete is significant. The usage of industrial wastewater instead of water in concrete is considered in this paper. In this study, 450 concrete samples are produced with different amounts of wastewater. The mechanical parameters, such as slump, compressive strength, water absorption, tensile strength, electrical resistivity, rapid freezing, half-cell potential and appearance, are investigated, and a specific concentration and impurities of wastewater that cause a 10% compressive strength reduction were found. The results showed that the usage of industrial wastewater does not significantly change the main characteristics of concrete. Although increasing the concentration of wastewater can decrease the durability and strength features of concrete nonlinearly, the negative effects on durability tests are more conspicuous, as utilizing concentrated wastewaters disrupt the formation of appropriate air voids, pore connectivity and pore-size distribution in the concrete.
Ehsan Nasseralshariati; Danial Mohammadzadeh; Nader Karballaeezadeh; Amir Mosavi; Uwe Reuter; Murat Saatcioglu. The Effect of Incorporating Industrials Wastewater on Durability and Long-Term Strength of Concrete. Materials 2021, 14, 4088 .
AMA StyleEhsan Nasseralshariati, Danial Mohammadzadeh, Nader Karballaeezadeh, Amir Mosavi, Uwe Reuter, Murat Saatcioglu. The Effect of Incorporating Industrials Wastewater on Durability and Long-Term Strength of Concrete. Materials. 2021; 14 (15):4088.
Chicago/Turabian StyleEhsan Nasseralshariati; Danial Mohammadzadeh; Nader Karballaeezadeh; Amir Mosavi; Uwe Reuter; Murat Saatcioglu. 2021. "The Effect of Incorporating Industrials Wastewater on Durability and Long-Term Strength of Concrete." Materials 14, no. 15: 4088.
The pavement is a complex structure that is influenced by various environmental and loading conditions. The regular assessment of pavement performance is essential for road network maintenance. International roughness index (IRI) and pavement condition index (PCI) are well-known indices used for smoothness and surface condition assessment, respectively. Machine learning techniques have recently made significant advancements in pavement engineering. This paper presents a novel roughness-distress study using random forest (RF). After determining the PCI and IRI values for the sample units, the PCI prediction process is advanced using RF and random forest trained with a genetic algorithm (RF-GA). The models are validated using correlation coefficient (CC), scatter index (SI), and Willmott’s index of agreement (WI) criteria. For the RF method, the values of the three parameters mentioned were −0.177, 0.296, and 0.281, respectively, whereas in the RF-GA method, −0.031, 0.238, and 0.297 values were obtained for these parameters. This paper aims to fulfill the literature’s identified gaps and help pavement engineers overcome the challenges with the conventional pavement maintenance systems.
Nader Karballaeezadeh; Danial Mohammadzadeh S.; Dariush Moazemi; Shahab S. Band; Amir Mosavi; Uwe Reuter. Smart Structural Health Monitoring of Flexible Pavements Using Machine Learning Methods. Coatings 2020, 10, 1100 .
AMA StyleNader Karballaeezadeh, Danial Mohammadzadeh S., Dariush Moazemi, Shahab S. Band, Amir Mosavi, Uwe Reuter. Smart Structural Health Monitoring of Flexible Pavements Using Machine Learning Methods. Coatings. 2020; 10 (11):1100.
Chicago/Turabian StyleNader Karballaeezadeh; Danial Mohammadzadeh S.; Dariush Moazemi; Shahab S. Band; Amir Mosavi; Uwe Reuter. 2020. "Smart Structural Health Monitoring of Flexible Pavements Using Machine Learning Methods." Coatings 10, no. 11: 1100.
The exponential increase in aviation activity and air traffic in recent decades has raised several public health issues. One of the critical public health concerns is runway safety and the increasing demand for airports without accidents. In addition to threatening human lives, runway accidents are often associated with severe environmental and pollution consequences. In this study, a three-step approach is used for runway risk assessment considering probability, location, and consequences of accidents through advanced statistical methods. This study proposes novel models for the implementation of these three steps in Iran. Data on runway excursion accidents were collected from several countries with similar air accident rates. The proposed models empower engineers to advance an accurate assessment of the accident probability and safety assessment of airports. For in-service airports, it is possible to assess existing runways to remove obstacles close to runways if necessary. Also, the proposed models can be used for preliminary evaluations of developing existing airports and the construction of new runways.
