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AI and Civil Engineering: A Revolution in Design, Construction, and Management
Author: Dr. Anya Sharma, PhD, PE. Dr. Sharma is a Professor of Civil Engineering at the Massachusetts Institute of Technology (MIT) with over 15 years of experience in applying AI and machine learning techniques to civil infrastructure projects. Her research focuses on predictive modeling for structural health monitoring, optimized design using genetic algorithms, and the application of AI in sustainable infrastructure development.
Publisher: ASCE Press, the publishing arm of the American Society of Civil Engineers (ASCE). ASCE is a leading professional organization for civil engineers globally, renowned for its rigorous standards and expertise in all aspects of civil engineering, including emerging technologies like AI.
Editor: Dr. David Chen, PhD, PE, a seasoned editor with ASCE Press and a distinguished civil engineer specializing in structural engineering and computational methods. His extensive experience ensures the technical accuracy and clarity of the published works.
Keywords: AI and civil engineering, artificial intelligence, civil engineering, infrastructure, machine learning, deep learning, BIM, digital twin, predictive maintenance, structural health monitoring, construction management, sustainable infrastructure.
Abstract: This article explores the transformative impact of AI and civil engineering, tracing its historical evolution and analyzing its current and future implications. We examine how AI is revolutionizing various stages of the civil engineering lifecycle, from initial design and planning to construction management and long-term maintenance. The integration of AI presents significant opportunities for enhancing efficiency, safety, and sustainability in infrastructure development.
1. The Historical Context of AI and Civil Engineering
The intersection of AI and civil engineering is relatively recent but rapidly evolving. Early applications involved expert systems, rule-based programs designed to mimic the decision-making process of experienced engineers. These systems found limited application in areas like structural design and material selection. However, the advent of machine learning (ML) and deep learning (DL) algorithms in the late 2000s and early 2010s marked a significant turning point. These algorithms, capable of learning from vast datasets, opened up new possibilities for analyzing complex engineering problems and extracting valuable insights.
The increasing availability of computational power and large datasets, coupled with advancements in sensor technology and the Internet of Things (IoT), further fueled the integration of AI and civil engineering. The emergence of Building Information Modeling (BIM) and digital twins provided the necessary digital infrastructure to support AI-driven applications.
2. Current Applications of AI in Civil Engineering
AI and civil engineering are now intertwined across various stages of the project lifecycle:
2.1 Design and Planning:
Generative Design: AI algorithms can explore a vast design space to generate optimized designs based on specified constraints and objectives. This leads to more efficient, cost-effective, and sustainable designs.
Material Selection: AI can analyze material properties and predict their long-term performance, enabling the selection of optimal materials for specific applications.
Risk Assessment: AI models can assess risks associated with various design options and predict potential failures, contributing to safer and more reliable infrastructure.
Predictive Modeling for Traffic and Transportation: AI algorithms are extensively used for traffic flow prediction and optimization, leading to improved transportation planning and reduced congestion.
2.2 Construction Management:
Construction scheduling and resource allocation: AI-powered tools optimize construction schedules, allocate resources effectively, and predict potential delays, improving project efficiency and cost management.
Safety monitoring: AI-powered computer vision systems can monitor construction sites for safety hazards, providing real-time alerts and reducing accidents.
Quality control: AI can analyze images and sensor data to detect defects and ensure quality control throughout the construction process.
Equipment Maintenance: Predictive maintenance using AI reduces downtime and improves equipment lifespan by forecasting equipment failures.
2.3 Operation and Maintenance:
Structural Health Monitoring (SHM): AI-powered SHM systems use sensor data to monitor the health of structures, detecting potential damage and predicting future failures, enabling timely interventions and preventing catastrophic events.
Predictive Maintenance: AI can predict maintenance needs based on historical data and sensor readings, reducing maintenance costs and improving infrastructure reliability.
Asset Management: AI algorithms support asset management by optimizing maintenance schedules and predicting the remaining useful life of infrastructure assets.
