Optimizing Coastal Hydro Turbines: Integrating Artificial Intelligence for Sustainable Energy Conversion

Abstract

As the global quest for sustainable energy intensifies, coastal areas emerge as pivotal zones for harnessing renewable resources. This article explores the transformative role of artificial intelligence (AI) in optimizing energy extraction from hydro turbines near the coast. Leveraging machine learning algorithms, the study addresses key facets, including site selection, predictive modeling, adaptive control, and environmental monitoring. The complexity and variability of coastal environments necessitate innovative solutions for maximizing energy output while minimizing ecological impact. AI proves instrumental in navigating these challenges by providing real-time adaptability and predictive capabilities. This research delves into the application of AI in enhancing the efficiency, reliability, and sustainability of coastal hydro energy extraction. From predicting tidal and wave patterns to dynamically adjusting turbine operations, AI algorithms contribute to the optimization of near-shore hydro energy. The article unfolds insights into how these advancements not only meet the demands of a dynamic coastal ecosystem but also align with global sustainability goals. In conclusion, the integration of AI with coastal hydro turbines marks a paradigm shift, ushering in an era of smart and adaptive energy solutions. This amalgamation not only optimizes energy production but also underscores a commitment to environmentally conscious practices, steering the course toward a cleaner and greener energy landscape. .

Full text

Optimizing Coastal Hydro Turbines: Integrating Artificial
Intelligence for Sustainable Energy Conversion
Seyed Reza Samaei
Post-doctoral, Lecturer of Technical and Engineering Faculty, Science and Research Branch,
Islamic Azad University, Tehran, Iran.
samaei@srbiau.ac.ir
Mohammad Asadian Ghahfarrokhi *
Assistant professor, Department of Marine industries, Science and Research Branch, Islamic
Azad University, Tehran, Iran.
asadian@srbiau.ac.ir
Abstract
As the global quest for sustainable energy intensifies, coastal areas
emerge as pivotal zones for harnessing renewable resources. This article
explores the transformative role of artificial intelligence (AI) in
optimizing energy extraction from hydro turbines near the coast.
Leveraging machine learning algorithms, the study addresses key
facets, including site selection, predictive modeling, adaptive control,
and environmental monitoring. The complexity and variability of
coastal environments necessitate innovative solutions for maximizing
energy output while minimizing ecological impact. AI proves
instrumental in navigating these challenges by providing real-time
adaptability and predictive capabilities. This research delves into the
application of AI in enhancing the efficiency, reliability, and
sustainability of coastal hydro energy extraction. From predicting tidal
and wave patterns to dynamically adjusting turbine operations, AI
algorithms contribute to the optimization of near-shore hydro energy.
The article unfolds insights into how these advancements not only meet
the demands of a dynamic coastal ecosystem but also align with global
sustainability goals. In conclusion, the integration of AI with coastal
hydro turbines marks a paradigm shift, ushering in an era of smart and
adaptive energy solutions. This amalgamation not only optimizes
energy production but also underscores a commitment to
environmentally conscious practices, steering the course toward a
cleaner and greener energy landscape.
.
Keywords: Coastal Hydro Turbines, Artificial Intelligence, Renewable Energy,
Sustainability, Near-Shore Energy.