Yaser Yousefi; Nader Karballaeezadeh; Dariush Moazami; Amirhossein Sanaei Zahed; Danial Mohammadzadeh S.; Amir Mosavi. Improving Aviation Safety through Modeling Accident Risk Assessment of Runway. International Journal of Environmental Research and Public Health 2020, 17, 6085 .
AMA StyleYaser Yousefi, Nader Karballaeezadeh, Dariush Moazami, Amirhossein Sanaei Zahed, Danial Mohammadzadeh S., Amir Mosavi. Improving Aviation Safety through Modeling Accident Risk Assessment of Runway. International Journal of Environmental Research and Public Health. 2020; 17 (17):6085.
Chicago/Turabian StyleYaser Yousefi; Nader Karballaeezadeh; Dariush Moazami; Amirhossein Sanaei Zahed; Danial Mohammadzadeh S.; Amir Mosavi. 2020. "Improving Aviation Safety through Modeling Accident Risk Assessment of Runway." International Journal of Environmental Research and Public Health 17, no. 17: 6085.
The most common index for representing structural condition of the pavement is the structural number. The current procedure for determining structural numbers involves utilizing falling weight deflectometer and ground-penetrating radar tests, recording pavement surface deflections, and analyzing recorded deflections by back-calculation manners. This procedure has two drawbacks: 1. falling weight deflectometer and ground-penetrating radar are expensive tests, 2. back-calculation ways has some inherent shortcomings compared to exact methods as they adopt a trial and error approach. In this study, three machine learning methods entitled Gaussian process regression, m5p model tree, and random forest used for the prediction of structural numbers in flexible pavements. Dataset of this paper is related to 759 flexible pavement sections at Semnan and Khuzestan provinces in Iran and includes “structural number” as output and “surface deflections and surface temperature” as inputs. The accuracy of results was examined based on three criteria of R, MAE, and RMSE. Among the methods employed in this paper, random forest is the most accurate as it yields the best values for above criteria (R=0.841, MAE=0.592, and RMSE=0.760). The proposed method does not require to use ground penetrating radar test, which in turn reduce costs and work difficulty. Using machine learning methods instead of back-calculation improves the calculation process quality and accuracy.
Nader Karballaeezadeh; Hosein Ghasemzadeh Tehrani; Danial Mohammadzadeh S.; Shahaboddin Shamshirband. Estimation of Flexible Pavement Structural Capacity Using Machine Learning Techniques. 2020, 1 .
AMA StyleNader Karballaeezadeh, Hosein Ghasemzadeh Tehrani, Danial Mohammadzadeh S., Shahaboddin Shamshirband. Estimation of Flexible Pavement Structural Capacity Using Machine Learning Techniques. . 2020; ():1.
Chicago/Turabian StyleNader Karballaeezadeh; Hosein Ghasemzadeh Tehrani; Danial Mohammadzadeh S.; Shahaboddin Shamshirband. 2020. "Estimation of Flexible Pavement Structural Capacity Using Machine Learning Techniques." , no. : 1.
The construction of different roads, such as freeways, highways, major roads or minor roads must be accompanied by constant monitoring and evaluation of service delivery. Pavements are generally assessed by engineers in terms of the smoothness, surface condition, structural condition and surface safety. Pavement assessment is often conducted using the qualitative indices such as international roughness index (IRI), pavement condition index (PCI), structural condition index (SCI) and skid resistance value (SRV), which are used for smoothness assessment, surface condition assessment, structural condition assessment, and surface safety assessment, respectively. In this paper, Tehran-Qom Freeway in Iran has been selected as the case study and its smoothness and pavement surface conditions are assessed. At 2-km intervals, a 100-meter sample unit is selected in the slow-speed lane (totally, 118 sample units). In these sample units, the PCI is calculated after a visual inspection of the pavement and the recording of distresses. Then, in each sample unit, the average IRI is computed. The purpose of this study is to provide a method for estimating PCI based on IRI. The proposed theory was developed by Random Forest (RF), and Random Forest optimized by Genetic Algorithm (RF-GA) methods and these methods were validated using correlation coefficient (CC), scattered index (SI), and Willmott’s index of agreement (WI) criteria. The proposed method reduces costs, saves time and eliminates the safety risks.
