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Artificial Intelligence for Real-time Hydrological Modelling and Forecasting
Dr Amin Talei Associate Professor PhD |
Artificial intelligence ( AI ) tools have been widely used in hydrological modeling , particularly rainfall-runoff modeling , rainfall prediction , and flood forecasting . Although these techniques have shown promising results through countless applications in resolving hydrological problems , the conventional AI techniques such artificial neural network and neuro-fuzzy systems suffer from |
some issues such as poor peak estimation , extensive network structure , and poor adaptability . My research team works on advanced AI-based algorithms with self-adaptive nature , efficient structure , and high performance in extreme events prediction using state-of-the-art methods such as episodic memory .
In another study , my team is developing an AI-based technique
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for acoustic rainfall sensing with potential application for crowdsourcing . In this study , the acoustic features of rainfall audio , which is the result of raindrops ' impact with surfaces , will be extracted and analyzed . These features will then be associated with the actual rainfall intensity through AI-based techniques such as neuro-fuzzy systems and deep learning . |
CIVIL ENGINEERING
Research expertise : Hydrological Modelling & Forecasting ; Sustainable Urban Water Management
E : amin . talei @ monash . edu T : + 603 5514 5648
Data-driven modelling techniques for capturing rainfall-runoff process .
Concrete Engineering and Technology : Translating from Material Scale to Structural and Life-Cycle Performance
Dr Sudharshan Raman Associate Professor PhD , MASCE , FCABE , CBuildE
Research expertise : Structural materials and systems ; Cement and cementitious composites ; Concrete Engineering ; Concrete Structures ; Sustainable Construction .
E : sudharshan . raman @ monash . edu T : + 603 5514 6337
Concrete is the most widely used construction material across the globe and is envisaged to be so in the coming decades . The focus of our group is on concreteand cementitious compositebased structural materials and systems , their structural and life-cycle performance , and field applications . We capitalize on cutting-edge experimental , analytical and computational tools and technology , to drive innovation in these areas . Some project examples include :
• Circular economy and valorization of waste materials in construction : The work of the group is directed towards adopting and optimising the combined utilisation of industrial by-products , agricultural waste , recycled and alternative aggregates , as well as construction and demolition waste , in the field-application and structural performance of concrete .
• High-performance-fibrereinforced-cementitious composites : State-of-the-art computational-data driven techniques , and advanced numerical and experimental solutions are being adopted in the design , development and optimisation of prefabricated fibre-reinforced concrete structural components , which is envisaged to result in innovative , lightweight , sustainable and resource efficient design solutions for composite fibre-reinforced concrete prefabricated structural elements .
Acoustic rainfall sensing using AI-based techniques .
Ultra-High Performance Fibre- Reinforced Concrete ( UHPFRC ) specimen , exhibiting unconfined compressive strength beyond 150 MPa [© Hiew , Raman , et al . ( 2021 )]
Experimental testing of reinforced concrete column strengthened with a novel high-strength selfcompacting ferrocomposite system [ © Raman et al . ( 2021 ) ]
R E S E A R C H E R P R O F I L E 2022 / 2023