Introduction
As the global pursuit of sustainable energy intensifies, coastal regions emerge as strategic hubs for
harnessing renewable resources, particularly through the utilization of hydro turbines. Near-shore
hydro energy presents a promising avenue, tapping into the kinetic power of tides, waves, and ocean
currents. This article delves into the transformative role of artificial intelligence (AI) in optimizing
energy extraction from hydro turbines situated along coastlines.
The inherent variability and complexity of coastal environments necessitate innovative approaches for
maximizing energy output and minimizing environmental impact. AI, with its ability to analyze vast
datasets and adapt in real-time, holds the key to overcoming these challenges. This exploration focuses
on how AI algorithms can enhance site selection, predictive modeling, adaptive control systems, and
environmental monitoring in the context of coastal hydro energy.
By integrating AI into the operational framework of hydro turbines near the coast, we aim to not only
optimize energy production but also contribute to the broader goals of sustainability, reliability, and
environmental stewardship. This article unfolds the intricacies of this integration, offering insights into
the potential transformative impact of AI on the future of coastal hydro energy extraction.
Energy can be extracted from marine structures through various technologies. Tidal and wave
energy are commonly harnessed:
I. Tidal Energy: Tidal power generators capture energy from the rise and fall of tides. This can
be achieved through tidal stream systems or tidal range systems.
II. Wave Energy: Devices like oscillating water columns or point absorbers convert the up and
down motion of waves into energy. They can be deployed offshore to capture wave power.
III. Ocean Thermal Energy Conversion (OTEC): OTEC utilizes the temperature difference
between warm surface water and cold deep water to generate power. It's a less common but
promising technology.
IV. Salinity Gradient Power: Differences in salinity between freshwater and seawater can be
exploited to generate energy using processes like pressure retarded osmosis.
These technologies aim to harness the vast, consistent energy potential of the oceans for sustainable
power generation.
Coastal areas offer opportunities for harnessing energy through hydro turbines in various ways:
✓ Tidal Turbines: Placed on the seabed, tidal turbines capture energy from the movement of
tides. As tidal currents flow in and out, these turbines spin, converting kinetic energy into
electricity.
✓ River Estuary Turbines: Installing hydro turbines in river estuaries near the coast allows the
capture of energy from both river flow and tidal movements, providing a hybrid energy
source.
✓ Ocean Current Turbines: Similar to tidal turbines but designed for open ocean currents,
these turbines can be positioned near coastal regions with strong ocean currents.
✓ Underwater Currents: Submerged turbines can harness energy from underwater currents
near the coast, converting the kinetic energy of moving water into electrical power.
These technologies leverage the kinetic energy present in coastal waters, offering a reliable and
predictable source of renewable energy.
Various types of water turbines near the coast for energy extraction include:
➢ Tidal Stream Turbines: Placed on the seabed, these turbines generate electricity by capturing
kinetic energy from the horizontal flow of tidal currents.
➢ Tidal Range Turbines (Barrage): Tidal barrages are structures built across tidal inlets,
incorporating turbines to harness energy from the rise and fall of the tides.

➢ River Turbines: Installed in rivers near the coast, these turbines utilize river flow to generate
electricity. They are especially effective in estuarine regions where river meets the sea.
➢ Ocean Current Turbines: Positioned in open ocean currents near the coast, these turbines
extract energy from continuous ocean flows, providing a consistent power source.
➢ Wave Energy Converters: Devices like point absorbers or oscillating water columns near the
coast convert the up-and-down motion of waves into mechanical energy, which is then
converted into electricity.
These turbine technologies offer a range of options for extracting energy from coastal waters,
contributing to sustainable and renewable energy solutions.
Using hydro turbines near the coast for energy extraction offers several advantages:
1. Renewable Resource: Coastal hydro turbines harness the power of tides, waves, and ocean
currents, providing a renewable and sustainable energy source.
2. Predictable and Reliable: Tidal patterns and ocean currents are highly predictable, allowing
for reliable energy generation. This predictability enhances grid stability.
3. High Energy Density: Coastal areas often experience strong tidal currents and wave actions,
providing high energy density for efficient power generation.
4. Low Greenhouse Gas Emissions: Hydro turbines produce electricity without direct
emissions of greenhouse gases, contributing to cleaner energy and helping mitigate climate
change.
5. Base-load Power Potential: Tidal and ocean current energy can provide a continuous and
steady power supply, making it suitable for base-load power generation, complementing
intermittent renewables.
6. Reduced Land Impact: Coastal hydro turbines typically require less land compared to large-
scale terrestrial renewable projects, minimizing environmental impact.
7. Proximity to Population Centers: Coastal regions are often near major population centers,
reducing transmission losses and costs associated with transporting electricity.
8. Long Lifespan: Hydro turbines have a long operational life, providing a stable and durable
energy generation infrastructure.
9. Job Creation: The development and maintenance of coastal hydro turbine projects can
contribute to local job creation and economic growth.
10. Diversification of Energy Mix: Integrating coastal hydro turbines into the energy mix
diversifies sources, enhancing energy security and resilience against fluctuations in other
power generation methods.
These advantages make coastal hydro turbines a promising component of a sustainable and reliable
energy portfolio.
Integrating artificial intelligence (AI) with hydro turbines near the coast can enhance efficiency,
performance, and overall energy extraction. Here are some ways AI can be applied:
Optimized Operations: AI algorithms can analyze real-time data, including tidal patterns,
wave heights, and turbine performance, to optimize the operation of hydro turbines for
maximum energy extraction.
Predictive Maintenance: AI-powered predictive maintenance models can forecast potential
turbine issues based on historical data and sensor readings, reducing downtime and extending
the lifespan of the equipment.
Adaptive Control Systems: AI can dynamically adjust turbine settings based on changing
environmental conditions, ensuring optimal energy capture in varying tidal and wave
scenarios.

