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Agricultural Sciences 2012年第4期3卷 557-561页

作者： Maryam Khoshkdaman Ali Akbar Ebadi danial kahrizi Department of Plant Breeding Razi University Kermanshah Iran Department of Plant Breeding Rice Research Institute of Iran(RRII) Rasht Iran Department of Plant Pathology Rice Research Institute of Iran(RRII) Rasht Iran

To evaluation of pathogenicity and race classification of Xanthomonas oryzae pv. oryzae agent bacterial leaf blight of rice, 153 isolates of X. oryzae pv. oryzae were collected from different rice-growing cities of Guilan province – Iran. All of isolates were inoculated to assess the differential characteristics of 26 near isogenic rice lines containing a single resistance gene or two to five genes. Inoculation was done 21 days after sowing in the greenhouse. Scoring of inoculated plants was done 18 days after inoculation. The level of infection was not so clear among pyramiding lines, expect IRBB53 and IRBB61. Therefore, the pyramiding lines can not be used as differentials for pathogenicity evaluation of X. oryzae pv. oryzae The 12 rice lines with a single resistance gene were used further to establish a system of races classification of X. oryzae pv. oryzae IRBB14, IRBB21and IRBB7 were resistance to the most isolates. Whereas, IRBB1, IRBB2, IRBB4 and IRBB10 were susceptible to all isolates. Based on the interactions between the isolates X. oryzae pv. oryzae and the 12 near-isogenic rice lines, seven singlegene rice lines were chosen as differentials, and the 153 tested isolates were classified into four races. Except for cultivar types, different terrain, climate, period of rice planting and other factors may be associated with the population diversity and virulent variation of X. oryzae pv. oryzae.

关键词： Bacterial Blight Near-Isogenic Lines Pyramiding Lines Race Classification Rice Xanthomonas oryzae pv. oryzae

A Mode-Switching Motion Control System for Reactive Interaction and Surface Following Using Industrial Robots

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IEEE/CAA Journal of Automatica Sinica 2018年第3期5卷 670-682页

作者： danial Nakhaeinia Pierre Payeur Robert Laganière School of Electrical Engineering and Computer Science University of Ottawa

This work proposes a sensor-based control system for fully automated object detection and exploration(surface following) with a redundant industrial robot. The control system utilizes both offline and online trajectory planning for reactive interaction with objects of different shapes and color using RGBD vision and proximity/contact sensors feedback where no prior knowledge of the objects is available. The RGB-D sensor is used to collect raw 3D information of the environment. The data is then processed to segment an object of interest in the scene. In order to completely explore the object, a coverage path planning technique is proposed using a dynamic 3D occupancy grid method to generate a primary(offline) trajectory. However, RGB-D sensors are very sensitive to lighting and provide only limited accuracy on the depth measurements. Therefore, the coverage path planning is then further assisted by a real-time adaptive path planning using a fuzzy self-tuning proportional integral derivative(PID)controller. The latter allows the robot to dynamically update the 3D model by a specially designed instrumented compliant wrist and adapt to the surfaces it approaches or touches. A modeswitching scheme is also proposed to efficiently integrate and smoothly switch between the interaction modes under certain conditions. Experimental results using a CRS-F3 manipulator equipped with a custom-built compliant wrist demonstrate the feasibility and performance of the proposed method.

关键词： Adaptive control fuzzy-PID controller manipulator control motion planning surface following

Modeling of shear wave velocity in limestone by soft computing methods

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International Journal of Mining Science and Technology 2017年第3期27卷 423-430页

作者： Behnia danial Ahangari Kaveh Moeinossadat Sayed Rahim Department of Mining Engineering Science and Research Branch Islamic Azad University Tehran *** Iran

