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  • 1.
    Al-Janabi, Ahmed Mohammed Sami
    et al.
    Department of Civil Engineering, Faculty of Engineering, Universiti Putra Malaysia, UPM Serdang, Selangor, Malaysia.
    Ghazali, Abdul Halim
    Department of Civil Engineering, Faculty of Engineering, Universiti Putra Malaysia, UPM Serdang, Selangor, Malaysia.
    Ghazaw, Yousry Mahmoud
    Department of Irrigation and Hydraulics, Faculty of Engineering, Alexandria University, Alexandria, Egypt. Department of Civil Engineering, College of Engineering, Qassim University, Buraydah, Saudi Arabia.
    Afan, Haitham Abdulmohsin
    Institute of Research and Development, Duy Tan University, Da Nang, Vietnam.
    Al-Ansari, Nadhir
    Luleå University of Technology, Department of Civil, Environmental and Natural Resources Engineering, Mining and Geotechnical Engineering.
    Yaseen, Zaher Mundher
    Sustainable Developments in Civil Engineering Research Group, Faculty of Civil Engineering, Ton Duc Thang University, Ho Chi Minh City, Vietnam.
    Experimental and Numerical Analysis for Earth-Fill Dam Seepage2020In: Sustainability, ISSN 2071-1050, E-ISSN 2071-1050, Vol. 12, no 6, p. 1-14, article id 2490Article in journal (Refereed)
    Abstract [en]

    Earth-fill dams are the most common types of dam and the most economical choice. However, they are more vulnerable to internal erosion and piping due to seepage problems that are the main causes of dam failure. In this study, the seepage through earth-fill dams was investigated using physical, mathematical, and numerical models. Results from the three methods revealed that both mathematical calculations using L. Casagrande solutions and the SEEP /Wnumerical model have a plotted seepage line compatible with the observed seepage line in the physical model. However,when the seepage flow intersected the downstream slope and when piping took place, the use of SEEP /Wto calculate the flow rate became useless as it was unable to calculate the volume of water flow in pipes. This was revealed by the big dierence in results between physical and numerical models in the first physical model, while the results were compatible in the second physical model when the seepage line stayed within the body of the dam and low compacted soil was adopted. Seepage analysis for seven dierent configurations of an earth-fill dam was conducted using the SEEP /W model at normal and maximum water levels to find the most appropriate configuration among them. The seven dam configurations consisted of four homogenous dams and three zoned dams. Seepage analysis revealed that if sucient quantity of silty sand soil is available around the proposed dam location, a homogenous earth-fill dam with a medium drain length of 0.5 m thickness is the best design configuration. Otherwise, a zoned earth-fill dam with a central core and 1:0.5 Horizontal to Vertical ratio (H:V) is preferred.

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  • 2.
    Li, Jing
    et al.
    Business School, Lanzhou City University, Lanzhou, China.
    Ameen, Ameen Mohammed Salih
    Department of Water Resources, University of Baghdad, Baghdad, Iraq.
    Mohammad, Thamer Ahmad
    Department of Water Resources, University of Baghdad, Baghdad, Iraq.
    Al-Ansari, Nadhir
    Luleå University of Technology, Department of Civil, Environmental and Natural Resources Engineering, Mining and Geotechnical Engineering.
    Yaseen, Zaher
    Sustainable Developments in Civil Engineering Research Group, Faculty of Civil Engineering, Ton Duc Thang University, Ho Chi Minh City, Vietnam.
    A Systematic Operation Program of a Hydropower Plant Based on Minimizing the Principal Stress: Haditha Dam Case Study2018In: Water, ISSN 2073-4441, E-ISSN 2073-4441, Vol. 10, no 1270, p. 2-20Article in journal (Refereed)
    Abstract [en]

    Dam operation and management have become more complex recently because of the need for considering hydraulic structure sustainability and environmental protect on. An Earthfill dam that includes a powerhouse system is considered as a significant multipurpose hydraulic structure. Understanding the effects of running hydropower plant turbines on the dam body is one of the major safety concerns for earthfill dams. In this research, dynamic analysis of earthfill dam, integrated with a hydropower plant system containing six vertical Kaplan turbines (i.e., Haditha dam), is investigated.In the first stage of the study, ANSYS-CFX was used to represent one vertical Kaplan turbine unit by designing a three-dimensional (3-D) finite element (FE) model. This model was used to differentiate between the effect of turbine units’ operation on dam stability in accordance to maximum and minimum reservoir upstream water levels, and the varying flowrates in a fully open gate condition. In the second stage of the analysis, an ANSYS-static modeling approach was used to develop a 3-D FE earthfill dam model. The water pressure pattern determined on the boundary of the running turbine model is transformed into the pressure at the common area of the dam body with turbines. The model is inspected for maximum and minimum upstream water levels. Findings indicate that the water stress fluctuations on the dam body are proportional to the inverse distance from the turbine region. Also, it was found that the cone and outlet of the hydropower turbine system are the most affected regions when turbine is running. Based on the attained results, a systematic operation program was proposed in order to control the running hydropower plant with minimized principal stress atselected nodes on the dam model and the six turbines.