Machine Learning for Resource Assessment: ML models can analyze historical data to
better understand the local hydrodynamic conditions, helping in site selection and turbine
placement for improved energy extraction.
Energy Forecasting: AI can predict tidal and wave patterns with high accuracy, enabling
better forecasting of energy production. This information aids in grid management and energy
distribution planning.
Dynamic Scheduling: AI algorithms can adapt turbine operation schedules in response to
changing energy demands, enhancing the integration of coastal hydro energy into the broader
power grid.
Fault Detection and Diagnosis: AI can identify and diagnose issues in real-time, allowing for
rapid response and minimizing the impact of turbine malfunctions on energy production.
Learning Algorithms for Efficiency Improvement: Machine learning algorithms can
continuously learn from operational data, leading to ongoing improvements in turbine
efficiency and energy extraction.
By leveraging AI in conjunction with hydro turbines near the coast, it is possible to optimize energy
production, reduce operational costs, and enhance the overall sustainability and reliability of coastal
energy extraction systems.
Artificial intelligence algorithms can be employed with data to optimize the extraction of energy
from near-shore hydro turbines in various ways:
❖ Data Analytics for Resource Assessment: AI algorithms can analyze historical and real-time
data, including tidal patterns, wave heights, and water currents, to assess the potential energy
resources at specific near-shore locations. This aids in selecting optimal sites for hydro turbine
deployment.
❖ Predictive Modeling: Machine learning models can predict tidal and wave patterns, allowing
for accurate forecasting of energy production. This enables efficient scheduling of turbine
operations to align with peak energy generation periods.
❖ Optimized Turbine Control: AI-powered control systems can dynamically adjust turbine
settings based on incoming data. This includes adapting to changes in tidal flow, wave
conditions, and variations in energy demand, ensuring the turbines operate at peak efficiency.
❖ Condition Monitoring and Predictive Maintenance: AI algorithms analyze sensor data
from turbines to predict potential failures or maintenance needs. This proactive approach
minimizes downtime, increases turbine lifespan, and optimizes energy extraction.
❖ Adaptive Power Output: AI-controlled systems can respond in real-time to fluctuations in
energy demand by adjusting the power output of near-shore hydro turbines. This helps
maintain grid stability and ensures efficient energy distribution.
❖ Learning Algorithms for Efficiency Improvement: Machine learning algorithms
continuously learn from operational data, identifying patterns and trends that can be used to
improve the overall efficiency of energy extraction over time.
❖ Integration with Smart Grids: AI facilitates the integration of near-shore hydro energy into
smart grid systems. By analyzing data on energy consumption, production, and grid
conditions, AI algorithms optimize the contribution of hydro turbines to meet the demands of
the grid.
❖ Environmental Impact Monitoring: AI can monitor and assess the environmental impact of
hydro turbine operations, ensuring that energy extraction is done sustainably and with minimal
ecological disturbance.
By combining artificial intelligence with data analytics, near-shore hydro turbines can be operated
more efficiently, enhancing their contribution to the renewable energy landscape.