The main purpose of current study is development of an intelligent model for estimation of shear wave velocity in limestone. Shear wave velocity is one of the most important rock dynamic parameters. Because rocks have complicated structure, direct determination of this parameter takes time, spends expenditure and requires accuracy. On the other hand, there are no precise equations for indirect determination of it; most of them are empirical. By using data sets of several dams of Iran and neuro-genetic, adaptive neuro-fuzzy inference system （ANFIS）, and gene expression programming （GEP） methods, models are rendered for prediction of shear wave velocity in limestone. Totally, 516 sets of data has been used for modeling. From these data sets, 413 ones have been utilized for building the intelligent model, and 103 have been used for their performance evaluation. Compressional wave velocity （Vp）, density （7） and porosity （.n）, were considered as input parameters. Respectively, the amount of R for neuro-genetic and ANFIS networks was 0.959 and 0.963. In addition, by using GEP, three equations are obtained; the best of them has 0.958R. ANFIS shows the best prediction results, whereas GEP indicates proper equations. Because these equations have accuracy, they could be used for prediction of shear wave velocity for limestone in the future.

关键词： Shear wave velocity Limestone Neuro-genetic Adaptive neuro-fuzzy inference system Gene expression programming

The effect of ZIF-90 particle in Pebax/Psf composite membrane on the transport properties of CO2,CH4 and N2 gases by Molecular Dynamics Simulation method

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Chinese Journal of Chemical Engineering 2020年第9期28卷 2267-2284页

作者： Ali Hatami Iman Salahshoori Niloufar Rashidi danial Nasirian Department of Chemical Engineering Science and Research BranchIslamicAzad UniversityTehranIran Department of Chemical Engineering Shahreza BranchIslamicAzad UniversityIsfahanIran

Nowadays,mixed matrix membranes(MMMs)have considered by many researchers to overcome the problems of polymeric *** addition,molecular dynamics(MD)and Monte Carlo(MC)simulation Methods are suitable tools for studying transport properties and morphology in *** this purpose,in this study using material studio 2017(MS)software,the transport properties of CO2,CH4 and N2 in Pebax,Psf neat Pebax/Psf composite and Pebax/Psf composite filled with ZIF-90 particles have been *** adding Psf to Pebax matrix,the selectivity of CO2/CH4 and CO2/N2 gases has significantly *** addition,adding ZIF-90 particles to the Pebax/Psf composite increased the permeability of CO2,CH4 and N2 compared to neat and composite *** morphological properties of the membranes,such as the fractional free volume(FFV),radial distribution function(RDF),glass transition temperature(TG),X-ray diffraction(XRD)and equilibrium density have calculated and acceptable results have obtained.

关键词： Separation Mixed Matrix Membranes Molecular Dynamics Monte Carlo Pebax 1656/Psf ZIF-90

Predicting crest settlement in concrete face rockfill dams using adaptive neuro-fuzzy inference system and gene expression programming intelligent methods

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Journal of Zhejiang University-Science A(Applied Physics & Engineering) 2013年第8期14卷 589-602页

作者： danial BEHNIA Kaveh AHANGARI Ali NOORZAD Sayed Rahim MOEINOSSADAT Department of Mining Engineering Science and Research Branch Islamic Azad University Faculty of Water and Environmental Engineering Power and Water University of Technology

This paper deals with the estimation of crest settlement in a concrete face rockfill dam （CFRD）, utilizing intelligent methods. Following completion of dam construction, considerable movements of the crest and the body of the dam can develop during the first impoundment of the reservoir. Although there is vast experience worldwide in CFRD design and construction, few accurate experimental relationships are available to predict the settlement in CFRD. The goal is to advance the development of intelligent methods to estimate the subsidence of dams at the design stage. Due to dam zonifieation and uncertainties in material properties, these methods appear to be the appropriate choice. In this study, the crest settlement behavior of CFRDs is analyzed based on compiled data of 24 CFRDs constructed during recent years around the world, along with the utilization of gene ex- pression programming （GEP） and adaptive neuro-fuzzy inference system （ANFIS） methods. In addition, dam height （H）, shape factor （St）, and time （t, time after first operation） are also assessed, being considered major factors in predicting the settlement behavior. From the relationships proposed, the values ofR2 for both equations of GEP （with and without constant） were 0.9603 and 0.9734, and for the three approaches of ANFIS （grid partitioning （GP）, subtractive clustering method （SCM）, and fuzzy c-means clustering （FCM）） were 0.9693, 0.8657, and 0.8848, respectively. The obtained results indicate that the overall behavior evaluated by this approach is consistent with the measured data of other CFRDs.