Nader Karballaeezadeh; Danial Mohammadzadeh S.; Dariush Moazami; Narjes Nabipour; Amir Mosavi; Uwe Reuter. Smart Structural Health Monitoring of Flexible Pavements Using Machine Learning Methods. 2020, 1 .
AMA StyleNader Karballaeezadeh, Danial Mohammadzadeh S., Dariush Moazami, Narjes Nabipour, Amir Mosavi, Uwe Reuter. Smart Structural Health Monitoring of Flexible Pavements Using Machine Learning Methods. . 2020; ():1.
Chicago/Turabian StyleNader Karballaeezadeh; Danial Mohammadzadeh S.; Dariush Moazami; Narjes Nabipour; Amir Mosavi; Uwe Reuter. 2020. "Smart Structural Health Monitoring of Flexible Pavements Using Machine Learning Methods." , no. : 1.
Design and advancement of the durable urban train infrastructures are of utmost importance for reliable mobility in the smart cities of the future. Given the importance of urban train lines, tunnels, and subway stations, these structures should be meticulously analyzed. In this research, two-dimensional modeling and analysis of the soil-structure mass of the Alan Dasht station of Mashhad Urban Train are studied. The two-dimensional modeling was conducted using Hashash’s method and displacement interaction. After calculating the free-field resonance and side distortion of the soil mass, this resonance was entered into PLAXIS finite element program, and finally, stress and displacement contours together with the bending moment, shear force and axial force curves of the structure were obtained.
Danial Mohammadzadeh S.; Nader Karballaeezadeh; Morteza Mohemmi; Amir Mosavi; Annamária R. Várkonyi-Kóczy. Urban Train Soil-Structure Interaction Modeling and Analysis. Inventive Computation and Information Technologies 2020, 361 -381.
AMA StyleDanial Mohammadzadeh S., Nader Karballaeezadeh, Morteza Mohemmi, Amir Mosavi, Annamária R. Várkonyi-Kóczy. Urban Train Soil-Structure Interaction Modeling and Analysis. Inventive Computation and Information Technologies. 2020; ():361-381.
Chicago/Turabian StyleDanial Mohammadzadeh S.; Nader Karballaeezadeh; Morteza Mohemmi; Amir Mosavi; Annamária R. Várkonyi-Kóczy. 2020. "Urban Train Soil-Structure Interaction Modeling and Analysis." Inventive Computation and Information Technologies , no. : 361-381.
Prediction of the remaining service life (RSL) of pavement is a challenging task for road maintenance and transportation engineering. The prediction of the RSL estimates the time that a major repair or reconstruction becomes essential. The conventional approach to predict RSL involves using non-destructive tests. These tests, in addition to being costly, interfere with traffic flow and compromise operational safety. In this paper, surface distresses of pavement are used to estimate the RSL to address the aforementioned challenges. To implement the proposed theory, 105 flexible pavement segments are considered. For each pavement segment, the type, severity, and extent of surface damage and the pavement condition index (PCI) were determined. The pavement RSL was then estimated using non-destructive tests include falling weight deflectometer (FWD) and ground-penetrating radar (GPR). After completing the dataset, the modeling was conducted to predict RSL using three techniques include support vector regression (SVR), support vector regression optimized by the fruit fly optimization algorithm (SVR-FOA), and gene expression programming (GEP). All three techniques estimated the RSL of the pavement by selecting the PCI as input. The correlation coefficient (CC), Nash–Sutcliffe efficiency (NSE), scattered index (SI), and Willmott’s index of agreement (WI) criteria were used to examine the performance of the three techniques adopted in this study. In the end, it was found that GEP with values of 0.874, 0.598, 0.601, and 0.807 for CC, SI, NSE, and WI criteria, respectively, had the highest accuracy in predicting the RSL of pavement.
Narjes Nabipour; Nader Karballaeezadeh; Adrienn Dineva; Amir Mosavi; Danial Mohammadzadeh S.; Shahaboddin Shamshirband; Danial S. Mohammadzadeh. Comparative Analysis of Machine Learning Models for Prediction of Remaining Service Life of Flexible Pavement. Mathematics 2019, 7, 1198 .