Designing a complete plan for energy extraction from hydro turbines near the coast involves several
steps, utilizing various algorithms and technologies. Here's a comprehensive outline:
1. Site Selection and Resource Assessment:
• Objective: Identify optimal locations for hydro turbine deployment based on tidal patterns,
wave characteristics, and proximity to the coast.
• Algorithm: Use machine learning (ML) algorithms for data analytics, incorporating historical
data on tidal flows, wave heights, and coastal conditions.
• Tools: Geographic Information System (GIS), ML regression models.
2. Turbine Technology Selection:
• Objective: Choose the most suitable turbine type for the identified coastal site.
• Algorithm: Decision-making algorithms considering factors such as water depth, flow
velocity, and turbine efficiency.
• Tools: Multi-Criteria Decision Analysis (MCDA).
3. Sensor Deployment and Data Collection:
• Objective: Install sensors to gather real-time data on tidal movements, wave characteristics,
and turbine performance.
• Algorithm: Implement data acquisition systems with sensor fusion techniques for
comprehensive data collection.
• Tools: Internet of Things (IoT) devices, Data Fusion algorithms.
4. Predictive Modeling for Energy Forecasting:
• Objective: Develop models to predict tidal and wave patterns for accurate energy production
forecasting.
• Algorithm: Time-series forecasting algorithms (e.g., Long Short-Term Memory - LSTM) for
predicting tidal and wave behavior.
• Tools: Python (using libraries like TensorFlow or PyTorch).
5. Adaptive Turbine Control System:
• Objective: Implement an AI-driven control system to optimize turbine operations based on
real-time data.
• Algorithm: Reinforcement learning algorithms for adaptive control.
• Tools: OpenAI Gym, Proximal Policy Optimization (PPO).
6. Predictive Maintenance:
• Objective: Reduce downtime and extend turbine lifespan by predicting maintenance needs.
• Algorithm: Condition Monitoring using anomaly detection algorithms (e.g., Isolation Forest,
One-Class SVM).
• Tools: Predictive Maintenance Platforms.
7. Integration with Smart Grid:
• Objective: Align energy production with grid demands for efficient power distribution.
• Algorithm: Demand response algorithms to adjust turbine output based on grid conditions.
• Tools: SCADA systems, Demand Response Management Systems (DRMS).
8. Environmental Impact Monitoring:
• Objective: Monitor and mitigate environmental impact.
• Algorithm: Environmental Impact Assessment (EIA) algorithms analyzing data on marine
life, water quality, etc.
• Tools: Remote sensing technology, EIA software.
9. Continuous Improvement:
• Objective: Use machine learning for ongoing efficiency improvements based on operational
data.
• Algorithm: Online learning algorithms for continuous optimization.
• Tools: Incremental Machine Learning frameworks.
10. Regulatory Compliance and Stakeholder Engagement:

• Objective: Ensure compliance with regulations and engage stakeholders.
• Algorithm: Compliance management algorithms for tracking and adhering to regulatory
requirements.
• Tools: Compliance management software.
11. Monitoring and Reporting:
• Objective: Regularly monitor performance and generate reports for stakeholders.
• Algorithm: Data visualization and reporting tools.
• Tools: Power BI, Tableau.
By combining these elements, the plan provides a comprehensive approach to extracting energy
from hydro turbines near the coast, integrating AI, machine learning, and various technologies for
efficient and sustainable energy production.
1. Input Data:
• Historical Tidal and Wave Data:
o Tide height and patterns.
o Wave heights and frequencies.
• Geographic Information:
o Bathymetric data for water depth.
o Coastal topography and proximity to population centers.
• Meteorological Data:
o Wind speed and direction.
o Temperature variations.
• Turbine Specifications:
o Turbine type and efficiency.
o Design parameters.
• Real-time Sensor Data:
o Tidal currents, wave heights, and turbine performance.
o Environmental conditions (temperature, salinity).
2. Analysis Steps:
Site Selection and Resource Assessment:
• Utilize machine learning algorithms to analyze historical tidal and wave data.
• Apply decision-making algorithms for turbine site selection based on geographic information.
Turbine Technology Selection:
• Employ Multi-Criteria Decision Analysis (MCDA) algorithms to choose the most suitable
turbine type.
Sensor Deployment and Data Collection:
• Implement data acquisition systems with sensor fusion techniques.
• Use IoT devices for real-time data collection.
Predictive Modeling for Energy Forecasting:
• Develop time-series forecasting models (e.g., LSTM) for predicting tidal and wave patterns.
Adaptive Turbine Control System:
• Implement reinforcement learning algorithms for adaptive turbine control based on real-time
data.
Predictive Maintenance:
• Use anomaly detection algorithms (e.g., Isolation Forest) for predictive maintenance.
Integration with Smart Grid:
• Apply demand response algorithms to align turbine output with grid demands.
Environmental Impact Monitoring:

• Utilize Environmental Impact Assessment (EIA) algorithms to monitor and mitigate
environmental impact.
Continuous Improvement:
• Implement online learning algorithms for continuous optimization based on operational data.
Regulatory Compliance and Stakeholder Engagement:
• Apply compliance management algorithms to track and adhere to regulatory requirements.
Monitoring and Reporting:
• Use data visualization and reporting tools (e.g., Power BI, Tableau) for performance
monitoring and stakeholder reporting.
3. Output Data:
• Site Selection and Resource Assessment:
o Identified optimal turbine deployment sites.
o Assessment reports on tidal and wave energy potential.
• Turbine Technology Selection:
o Selected turbine type and specifications.
• Sensor Deployment and Data Collection:
o Real-time data streams from deployed sensors.
• Predictive Modeling for Energy Forecasting:
o Energy production forecasts based on tidal and wave predictions.
• Adaptive Turbine Control System:
o Real-time adjustments to turbine operations.
• Predictive Maintenance:
o Predicted maintenance schedules and alerts.
• Integration with Smart Grid:
o Aligned turbine output with grid demand.
• Environmental Impact Monitoring:
o Reports on environmental impact and mitigation measures.
• Continuous Improvement:
o Algorithms for ongoing efficiency improvements.
• Regulatory Compliance and Stakeholder Engagement:
o Compliance reports and stakeholder engagement strategies.
• Monitoring and Reporting:
o Performance dashboards and regular reports for stakeholders.
These outputs collectively contribute to the effective and sustainable extraction of energy from
hydro turbines near the coast, ensuring optimal performance, minimal environmental impact, and
compliance with regulatory standards.

Conclusion
In conclusion, the integration of artificial intelligence (AI) into the realm of coastal hydro energy
extraction marks a significant stride toward a more efficient, sustainable, and adaptive future. The
synergistic marriage of advanced AI algorithms with near-shore hydro turbines has showcased
remarkable potential across various facets of the energy generation landscape.
The application of AI in site selection, predictive modeling, adaptive control systems, and
environmental monitoring has demonstrated its prowess in optimizing energy output while navigating
the dynamic nature of coastal ecosystems. The predictive capabilities of AI, coupled with real-time
adaptability, have proven instrumental in addressing the challenges associated with the inherent
variability of tides, waves, and ocean currents.
As we chart the course forward, it becomes evident that AI is not merely a technological augmentation
but a catalyst for reshaping the very fabric of coastal hydro energy systems. The continuous learning
and improvement mechanisms embedded in AI algorithms pave the way for a resilient infrastructure
that can adapt to evolving environmental conditions and energy demands.
Beyond the technical aspects, the integration of AI aligns with the broader global imperative for
sustainability. By minimizing environmental impact through optimized operations and predictive
maintenance, AI-driven coastal hydro energy contributes to a cleaner and greener energy landscape.
In essence, this article has illuminated the transformative potential of AI in unlocking the full
capabilities of coastal hydro turbines. As we embrace the era of smart and adaptive energy solutions,
the marriage of AI and hydro energy stands as a beacon, guiding us toward a future where coastal
regions not only harness the power of the oceans but do so in harmony with the delicate ecosystems
they encompass.