关键词： Concrete face rockfill dam (CFRD) Crest settlement Adaptive neuro-fuzzy inference system (ANFIS) Geneexpression programming (GEP)

Estimation of flexible pavement structural capacity using machine learning techniques

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Frontiers of Structural and Civil Engineering 2020年第5期14卷 1083-1096页

作者： Nader KARBALLAEEZADEH Hosein GHASEMZADEH TEHRANI danial MOHAMMADZADEH SHADMEHRI Shahaboddin SHAMSHIRBAND Civil Engineering Department Shahrood University of TechnologyShahrood ***Iran Department of Civil Engineering Ferdowsi University of MashhadMashhad ***Iran Department of Elite Relations with Industries Khorasan Construction Engineering OrganizationMashhad ***Iran Department for Management of Science and Technology Development Ton Duc Thang UniversityHo Chi Minh CityVietnam Faculty of Information Technology Ton Duc Thang UniversityHo Chi Minh CityVietnam

The most common index for representing structural condition of the pavement is the structural *** 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 *** procedure has two drawbacks:falling weight deflectometer and ground-penetrating radar are expensive tests;back-calculation ways has some inherent shortcomings compared to exact methods as they adopt a trial and error *** 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 *** 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 *** accuracy of results was examined based on three criteria of R,MAE,and *** 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 *** machine learning methods instead of back-calculation improves the calculation process quality and accuracy.

关键词： transportation infrastructure flexible pavement structural number prediction Gaussian process regression M5P model tree random forest

Optimized functional linked neural network for predicting diaphragm wall deflection induced by braced excavations in clays

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Geoscience Frontiers 2022年第2期13卷 34-51页

作者： Chengyu Xie Hoang Nguyen Yosoon Choi danial Jahed Armaghani School of Environment and Resources Xiang tan UniversityXiangtan 411105China Department of Surface Mining Mining FacultyHanoi University of Mining and GeologyDuc ThangBac Tu LiemHanoiViet Nam Innovations for Sustainable and Responsible Mining(ISRM)Group Hanoi University of Mining and GeologyDuc ThangBac Tu LiemHanoiViet Nam Department of Energy Resources Engineering Pukyong National UniversityBusan 48513Republic of Korea Department of Urban Planning Engineering Networks and SystemsInstitute of Architecture and ConstructionSouth Ural State University76Lenin ProspectChelyabinsk 454080Russia

Deep excavation during the construction of underground systems can cause movement on the ground,especially in soft clay *** high levels,excessive ground movements can lead to severe damage to adjacent *** this study,finite element analyses(FEM)and the hardening small strain(HSS)model were performed to investigate the deflection of the diaphragm wall in the soft clay layer induced by braced *** geometric and mechanical properties of the wall were investigated to study the deflection behavior of the wall in soft ***,1090 hypothetical cases were surveyed and simulated based on the HSS model and FEM to evaluate the wall deflection *** results were then used to develop an intelligent model for predicting wall deflection using the functional linked neural network(FLNN)with different functional expansions and activation *** the FLNN is a novel approach to predict wall deflection;however,in order to improve the accuracy of the FLNN model in predicting wall deflection,three swarm-based optimization algorithms,such as artificial bee colony(ABC),Harris’s hawk’s optimization(HHO),and hunger games search(HGS),were hybridized to the FLNN model to generate three novel intelligent models,namely ABC-FLNN,HHO-FLNN,*** results of the hybrid models were then compared with the basic FLNN and MLP *** revealed that FLNN is a good solution for predicting wall deflection,and the application of different functional expansions and activation functions has a significant effect on the outcome predictions of the wall *** is remarkably interesting that the performance of the FLNN model was better than the MLP model with a mean absolute error(MAE)of 19.971,root-mean-squared error(RMSE)of 24.574,and determination coefficient(R^(2))of ***,the performance of the MLP model only obtained an MAE of 20.321,RMSE of 27.091,and R^(2)of ***,the results also indicated that the proposed

关键词： Diaphragm wall deflection Braced excavation Finite element analysis Clays Meta-heuristic algorithms Functional linked neural network