AMA StyleNarjes Nabipour, Nader Karballaeezadeh, Adrienn Dineva, Amir Mosavi, Danial Mohammadzadeh S., Shahaboddin Shamshirband, Danial S. Mohammadzadeh. Comparative Analysis of Machine Learning Models for Prediction of Remaining Service Life of Flexible Pavement. Mathematics. 2019; 7 (12):1198.
Chicago/Turabian StyleNarjes Nabipour; Nader Karballaeezadeh; Adrienn Dineva; Amir Mosavi; Danial Mohammadzadeh S.; Shahaboddin Shamshirband; Danial S. Mohammadzadeh. 2019. "Comparative Analysis of Machine Learning Models for Prediction of Remaining Service Life of Flexible Pavement." Mathematics 7, no. 12: 1198.
Remaining service life (RSL) of pavement, as a sign of future pavement performance, has always received growing attention from pavement engineers. The RSL describes the time from the moment of pavement inspection until such a time when a major repair or reconstruction is required. The conventional approach to determining RSL involves using non-destructive tests. These tests, in addition to being costly, interfere with traffic flow and compromise users' safety. In this paper, surface distresses of pavement have been used to estimate the pavement’s RSL in order to eliminate the aforementioned problems and challenges. To implement the proposed theory, 105 flexible pavement segments were taken from Shahrood-Damghan Highway (Highway 44) in Iran. For each pavement segment, the type, severity, and extent of surface damage and pavement condition index (PCI) were determined. The pavement RSL was then estimated using non-destructive tests include Falling Weight Deflectometer (FWD) and Ground Penetrating Radar (GPR). After completing the dataset, the modeling was conducted to predict RSL using three techniques include Support Vector Regression (SVR), Support Vector Regression Optimized by Fruit Fly Optimization Algorithm (SVR-FOA), and Gene Expression Programming (GEP). All three techniques estimated the RSL of the pavement by selecting the PCI as input. The Correlation Coefficient (CC), Nash-Sutcliffe efficiency (NSE), Scattered Index (SI), and Willmott’s Index of agreement (WI) criteria were used to examine the performance of the three techniques adopted in this study. In the end, it was found that GEP with values of 0.874, 0.598, 0.601, and 0.807 for CC, SI, NSE, and WI criteria, respectively, had the highest accuracy in predicting the RSL of pavement.
Nader Karballaeezadeh; Adrienn Dineva; Amir Mosavi; Narjes Nabipour; Shahaboddin Shamshirband; Danial Mohammadzadeh. Hybrid Machine Learning Model of Support Vector Machine and Fruit Fly Optimization Algorithm for Prediction of Remaining Service Life of Flexible Pavement. 2019, 1 .
AMA StyleNader Karballaeezadeh, Adrienn Dineva, Amir Mosavi, Narjes Nabipour, Shahaboddin Shamshirband, Danial Mohammadzadeh. Hybrid Machine Learning Model of Support Vector Machine and Fruit Fly Optimization Algorithm for Prediction of Remaining Service Life of Flexible Pavement. . 2019; ():1.
Chicago/Turabian StyleNader Karballaeezadeh; Adrienn Dineva; Amir Mosavi; Narjes Nabipour; Shahaboddin Shamshirband; Danial Mohammadzadeh. 2019. "Hybrid Machine Learning Model of Support Vector Machine and Fruit Fly Optimization Algorithm for Prediction of Remaining Service Life of Flexible Pavement." , no. : 1.
Design and advancement of the durable urban train infrastructures are of utmost importance for reliable mobility in the smart cities of the future. Given the importance of urban train lines, tunnels, and subway stations, these structures should be meticulously analyzed. In this research, two-dimensional modeling and analysis of the soil-structure mass of the Alan Dasht station of Mashhad Urban Train are studied. The two-dimensional modeling was conducted using Hashash’s method and displacement interaction. After calculating the free-field resonance and side distortion of the soil mass, this resonance was entered into PLAXIS finite element program, and finally, stress and displacement contours together with the bending moment, shear force and axial force curves of the structure were obtained.
Danial Mohammadzadeh S.; Nader Karballaeezadeh; Morteza Mohemmi; Amir Mosavi; Annamária R. Várkonyi-Kóczy. Urban Train Soil-Structure Interaction Modeling and Analysis. 2019, 1 .
AMA StyleDanial Mohammadzadeh S., Nader Karballaeezadeh, Morteza Mohemmi, Amir Mosavi, Annamária R. Várkonyi-Kóczy. Urban Train Soil-Structure Interaction Modeling and Analysis. . 2019; ():1.
