About the book

  • Discusses the application of metaheuristic algorithms to key optimization problems in civil engineering.

  • Highlights the growing need for greater accuracy, efficiency, and speed in analysis and design due to advancements in civil engineering technologies.

  • Explains the development of novel methods for the optimal design of complex, real-world systems with large numbers of elements.

  • Designed for scientists, engineers, and students interested in applying newly developed metaheuristics to practical problems.

  • Presents concepts applicable not only to civil engineering but also to mechanical, electrical, and industrial engineering optimization problems.

  • Serves as an essential resource for professionals using optimization methods in design and for students and researchers focused on structural optimization.

  1. Introduction
  2. Optimum Stacking Sequence Design of Composite Laminates for Maximum Buckling Load Capacity
  3. Optimum Design of Castellated Beams with Composite Action and Semi-rigid Connection
  4. Optimal Design of Steel Curved Roof Frames by Enhanced Vibrating Particles System Algorithm
  5. Geometry and Sizing Optimization of Steel Pitched Roof Frames
  6. Two-Stage Optimal Sensor Placement Using Graph-Theory and Evolutionary Algorithms
  7. The Charged System Search Algorithm for Adaptive Node Moving Refinement in Discrete Least-Squares Meshless Method
  8. Performance-Based Multi-objective Optimization of Large Steel Structures
  9. Optimal Seismic Design of Steel Plate Shear Walls Using CBO and ECBO Algorithms
  10. Colliding Bodies Optimization Algorithm for Structural Optimization of Offshore Wind Turbines with Frequency Constraints
  11. Colliding Bodies Optimization for Analysis and Design of Water Distribution Systems
  12. Optimization of Tower Crane Location and Material Quantity Between Supply and Demand Points
  13. Optimization of Building Components with Sustainability Aspects in BIM Environment
  14. Multi-objective Optimization of Construction Site Layout
  15. Multi-objective Electrical Energy Scheduling in Smart Homes Using Ant Lion Optimizer and Evidential Reasoning

 

  • Hardcover ISBN: 978-3-030-45472-2
  • Softcover ISBN: 978-3-030-45475-3
  • eBook ISBN: 978-3-030-45473-9
  • DOI: https://doi.org/10.1007/978-3-030-45473-9  
About the book
  1. Argues that artificial intelligence requires a new foundation, shifting from human-centric intelligence to ecological intelligence.
  1. Proposes that grounding AI within the Earth System is essential to achieve the promise of a better world.
  1. Emphasizes principles such as emergence, interconnectedness, and real-time feedback as core to future AI development.
  1. Suggests this shift could lead to a historic transformation — an age of re-integration.
  1. Identifies a critical weakness in current AI thinking that may hinder positive global transformation.
  1. Highlights systems theory as a necessary foundation for technologies aiming to improve society.
  1. Advocates for a radical, ecology-based rethinking of AI design and purpose.
  1. Explains why ecosystem intelligence, rather than human intelligence, provides a stronger framework for AI.
  1. Examines the potential positive impacts of this approach across society, the environment, and the economy.
  1. Discusses practical pathways for delivering this transformative shift.

 

  1. Should I Stay or Should I Go? Ethics in AI
  • Choosing an ethical framework
  • The strange case of Asimov’s laws
  • Free will and moral judgement
  • The confused Owl of Minerva: dangers of a moral vacuum
  • Who’s in charge of the big bad wolf?
  • What should a declaration of AI rights look like?
  1. Gender, Race, Culture and Fear
  • Gender issues in AI
  • Racial issues in AI
  • Cultural issues in AI
  • Fear and loathing in AI
  1. The Thinker: Human Intelligence
  • Human intelligence: Carolus Linnaeus and his wise, wise men
  • So what is human intelligence?
  • Philosophy and intelligence: the framing of our thoughts
  1. Other Modes of Intelligence: Thinking Outside the Human Box
  • Animal intelligence: Machiavellian sentience and the wisdom of the swarm
  • Plant intelligence: headless, brainless, dispersed intelligence
  • Microbial intelligence: gene-swapping revelry in the quorum
  • Ecosystem intelligence: systems thinking in the cathedral of thought
  • Systems are non-linear
  • Systems are emergent
  • Systems are sub-optimal
  • Systems rely of real-time feedback
  1. Highway to Hell: The Existentialist Threat Facing Humankind
  • A brief history of our path towards destruction
  • The five clear road signs that point towards criticality
  • Why ecological damage matters to us
  • Adam Smith and his invisible hand
  • Kuznets and his curve: how ninety five percent speculation led us badly astray　
  1. Forget the Romans. What has AI ever done for us?
  • AI and economics: the best of things or the worst of things?
  • AI and society
  • AI and the environment
  • Technology and sustainability: bellicose bedfellows or Romeo and Juliet?
  1. Imagining a New World
  • The swallow whose nest was stolen: a salutary tale
  • Blinded by the bling: dashboard dogs and a disappearing sea
  • What needs changed and what change do we need?
  • The chains that bind: taking responsibility for our footprints
  • The Ogiek people and the new, improved invisible hand
  • Lessons from the edge of the world: The St Kildan legacy
  • The Garden of Eden complex: how not to fix the world
  • The three cornerstones: diversity, resilience and integration
  • The central role of AI in feedback: shaping our new world
  1. Barriers to Change
  • The five philosophical barriers
  • Structural barriers to change
  • The seven dragons: psychological barriers
  • How AI can help overcome these barriers
  1. Transition
  • The nature of transition
  • Studies in transition
  • Why societal change is key
  • How to manage societal change
  • Requiem for the King of Phrygia