3. Challenges and Future Trends in AI and Civil Engineering
Despite the immense potential, several challenges remain in the widespread adoption of AI in civil engineering:
Data Availability and Quality: AI algorithms require large, high-quality datasets for training. Acquiring and cleaning this data can be challenging and expensive.
Computational Resources: Training complex AI models requires significant computational power, which can be a barrier for smaller firms.
Explainability and Trust: The "black box" nature of some AI algorithms can make it difficult to understand their decision-making process, raising concerns about trust and accountability.
Integration with Existing Systems: Integrating AI-powered tools into existing workflows and systems can be complex and require significant effort.
Skills Gap: A shortage of engineers with expertise in AI and data science poses a significant challenge.
Future trends suggest an increasing focus on:
Hybrid AI models: Combining AI with human expertise to leverage the strengths of both.
Explainable AI (XAI): Developing AI algorithms that are more transparent and understandable.
Edge computing: Processing data closer to the source to reduce latency and bandwidth requirements.
Digital twins: Creating comprehensive digital representations of infrastructure assets for improved monitoring and management.
Increased collaboration: Fostering collaboration between AI researchers, civil engineers, and other stakeholders to accelerate the adoption of AI in civil engineering.
Conclusion
AI and civil engineering are poised for a period of unprecedented growth and transformation. The integration of AI offers significant opportunities for enhancing the efficiency, safety, and sustainability of infrastructure development. By addressing the challenges related to data availability, computational resources, and skills development, the civil engineering industry can fully leverage the potential of AI to build a more resilient and sustainable future.
FAQs
1. What is the difference between machine learning and deep learning in civil engineering? Machine learning uses algorithms to learn from data, while deep learning is a subset of machine learning using artificial neural networks with multiple layers to analyze complex data.
2. How can AI improve the safety of construction sites? AI-powered computer vision can monitor for safety hazards, providing real-time alerts and reducing accidents.
3. What are the benefits of using AI for predictive maintenance? AI predicts maintenance needs, reducing downtime, costs, and improving infrastructure reliability.
4. What are the challenges in implementing AI in civil engineering projects? Data availability, computational resources, explainability, integration with existing systems, and skills gaps are key challenges.
5. How can AI help in sustainable infrastructure development? AI optimizes designs for energy efficiency, material usage, and reduced environmental impact.
6. What is a digital twin in the context of civil engineering? A digital twin is a virtual representation of a physical infrastructure asset, used for monitoring, simulation, and predictive maintenance.
7. How can AI improve traffic management? AI predicts traffic flow, optimizes traffic signals, and improves transportation planning.
8. What role does BIM play in AI-driven civil engineering? BIM provides the digital infrastructure necessary for AI applications, offering data-rich models for analysis.
9. What are the ethical considerations of using AI in civil engineering? Ensuring fairness, transparency, accountability, and privacy in AI systems is crucial for responsible implementation.
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2. "Predictive Maintenance of Highway Infrastructure using Machine Learning": This study investigates the use of ML algorithms for predicting the maintenance needs of highways.
3. "Deep Learning for Structural Health Monitoring of High-Rise Buildings": This research focuses on the application of deep learning in detecting damage in high-rise structures.
4. "AI-driven Optimization of Construction Schedules": This article examines the use of AI for improving construction project scheduling and resource allocation.
5. "The Role of Digital Twins in Infrastructure Asset Management": This paper discusses the benefits of using digital twins for managing infrastructure assets.