Chicago/Turabian StyleDanial Mohammadzadeh S.; Nader Karballaeezadeh; Morteza Mohemmi; Amir Mosavi; Annamária R. Várkonyi-Kóczy. 2019. "Urban Train Soil-Structure Interaction Modeling and Analysis." , no. : 1.
Considering citizens’ perceptions of their living environment is very helpful in making the right decisions for city planners who intend to build a sustainable society. Mental map analyses are widely used in understanding the level of perception of individuals regarding the surrounding environment. The present study introduces Aram Mental Map Analyzer (AMMA), an open-source program, which allows researchers to use special features and new analytical methods to receive outputs in numerical data and analytical maps with greater accuracy and speed. AMMA performance is contingent upon two principles of accuracy and complexity, the accuracy of the program is measured by Accuracy Placed Landmarks (APL) and General Orientation (GO), which respectively analyses the landmark placement accuracy and the main route mapping accuracy. Also, the complexity section is examined through two analyses Cell Percentage (CP) and General Structure (GS), which calculates the complexity of citizens’ perception of space based on the criteria derived from previous studies. AMMA examines all the dimensions and features of the graphic maps and its outputs have a wide range of valid and differentiated information, which is tailored to the research and information subject matter that is required.
Farshid Aram; Ebrahim Solgi; Ester Higueras García; Danial Mohammadzadeh S.; Amir Mosavi; Shahaboddin Shamshirband. Design and Validation of a Computational Program for Analysing Mental Maps: Aram Mental Map Analyzer. Sustainability 2019, 11, 3790 .
AMA StyleFarshid Aram, Ebrahim Solgi, Ester Higueras García, Danial Mohammadzadeh S., Amir Mosavi, Shahaboddin Shamshirband. Design and Validation of a Computational Program for Analysing Mental Maps: Aram Mental Map Analyzer. Sustainability. 2019; 11 (14):3790.
Chicago/Turabian StyleFarshid Aram; Ebrahim Solgi; Ester Higueras García; Danial Mohammadzadeh S.; Amir Mosavi; Shahaboddin Shamshirband. 2019. "Design and Validation of a Computational Program for Analysing Mental Maps: Aram Mental Map Analyzer." Sustainability 11, no. 14: 3790.
In construction projects, estimation of the settlement of fine-grained soils is of critical importance, and yet is a challenging task. The coefficient of consolidation for the compression index (Cc) is a key parameter in modeling the settlement of fine-grained soil layers. However, the estimation of this parameter is costly, time-consuming, and requires skilled technicians. To overcome these drawbacks, we aimed to predict Cc through other soil parameters, i.e., the liquid limit (LL), plastic limit (PL), and initial void ratio (e0). Using these parameters is more convenient and requires substantially less time and cost compared to the conventional tests to estimate Cc. This study presents a novel prediction model for the Cc of fine-grained soils using gene expression programming (GEP). A database consisting of 108 different data points was used to develop the model. A closed-form equation solution was derived to estimate Cc based on LL, PL, and e0. The performance of the developed GEP-based model was evaluated through the coefficient of determination (R2), the root mean squared error (RMSE), and the mean average error (MAE). The proposed model performed better in terms of R2, RMSE, and MAE compared to the other models.
Danial Mohammadzadeh S.; Seyed-Farzan Kazemi; Amir Mosavi; Ehsan Nasseralshariati; Joseph H. M. Tah. Prediction of Compression Index of Fine-Grained Soils Using a Gene Expression Programming Model. Infrastructures 2019, 4, 26 .
AMA StyleDanial Mohammadzadeh S., Seyed-Farzan Kazemi, Amir Mosavi, Ehsan Nasseralshariati, Joseph H. M. Tah. Prediction of Compression Index of Fine-Grained Soils Using a Gene Expression Programming Model. Infrastructures. 2019; 4 (2):26.
Chicago/Turabian StyleDanial Mohammadzadeh S.; Seyed-Farzan Kazemi; Amir Mosavi; Ehsan Nasseralshariati; Joseph H. M. Tah. 2019. "Prediction of Compression Index of Fine-Grained Soils Using a Gene Expression Programming Model." Infrastructures 4, no. 2: 26.