 

  • Paperback ISBN: 9780367436544
  • Hardback ISBN: 9780367152000
  • eBook ISBN: 9780429055676
About the book
  1. Highlights land use and water resources as critical environmental issues requiring effective conservation, management, and maintenance practices.
  2. Emphasizes the role of engineering techniques in addressing challenges related to flood control, soil conservation, rainfall–runoff processes, and groundwater hydrology.
  3. Provides foundational hydrological principles to support practical strategies for watershed planning, design, and management.
  4. Introduces modern approaches, including machine learning and neural networks for runoff prediction.
  5. Explores the use of artificial intelligence for forecasting groundwater fluctuations.
  6. Covers hydrologic analysis and design integrated with soil conservation through proper watershed management techniques.
  7. Analyzes land erosion and sediment transport across watersheds of varying scales.
  8. Presents systematic methodologies for runoff estimation.
  9. Discusses water harvesting and the development of water-yield catchments.
  10. Serves as a valuable resource for students, environmental consultants, water resource engineers, and researchers in water science and engineering.

 

  1. Introduction to Watershed Management
  2. Characteristics of Watershed
  3. Soil Erosion and Its Control
  4. Water Harvesting
  5. Water Quality Management in Watershed
  6. Groundwater
  7. Flood and Drought
  8. Sediment Sampling and Transport
  9. Runoff
  10. Application of Artificial Intelligence for Prediction of Ground Water Fluctuation
  11. Prediction of Flood Using Hybrid ANFIS-FFA Approaches in Barak River Basin
  12. Prophecy of Sediment Load Using Hybrid AI Approaches at Various Gauge Station in Mahanadi River Basin, India
  13. Scheming of Runoff Using Hybrid ANFIS for a Watershed: Western Odisha, India
  14. Application of Hybrid Neural Network Techniques for Drought Forecasting

 

  • eBook ISBN: 9781003168041
  • DOI: https://doi.org/10.1201/9781003168041
About the book
  • Showcases cutting-edge applications of machine learning techniques in disaster management.
  • Focuses on monitoring, analyzing, and forecasting hydro-meteorological variables.
  • Highlights predictive modeling as a key discipline for early warning and natural hazard forecasting.
  • Integrates expertise from diverse fields including numerical weather forecasting, meteorology, hydrology, engineering, agriculture, economics, and disaster policy-making.
  • Promotes an interdisciplinary framework for developing robust hazard risk mitigation models.
  • Advances the application of artificial intelligence in decision-support systems for disaster management and policy formulation.
  • Serves as a valuable reference for graduate students, academics, scientists, and professionals in disaster management, AI, and environmental sciences.
  1. Drought Index Prediction Using Data Intelligent Analytic Models: A Review
  2. Bayesian Markov Chain Monte Carlo-Based Copulas: Factoring the Role of Large-Scale Climate Indices in Monthly Flood Prediction
  3. Gaussian Naïve Bayes Classification Algorithm for Drought and Flood Risk Reduction
  4. Hydrological Drought Investigation Using Streamflow Drought Index
  5. Intelligent Data Analytics Approaches for Predicting Dissolved Oxygen Concentration in River: Extremely Randomized Tree Versus Random Forest, MLPNN and MLR
  6. Evolving Connectionist Systems Versus Neuro-Fuzzy System for Estimating Total Dissolved Gas at Forebay and Tailwater of Dams Reservoirs
  7. Modulation of Tropical Cyclone Genesis by Madden–Julian Oscillation in the Southern Hemisphere
  8. Intelligent Data Analytics for Time Series, Trend Analysis and Drought Indices Comparison
  9. Conjunction Model Design for Intermittent Streamflow Forecasts: Extreme Learning Machine with Discrete Wavelet Transform
  10. Systematic Integration of Artificial Intelligence Toward Evaluating Response of Materials and Structures in Extreme Conditions
  11. Machine Learning to Derive Unified Material Models for Steel Under Fire Conditions
  12. Energy Dissipation in Rough Chute: Experimental Approach Versus Artificial Intelligence Modeling
  13. Morphological Changes of Floodplain Reach of Jhelum River, India, from 1984 to 2018
  14. Spatial Modeling of Soil Erosion Susceptibility with Support Vector Machine
  15. Spatial Prediction of Landslide Susceptibility Using Random Forest Algorithm
  16. Artificial Neural Networks for Prediction of Steadman Heat Index
  17. Daily Flood Forecasts with Intelligent Data Analytic Models: Multivariate Empirical Mode Decomposition-Based Modeling Methods
  18. Machine Learning Method in Prediction Streamflow Considering Periodicity Component
  19. Empirical Model for the Assessment of Climate Change Impacts on Spatial Pattern of Water Availability in Nigeria