Stability analysis of underground mine hard rock pillars via combination of finite difference methods,neural networks,and Monte Carlo simulation techniques

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Underground Space 2021年第4期6卷 379-395页

作者： Chuanqi Lia Jian Zhou danial Jahed Armaghani Xibing Li School of Resources and Safety Engineering Central South UniversityChangsha 410083China Department of Civil Engineering Faculty of EngineeringUniversity of Malaya50603 Kuala LumpurMalaysia

Pillar stability is always evaluated using the safety factor(SF),which is defined as the ratio of pillar strength to pillar ***,most researchers have estimated pillar stress using the pillar shape ratio(w/h),uniaxial compressive strength(UCS)of the intact rock mass,and pillar depth(H).In this study,the geological strength index(GSI)of hard rock pillars was considered as a new variable for predictive *** index was developed by combining numerical simulation software(i.e.,FLAC3D)and a backpropagation neural network(BPNN).A hard rock pillar stability analysis,based on three methods including deterministic method,sensitivity analysis,and Monte Carlo simulation(MCS),was performed.A new formula was proposed to estimate the SF values based on the predicted stress,considering the GSI variable in the deterministic *** sensitivity analysis indicated that the variables impacting the SF from high to low are UCS,GSI,w/h,and *** this study,pillar stability was analyzed mainly using the GSI and MCS *** MCS results revealed that the GSI is also a major factor in pillar stability and has a greater effect on weak pillars than on strong ***,a pillar is more likely to be unstable when both the GSI and the UCS are *** study provides several references and procedures for improving the design of stable pillars considering the GSI as an important factor.

关键词： Hard rock pillar Numerical simulation Neural networks Safety factor Geological strength index Monte Carlo simulation

Ability of rotary compactor in compacting consistent slabs and effect of rotary compaction on engineering properties of stone mastic asphalt mixtures

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Journal of Central South University 2013年第12期20卷 3754-3763页

作者： Ratnasamy Muniandy Hamed Haghighi Salihudin bin Hassim danial Moazami Department of Civil Engineering Universiti Putra Malaysia

A new automatic rotary compactor and its abilities in compacting stone mastic asphalt （SMA） are presented. Following an overview of the rotary compactor and the compaction procedure, it is demonstrated that the rotary compactor is able to produce uniform slabs with the desired thickness of 65 mm all over around. Furthermore, 132 cored samples from the rotary compactor had been compacted uniformly with approximately 4% optimum air void content. In addition, performance tests results indicate that the rotary compactor produces asphalt mixturures with the requirements of resilient modulus, Marshall stability and flow. A weight factor was introduced for each fraction of aggregates in the degradation analysis to compensate the crushing effect of aggregates during mixing and compacting.

关键词： stone mastic asphalt (SMA) rotary compactor SMA compaction asphalt mixture engineering properties

Long term performance of warm mix asphalt versus hot mix asphalt

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Journal of Central South University 2013年第1期20卷 256-266页

作者： Ziari Hasan Behbahani Hamid Izadi Amir Nasr danial School of Civil Engineering Iran University of Science and Technology

The fatigue behavior, indirect tensile strength （ITS） and resilient modulus test results for warm mix asphalt （WMA） as well as hot mix asphalt （HMA） at different ageing levels were evaluated. Laboratory-prepared samples were aged artificially in the oven to simulate short-term and long term ageing in accordance with AASHTO R30 and then compared with unaged specimens. Beam fatigue testing was performed using beam specimens at 25 ℃ based on AASHTO T321 standard. Fatigue life, bending stiffness and dissipated energy for both unaged and aged mixtures were calculated using four-point beam fatigue test results. Three-point bending tests were performed using semi-circular bend （SCB） specimens at -10 ℃ and the critical mode I stress intensity factor K1 was then calculated using the peak load obtained from the load-displacement curve. It is observed that Sasobit and Rheofalt warm mix asphalt additives have a significant effect on indirect tensile strength, resilient modulus, fatigue behavior and stress intensity factor of aged and unaged mixtures.

关键词： warm mix asphalt hot mix asphalt fatigue behavior resilient modulus tensile strength stress intensity factor ageing