Appropriate estimation of soil settlement is of significant importance since it directly influences the performance of building and infrastructures that are built on soil. In particular, the settlement of fine-grained soils is critical because of low permeability and continuous settlement with time. Coefficient of consolidation (Cc) is a key parameter to estimate settlement of fine-grained soil layers. However, estimation of this parameter is time consuming, needs skilled technicians, and specific equipment. In this study, Cc was estimated using several soil parameters such as liquid limit (LL), plastic limit (PL), and initial void ratio (e0). Estimating such parameters in laboratory is straight forward and needs substantially less time and cost compared to conventional tests to estimate Cc such as oedometer test. This study presents a novel prediction model for Cc of fine-grained soils using gene-expression programming (GEP). GEP is a biologically inspired technique capable of offering closed-form solution for the optimal solution. A database consisted of 108 different data points was used to develop the model. A closed-form equation solution was derived to estimate Cc based on LL, PL, and e0. The performance of developed GEP-based model was evaluated through coefficient of determination (R2), root mean squared error (RMSE), and mean average error (MAE). High R2 and low error values indicated the descent performance of the model. Furthermore, the model was evaluated using the additional performance measures and met all the suggested criteria. Furthermore, the model had a better performance in terms of R2, RMSE, and MAE compared to most of existing models. It is expected that the developed model will decrease the time and cost associate with determining Cc of fine-grained soils.Keywords: evolutionary model, gene-expression programming (GEP), prediction, soil compression index, estimation, soil engineering, soil informatics, civil engineering, machine learning, data science, big data, soft computing, deep learning, forecasting, subject classification codes, construction informatics, computational intelligence (CI), artificial intelligence (AI), estimation
Danial Mohammadzadeh; Seyed-Farzan Kazemi; Amir Mosavi. Evolutionary Prediction Model for Fine-Grained Soils Compression Index Using Gene-Expression Programming. 2019, 1 .
AMA StyleDanial Mohammadzadeh, Seyed-Farzan Kazemi, Amir Mosavi. Evolutionary Prediction Model for Fine-Grained Soils Compression Index Using Gene-Expression Programming. . 2019; ():1.
Chicago/Turabian StyleDanial Mohammadzadeh; Seyed-Farzan Kazemi; Amir Mosavi. 2019. "Evolutionary Prediction Model for Fine-Grained Soils Compression Index Using Gene-Expression Programming." , no. : 1.
Accurate prediction of the remaining service life (RSL) of pavement is essential for the design and construction of roads, mobility planning, transportation modeling as well as road management systems. However, the expensive measurement equipment and interference with the traffic flow during the tests are reported as the challenges of the assessment of RSL of pavement. This paper presents a novel prediction model for RSL of road pavement using support vector regression (SVR) optimized by particle filter to overcome the challenges. In the proposed model, temperature of the asphalt surface and the pavement thickness (including asphalt, base and sub-base layers) are considered as inputs. For validation of the model, results of heavy falling weight deflectometer (HWD) and ground-penetrating radar (GPR) tests in a 42-km section of the Semnan–Firuzkuh road including 147 data points were used. The results are compared with support vector machine (SVM), artificial neural network (ANN) and multi-layered perceptron (MLP) models. The results show the superiority of the proposed model with a correlation coefficient index equal to 95%.
Nader Karballaeezadeh; Danial Mohammadzadeh S.; Shahaboddin Shamshirband; Pouria Hajikhodaverdikhan; Amir Mosavi; Kwok-Wing Chau. Prediction of remaining service life of pavement using an optimized support vector machine (case study of Semnan–Firuzkuh road). Engineering Applications of Computational Fluid Mechanics 2019, 13, 188 -198.
AMA StyleNader Karballaeezadeh, Danial Mohammadzadeh S., Shahaboddin Shamshirband, Pouria Hajikhodaverdikhan, Amir Mosavi, Kwok-Wing Chau. Prediction of remaining service life of pavement using an optimized support vector machine (case study of Semnan–Firuzkuh road). Engineering Applications of Computational Fluid Mechanics. 2019; 13 (1):188-198.