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  • 3.
    Penghui, Liu
    et al.
    Computer Science Department, Baoji University of Arts and Sciences, Baoji, China.
    Ewees, Ahmed A.
    Computer Department, Damietta University, Damietta, Egypt.
    Hamiye Beyaztas, Beste
    Department of Statistics, Istanbul Medeniyet University, Istanbul, Turkey.
    Qi, Chongchong
    School of Resources and Safety Engineering, Central South University, Changsha, China.
    Salih, Sinan Q.
    Institute of Research and Development, Duy Tan University, Da Nang, Vietnam. Computer Science Department, College of Computer Science and Information Technology, University of Anbar, Ramadi, Iraq .
    Al-Ansari, Nadhir
    Luleå University of Technology, Department of Civil, Environmental and Natural Resources Engineering, Mining and Geotechnical Engineering.
    Bhagat, Suraj Kumar
    Faculty of Civil Engineering, Ton Duc Thang University, Ho Chi Minh City, Vietnam.
    Yaseen, Zaher Mundher
    Sustainable Developments in Civil Engineering Research Group, Faculty of Civil Engineering, Ton Duc Thang University, Ho Chi Minh City, Vietnam.
    Singh, Vijay P.
    Department of Biological and Agricultural Engineering, Texas A&M University, College Station, USA. Zachry Department of Civil Engineering, Texas A&M University, College Station, USA.
    Metaheuristic Optimization Algorithms Hybridized With Artificial Intelligence Model for Soil Temperature Prediction: Novel Model2020In: IEEE Access, E-ISSN 2169-3536, Vol. 8, p. 51884-51904Article in journal (Refereed)
    Abstract [en]

    An enhanced hybrid articial intelligence model was developed for soil temperature (ST) prediction. Among several soil characteristics, soil temperature is one of the essential elements impacting the biological, physical and chemical processes of the terrestrial ecosystem. Reliable ST prediction is signicant for multiple geo-science and agricultural applications. The proposed model is a hybridization of adaptive neuro-fuzzy inference system with optimization methods using mutation Salp Swarm Algorithm and Grasshopper Optimization Algorithm (ANFIS-mSG). Daily weather and soil temperature data for nine years (1 of January 2010 - 31 of December 2018) from ve meteorological stations (i.e., Baker, Beach, Cando, Crary and Fingal) in North Dakota, USA, were used for modeling. For validation, the proposed ANFIS-mSG model was compared with seven models, including classical ANFIS, hybridized ANFIS model with grasshopper optimization algorithm (ANFIS-GOA), salp swarm algorithm (ANFIS-SSA), grey wolf optimizer (ANFIS-GWO), particle swarm optimization (ANFIS-PSO), genetic algorithm (ANFIS-GA),and Dragon y Algorithm (ANFIS-DA). The ST prediction was conducted based on maximum, mean and minimum air temperature (AT). The modeling results evidenced the capability of optimization algorithms for building ANFIS models for simulating soil temperature. Based on the statistical evaluation; for instance, the root mean square error (RMSE) was reduced by 73%, 74.4%, 71.2%, 76.7% and 80.7% for Baker, Beach, Cando, Crary and Fingal meteorological stations, respectively, throughout the testing phase when ANFIS-mSG was used over the standalone ANFIS models. In conclusion, the ANFIS-mSG model was demonstrated as an effective and simple hybrid articial intelligence model for predicting soil temperature based on univariate air temperature scenario.

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  • 4.
    Yaseen, Zaher Mundher
    et al.
    School of Civil Engineering, Faculty of Engineering, Universiti Teknologi Malaysia, Skudai, Johor Bahru, Malaysia.
    Mohtar, Wan Hanna Melini Wan
    Sustainable and Smart Township Research Centre (SUTRA), Faculty of Engineering and Built Environment, Universiti Kebangsaan Malaysia, Bangi, Selangor, Malaysia.
    Ameen, Ameen Mohammed Salih
    Department of Water Resources, University of Baghdad, Baghdad, Iraq.
    Ebtehaj, Isa
    Department of Civil Engineering, Razi University, Kermanshah, Iran.
    Razali, Siti Fatin Mohd
    Sustainable and Smart Township Research Centre (SUTRA), Faculty of Engineering and Built Environment, Universiti Kebangsaan Malaysia, Bangi, Selangor, Malaysia.
    Bonakdari, Hossein
    Department of Civil Engineering, Razi University, Kermanshah, Iran.
    Salih, Sinan Q.
    Institute of Research and Development, Duy Tan University, Da Nang, Vietnam.
    Al-Ansari, Nadhir
    Luleå University of Technology, Department of Civil, Environmental and Natural Resources Engineering, Mining and Geotechnical Engineering.
    Shahid, Shamsuddin
    School of Civil Engineering, Faculty of Engineering, Universiti Teknologi Malaysia, Skudai, Johor Bahru, Malaysia.
    Implementation of Univariate Paradigm for Streamflow Simulation Using Hybrid Data-Driven Model: Case Study in Tropical Region2019In: IEEE Access, E-ISSN 2169-3536, Vol. 7, p. 74471-74481Article in journal (Refereed)
    Abstract [en]

    The performance of the bio-inspired adaptive neuro-fuzzy inference system (ANFIS) models are proposed for forecasting highly non-linear streamflow of Pahang River, located in a tropical climatic region of Peninsular Malaysia. Three different bio-inspired optimization algorithms namely particle swarm optimization (PSO), genetic algorithm (GA), and differential evolution (DE) were individually used to tune the membership function of ANFIS model in order to improve the capability of streamflow forecasting. Different combination of antecedent streamflow was used to develop the forecasting models. The performance of the models was evaluated using a number of metrics including mean absolute error (MAE), root mean square error (RMSE), coefficient of determination ( R2 ), and Willmott’s Index (WI) statistics. The results revealed that increasing number of inputs has a positive impact on the forecasting ability of both ANFIS and hybrid ANFIS models. The comparison of the performance of three optimization methods indicated PSO improved the capability of ANFIS model (RMSE = 7.96; MAE = 2.34; R2=0.998 and WI = 0.994) more compared to GA and DE in forecasting streamflow. The uncertainty band of ANFIS-PSO forecast was also found the lowest (±0.217), which indicates that ANFIS-PSO model can be used for reliable forecasting of highly stochastic river flow in tropical environment.

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