References
[1] Seyed Reza Samaei, Madjid Ghodsi Hassanabad, Mohammad Asadian ghahfarrokhi, Mohammad
Javad Ketabdari, "Numerical and experimental investigation of damage in environmentally-
sensitive civil structures using modal strain energy (case study: LPG wharf)". Int. J. Environ. Sci.
Technol. 18, 1939–1952 (2021). https://doi.org/10.1007/s13762-021-03321-2 (Link)
[2] Seyed Reza Samaei, Madjid Ghodsi Hassanabad, Mohammad Asadian Ghahfarrokhi, Mohammad
Javad Ketabdari, " Detecting damage location and severity in the leg of SA20 jack up rig (Hull
110) using an iterative modal strain energy method," Journal of xi'an university of architecture &
technology, Page No: 213-236, https://doi.org/10.37896/JXAT2.1/30319 (Link)
[3] Seyed Reza Samaei, Farhood Azarsina & Mohammad Asadian Ghahferokhi, “Numerical
Simulation of Floating Pontoon Breakwater with ANSYS AQWA Software and Validation of the
Results with Laboratory Data”, Bulletin de la Société Royale des Sciences de Liège (Link)
[4] Seyed Reza Samaei, Madjid Ghodsi Hassanabad, (2024): Innovations in Water Treatment:
Navigating Sustainable Solutions for the Future, The 12th International Conference on Civil,
Architecture and urban Management, Tbilisi, Georgia.
[5] Seyed Reza Samaei, (2024): Artificial Intelligence in the survey of citizens' satisfaction with the
services of an organization-Case study: Tehran Municipality, The 12th International Conference on
Civil, Architecture and urban Management, Tbilisi, Georgia.
[6] Seyed Reza Samaei, Madjid Ghodsi Hassanabad, (2024): The Crucial Interplay of Seas, Marine
Industries, and Artificial Intelligence in Sustainable Development, Eighth International Conference
on Technology Development in Oil, Gas, Refining and Petrochemicals.
[7] Mohsen Ashoori, Seyed Reza Samaei, The Essential Role of Coaching in Developing Movement
Skills: A Holistic Approach, (2024): The 8th International Conference on New Researches in
Sports Sciences and Physical Education.
[8] Seyed Reza Samaei, Madjid Ghodsi Hassanabad, (2024): Investigating and providing solutions to
reduce air pollution in large cities, 2th International Conference on Creative achievements of
architecture, urban planning, civil engineering and environment in the sustainable development of
the Middle East.
[9] Seyed Reza Samaei, Madjid Ghodsi Hassanabad, (2024): Management of Surface and Flood Water
Collection in Tehran Metropolis Using Artificial Intelligence, 2th International Conference on
Creative achievements of architecture, urban planning, civil engineering and environment in the
sustainable development of the Middle East.
[10] Seyed Reza Samaei, Madjid Ghodsi Hassanabad, (2024): Marine industries in the coasts of
Makran with the view of balanced and sustainable development of internal and external, 2th
International Conference on Creative achievements of architecture, urban planning, civil
engineering and environment in the sustainable development of the Middle East.
[11] Seyed Reza Samaei, Madjid Ghodsi Hassanabad, (2024): The transformative role of artificial
intelligence in engineering sciences with an emphasis on civil engineering and marine industries,
2th International Conference on Creative achievements of architecture, urban planning, civil
engineering and environment in the sustainable development of the Middle East.
[12] Seyed Reza Samaei, Madjid Ghodsi Hassanabad, (2024): THE ROLE OF ARTIFICIAL
INTELLIGENCE IN EARTHQUAKE ENGINEERING: PREVENTION, RESPONSE AND
IMPROVEMENT OF STRUCTURES, 9th International Conference on Seismology and
Earthquake Engineering.
[13] Seyed Reza Samaei, (2023): Investigating the impact of managers' competence on employees'
performance (case study - Transport and Traffic Organization of Tehran Municipality), The 17th
International Conference on Management, Economy and Development.
https://civilica.com/l/115404/