Chicago/Turabian StyleNader Karballaeezadeh; Danial Mohammadzadeh S.; Shahaboddin Shamshirband; Pouria Hajikhodaverdikhan; Amir Mosavi; Kwok-Wing Chau. 2019. "Prediction of remaining service life of pavement using an optimized support vector machine (case study of Semnan–Firuzkuh road)." Engineering Applications of Computational Fluid Mechanics 13, no. 1: 188-198.
Multi-gene genetic programming (MGGP) is a new nonlinear system modeling approach that integrates the capabilities of standard GP and classical regression. This paper deals with the prediction of compression index of fine-grained soils using this robust technique. The proposed model relates the soil compression index to its liquid limit, plastic limit and void ratio. Several laboratory test results for fine fine-grained were used to develop the models. Various criteria were considered to check the validity of the model. The parametric and sensitivity analyses were performed and discussed. The MGGP method was found to be very effective for predicting the soil compression index. The prediction coefficients of determination were 0.856 and 0.840 for the training and testing data, respectively. A comparative study was further performed to prove the superiority of the MGGP model to the existing soft computing and traditional empirical equations.
Danial Mohammadzadeh S; Jafar Bolouri Bzaz; S. H. Vafaee Jani Yazd; Amir H. Alavi. Deriving an intelligent model for soil compression index utilizing multi-gene genetic programming. Environmental Earth Sciences 2016, 75, 1 -11.
AMA StyleDanial Mohammadzadeh S, Jafar Bolouri Bzaz, S. H. Vafaee Jani Yazd, Amir H. Alavi. Deriving an intelligent model for soil compression index utilizing multi-gene genetic programming. Environmental Earth Sciences. 2016; 75 (3):1-11.
Chicago/Turabian StyleDanial Mohammadzadeh S; Jafar Bolouri Bzaz; S. H. Vafaee Jani Yazd; Amir H. Alavi. 2016. "Deriving an intelligent model for soil compression index utilizing multi-gene genetic programming." Environmental Earth Sciences 75, no. 3: 1-11.
Danial Mohammadzadeh S.; Jafar Bolouri Bzaz; Amir H. Alavi. An evolutionary computational approach for formulation of compression index of fine-grained soils. Engineering Applications of Artificial Intelligence 2014, 33, 58 -68.
AMA StyleDanial Mohammadzadeh S., Jafar Bolouri Bzaz, Amir H. Alavi. An evolutionary computational approach for formulation of compression index of fine-grained soils. Engineering Applications of Artificial Intelligence. 2014; 33 ():58-68.
Chicago/Turabian StyleDanial Mohammadzadeh S.; Jafar Bolouri Bzaz; Amir H. Alavi. 2014. "An evolutionary computational approach for formulation of compression index of fine-grained soils." Engineering Applications of Artificial Intelligence 33, no. : 58-68.
Amir H. Gandomi; Danial Mohammadzadeh S.; Juan Luis Pérez Ordóñez; Amir H. Alavi. Linear genetic programming for shear strength prediction of reinforced concrete beams without stirrups. Applied Soft Computing 2014, 19, 112 -120.
AMA StyleAmir H. Gandomi, Danial Mohammadzadeh S., Juan Luis Pérez Ordóñez, Amir H. Alavi. Linear genetic programming for shear strength prediction of reinforced concrete beams without stirrups. Applied Soft Computing. 2014; 19 ():112-120.
Chicago/Turabian StyleAmir H. Gandomi; Danial Mohammadzadeh S.; Juan Luis Pérez Ordóñez; Amir H. Alavi. 2014. "Linear genetic programming for shear strength prediction of reinforced concrete beams without stirrups." Applied Soft Computing 19, no. : 112-120.
Saeed K. Babanajad; Amir Gandomi; Danial Mohammadzadeh S.; Amir H. Alavi. Numerical modeling of concrete strength under multiaxial confinement pressures using linear genetic programming. Automation in Construction 2013, 36, 136 -144.
AMA StyleSaeed K. Babanajad, Amir Gandomi, Danial Mohammadzadeh S., Amir H. Alavi. Numerical modeling of concrete strength under multiaxial confinement pressures using linear genetic programming. Automation in Construction. 2013; 36 ():136-144.
Chicago/Turabian StyleSaeed K. Babanajad; Amir Gandomi; Danial Mohammadzadeh S.; Amir H. Alavi. 2013. "Numerical modeling of concrete strength under multiaxial confinement pressures using linear genetic programming." Automation in Construction 36, no. : 136-144.