 

  • Hardcover ISBN: 978-981-15-5771-2
  • Softcover ISBN: 978-981-15-5774-3
  • eBook ISBN: 978-981-15-5772-9
  • DOI: https://doi.org/10.1007/978-981-15-5772-9
About the book
  • Emphasizes the crucial role of soft computing applications in civil engineering design.
  • Highlights the need to balance safety, aesthetics, cost-efficiency, and environmental considerations in engineering projects.
  • Explains the value of advanced optimization techniques for complex systems involving multi-constraint, multi-objective, and control problems requiring iterative differential equation solutions.
  • Describes the inspiration behind metaheuristic algorithms, which draw from natural processes to solve optimization challenges.
  • Notes that combining features of different AI-based algorithms can enhance precision, computational efficiency, and convergence speed.
  • Presents the book as a timely resource summarizing recent advancements in civil engineering optimization.
  • Covers both traditional metaheuristic approaches and emerging trends integrating artificial intelligence and machine learning for predicting optimal solutions.
  • Aims to streamline lengthy optimization processes through intelligent computational methods.
  • Includes chapters addressing diverse civil engineering applications, from fundamental concepts to advanced adaptations.
  • Designed as a comprehensive academic resource for undergraduate and graduate engineering students.
  • Intended to serve as a reference text for university courses across various civil engineering disciplines.
  • Edited by experts who will contribute to most chapters, potentially incorporating recent research projects and postgraduate theses based on availability of high-quality work.
  1. Introduction to Data Analysis and Machine Learning Applications in Civil Engineering
  2. Application of Artificial Intelligence (AI) in Civil Engineering
  3. Machine Learning Applications in Structural Engineering
  4. A Multi-objective Optimal Design Process for Determination of Link Capacity Expansions
  5. IoT with Deep Learning in Pipeline and Metro Track Risk Estimation Using Smart Cities Development
  6. Forecasting of Lake Level by Soft Computing Approaches
  7. Artificial Intelligence-Driven Structural Health Monitoring: Challenges, Progress, and Applications
  8. Optimizing Tuned Mass Damper by Examining Displacement Ratios with and Without TMD System
  9. Evaluation of Predictive Models for Mechanical Properties of Earth-Based Composites for Sustainable Building Applications
  10. Shear Wall Cost Optimization by Employing Harmony Search
  11. Effect of CatBoost Parameters on Cost Minimization of Rectangular Section Reinforced Concrete Columns Under Uniaxial Bending Effect
  12. Machine Learning Approaches for Predicting Compressive and Shear Strength of EB FRP-Reinforced Concrete Elements: A Comprehensive Review
  13. A Modified Jaya Algorithm for Optimum Design of Carbon Fiber-Reinforced Polymers
  14. Prediction of Bi-Linear Strength Envelope of Brazilian Soils Using Machine Learning Techniques
  15. Assessment of Unconfined Compressive Strength of Stabilized Soil Using Artificial Intelligence Tools: A Scientometrics Review
  16. A Review of Deformations Prediction for Oil and Gas Pipelines Using Machine and Deep Learning
  17. Determination of the Effect of XGBoost’s Parameters on a Structural Problem
  18. Area Optimization of Bending Members with Different Shapes in Terms of Pure Bending
  19. A Simplified Flower Pollination Algorithm for Structural Optimization of Trusses
  • Hardcover ISBN: 978-3-031-65975-1
  • Softcover ISBN: 978-3-031-65978-2
  • eBook ISBN: 978-3-031-65976-8
  • DOI: https://doi.org/10.1007/978-3-031-65976-8
About the book
  • Highlights how artificial intelligence (AI) is transforming civil engineering through massive data streams and advanced computing techniques.
  • Provides a comprehensive review of key AI technologies, including machine learning, deep learning, natural language processing, computer vision, robotics, and generative AI.
  • Examines applications of AI across multiple civil engineering domains such as design, construction, monitoring, transportation, geotechnical, environmental, and asset management.
  • Explores how AI-driven models improve sustainability, safety, and operational efficiency.
  • Addresses critical challenges including data limitations, model interpretability, and ethical concerns.
  • Emphasizes the increasing demand for AI-related skills within the civil engineering workforce.
  • Discusses emerging trends such as digital twins, smart cities, and quantum computing.
  • Identifies research gaps and future opportunities by synthesizing recent advancements in the field.
  1. Ultra Smart Cyberspace Manufacturing Infrastructure Using Artificial Intelligence-Driven Automated Engineering
  2. Human-Centric AI Tools Into Agile Methodologies for Optimized Software Development
  3. AI-Driven Automation in Agile Software Development Teams Enhancing Efficiency and Collaboration
  4. Artificial Intelligence in Civil Engineering: Novel Prospects to Explore
  5. AI-Driven Optimization Techniques for Energy-Efficient Building Systems
  6. AI in Computer Vision
  7. Ultra Smart Engineering and Manufacturing Impact
  8. A Hand Gesture Recognition Model Using Deep Learning Algorithms
  9. Deepfake Face Detection System for Differentiating Real and Authentic Images
  10. Exhaustive Survey on Automated ASD Diagnosis Integrating Behavioural and Neuroimaging Data
  11. Reinforcement Learning for Autonomous Optimization in Intelligent Engineering
  12. Robotics in Financial Management: Pioneering Sustainability Through Intelligent Automation
  13. Technology-Driven Smart Engineering and Smart Manufacturing