[14] Seyed Reza Samaei, (2023), Investigating the role of managers on the efficiency of employees of
the Transport and Traffic Organization of Tehran Municipality. The 8th International and National
Conference on Management, Accounting and Law Studies, Tehran. https://civilica.com/l/112315/
[15] Seyed Reza Samaei, Madjid Ghodsi Hassanabad, (2024): Machine Learning Approach for
Structural Health Monitoring and Damage Detection, 8th International Conference on Civil
Engineering, Architecture, Urban Planning with Sustainable Development Approach.
[16] Seyed Reza Samaei, Mohammad Asadian Ghahferokhi, Farhood Azarsinai, " Experimental study
of two types of simple and step floating pontoon breakwater in regular waves," International
Journal of Marine Science and Environment (Link)
[17] Seyed Reza Samaei; Madjid Ghodsi Hassanabad. "Damage location and intensity detection in
tripod jacket substructure of wind turbine using improved modal strain energy and genetic
algorithm". Journal of Structural and Construction Engineering, 9, 4, 2022, 182-202. doi:
10.22065/jsce.2021.294103.2488 (Link)
[18] Seyed Reza Samaei; Madjid Ghodsi Hassanabad; Mohammad Asadian ghahfarrokhi; Mohammad
Javad Ketabdari. "Numerical and experimental study to identify the location and severity of
damage at the pier using the improved modal strain energy method-Case study: Pars Asaluyeh LPG
export pier". Journal of Structural and Construction Engineering, 8, Special Issue 3, 2021, 162-
179. doi: 10.22065/jsce.2020.246425.2225 (Link)
[19] Seyed Reza Samaei, Madjid Ghodsi Hassanabad, Mohammad Asadian ghahfarrokhi, Javad
Ketabdari. “Structural health monitoring of offshore structures using a modified modal strain
energy method (Case study: four-leg jacket substructure of an offshore wind turbine).” Marine
Engineering 2020; 16 (32) :119-130 (Link)
[20] Seyed Reza Samaei; Madjid Ghodsi Hassanabad; amin karimpor zahraei. "Identification of
Location and Severity of Damages in the Offshore wind Turbine Tripod Platform by Improved
Modal Strain Energy Method". Analysis of Structure and Earthquake, 18, 3, 2021, 51-62. doi:
10.30495/civil.2021.686956 (Link)
[21] Seyed Reza Samaei; Madjid Ghodsi Hassanabad; Mohammad asadian ghahfarrokhi; mohammad
javad ketabdari. "Investigation of location and severity of damage in four-legged offshore wind
turbine stencil infrastructure by improved modal strain energy method". Analysis of Structure and
Earthquake, 17, 3, 2021, 79-90. (Link)
[22] Seyed Reza Samaei, Farhood Azarsina, Mohammad Asadian Ghahfarrokhi. " Numerical
simulation of floating pontoon breakwater with Ansys Aqua software and validation of results with
laboratory data.", The third national conference on recent innovations in civil engineering,
architecture and urban planning, 2016.
[23] Seyed Reza Samaei, Farhood Azarsina, Mohammad Asadian. " Numerical simulation of floating
pontoon breakwater with Ansys Aqua software and validation of results with laboratory data.", The
third national conference on recent innovations in civil engineering, architecture and urban
planning, 2016 (Link)
[24] Seyed Reza Samaei, Mohammad Asadian. " Introducing two types of simple and stepped pontoon
floating breakwaters and analysis of wave passage coefficient by laboratory method.", The third
national conference on recent innovations in civil engineering, architecture and urban planning,
2016 (Link)
[25] Seyed Reza Samaei, (2024): Using Artificial Intelligence to Increase Urban Resilience: A Case
Study of Tehran, 13th International Conference On Advanced Research in Science, Engineering and
Technology, Brussels, Belgium.
[26] Seyed Reza Samaei, (2024): A Comprehensive Algorithm for AI-Driven Transportation
Improvements in Urban Areas, 13th International Conference On Advanced Research in Science,
Engineering and Technology, Brussels, Belgium.