 

  • DOI: https://doi.org/10.4018/979-8-3373-4839-1.ch004
About the book
  • Provides a comprehensive overview of integrating artificial intelligence (AI) techniques into wastewater treatment processes.
  • Covers applications such as AI-driven monitoring and control systems in treatment plants.
  • Explores predictive modeling approaches for improved pollutant removal.
  • Discusses the development of smart sensor networks for real-time data analysis.
  • Demonstrates how combining AI methodologies with wastewater treatment enhances operational efficiency.
  • Highlights the potential for cost reduction and minimization of environmental impacts.
  • Addresses the urgent need for sustainable and efficient wastewater management solutions.
  • Explains how machine learning and data analytics enable proactive decision-making and optimized resource allocation.
  • Emphasizes AI’s capability to predict system failures and improve reliability.
  • Contributes to the broader objective of environmentally sustainable and economically viable wastewater treatment practices.
  1. Introduction
  2. Overview of Applications of Artificial Intelligence (AI) in Diverse Fields
  3. Artificial Intelligence in Wastewater Treatment
  4. Potential of AI Optimization in Wastewater Treatment Processes
  5. Real-Time Data Analysis with Smart Sensors
  6. Automated Water Control System in Wastewater Treatment Plants
  7. Predictive Modeling for Pollutant Removal: Machine Learning Algorithms for Predictive Analysis
  8. Case Studies and Success Stories of AI Implementation in Wastewater Treatment
  9. Environmental Sustainability, Compliance and Life Cycle Assessment of AI-Integrated Systems
  10. Emerging AI Technologies in Wastewater Treatment
  11. Regulatory Framework and Standards for Adapting AI Systems: Interplay Between AI and Environmental Policies
  12. Recommendations for Implementation of AI: Steps Toward Industry-Wide Adoption
  13. Conclusions and Future Prospects of AI in Wastewater Treatment
  • Hardcover ISBN: 978-3-031-69432-5
  • Softcover ISBN: 978-3-031-69435-6
  • eBook ISBN: 978-3-031-69433-2
  • DOI:https://doi.org/10.1007/978-3-031-69433-2
About the book
  • Highlights the latest Artificial Intelligence (AI) technologies and applications in construction engineering and management.
  • Notes that the construction industry has been relatively slow in adopting digital technologies, traditionally relying on professional expertise and experience.
  • Explains how AI combines large datasets, rapid iterative processing, and intelligent algorithms such as neural networks, process mining, and deep learning.
  • Describes AI’s ability to learn automatically from patterns and features within data.
  • Provides solutions for key construction challenges, including knowledge discovery, risk estimation, root cause analysis, damage prediction, and defect detection.
  • Emphasizes the significant transformation driven by emerging AI applications in recent years.
  • Demonstrates how AI enhances project efficiency and safety.
  • Highlights improvements in automation, productivity, and global competitiveness within the construction sector.
  1. Introduction to Artificial Intelligence
  2. Knowledge Representation and Discovery
  3. Fuzzy Modeling and Reasoning
  4. Time Series Prediction
  5. Information Fusion
  6. Dynamic Bayesian Networks
  7. Process Mining
  8. Agent-Based Simulation
  9. Expert Systems
  10. Computer Vision
  11. Conclusions and Future Directions
  • Hardcover ISBN: 978-981-16-2841-2
  • Softcover ISBN: 978-981-16-2844-3
  • eBook ISBN:978-981-16-2842-9
  • DOI: https://doi.org/10.1007/978-981-16-2842-9