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| ai and civil engineering: Artificial Intelligence and Machine Learning Techniques for Civil Engineering Plevris, Vagelis, Ahmad, Afaq, Lagaros, Nikos D., 2023-06-05 In recent years, artificial intelligence (AI) has drawn significant attention with respect to its applications in several scientific fields, varying from big data handling to medical diagnosis. A tremendous transformation has taken place with the emerging application of AI. AI can provide a wide range of solutions to address many challenges in civil engineering. Artificial Intelligence and Machine Learning Techniques for Civil Engineering highlights the latest technologies and applications of AI in structural engineering, transportation engineering, geotechnical engineering, and more. It features a collection of innovative research on the methods and implementation of AI and machine learning in multiple facets of civil engineering. Covering topics such as damage inspection, safety risk management, and information modeling, this premier reference source is an essential resource for engineers, government officials, business leaders and executives, construction managers, students and faculty of higher education, librarians, researchers, and academicians. |
| ai and civil engineering: Artificial Intelligence Applications for Sustainable Construction Moncef L. Nehdi, Harish Chandra Arora, Krishna Kumar, Robertas Damaševičius, Aman Kumar, 2024-02-13 Artificial Intelligence Applications for Sustainable Construction presents the latest developments in AI and ML technologies applied to real-world civil engineering concerns. With an increasing amount of attention on the environmental impact of every industry, more construction projects are going to require sustainable construction practices. This volume offers research evidence, simulation results, and case studies to support this change. Sustainable construction, in fact, not only uses renewable and recyclable materials when building new structures or repairing deteriorating ones, but also adopts all possible methods to reduce energy consumption and waste. The concisely written but comprehensive, practical knowledge put forward by this international group of highly specialized editors and contributors will prove to be beneficial to engineering students and professionals alike. - Presents convincing success stories that encourage application of AI-powered tools to civil engineering - Provides a wealth of valuable technical information to address and resolve many challenging construction problems - Illustrates the most recent shifts in thinking and practice for sustainable construction |
| ai and civil engineering: Artificial Intelligence and Expert Systems for Engineers C.S. Krishnamoorthy, S. Rajeev, 2018-04-24 This book provides a comprehensive presentation of artificial intelligence (AI) methodologies and tools valuable for solving a wide spectrum of engineering problems. What's more, it offers these AI tools on an accompanying disk with easy-to-use software. Artificial Intelligence and Expert Systems for Engineers details the AI-based methodologies known as: Knowledge-Based Expert Systems (KBES); Design Synthesis; Design Critiquing; and Case-Based Reasoning. KBES are the most popular AI-based tools and have been successfully applied to planning, diagnosis, classification, monitoring, and design problems. Case studies are provided with problems in engineering design for better understanding of the problem-solving models using the four methodologies in an integrated software environment. Throughout the book, examples are given so that students and engineers can acquire skills in the use of AI-based methodologies for application to practical problems ranging from diagnosis to planning, design, and construction and manufacturing in various disciplines of engineering. Artificial Intelligence and Expert Systems for Engineers is a must-have reference for students, teachers, research scholars, and professionals working in the area of civil engineering design in particular and engineering design in general. |
| ai and civil engineering: Artificial Intelligence in Construction Engineering and Management Limao Zhang, Yue Pan, Xianguo Wu, Mirosław J. Skibniewski, 2021-06-18 This book highlights the latest technologies and applications of Artificial Intelligence (AI) in the domain of construction engineering and management. The construction industry worldwide has been a late bloomer to adopting digital technology, where construction projects are predominantly managed with a heavy reliance on the knowledge and experience of construction professionals. AI works by combining large amounts of data with fast, iterative processing, and intelligent algorithms (e.g., neural networks, process mining, and deep learning), allowing the computer to learn automatically from patterns or features in the data. It provides a wide range of solutions to address many challenging construction problems, such as knowledge discovery, risk estimates, root cause analysis, damage assessment and prediction, and defect detection. A tremendous transformation has taken place in the past years with the emerging applications of AI. This enables industrial participants to operate projects more efficiently and safely, not only increasing the automation and productivity in construction but also enhancing the competitiveness globally. |
| ai and civil engineering: Optimization and Artificial Intelligence in Civil and Structural Engineering B.H. Topping, 2013-11-11 This volume and its companion volume includes the edited versions of the principal lectures and selected papers presented at the NATO Advanced Study Institute on Optimization and Decision Support Systems in Civil Engineering. The Institute was held in the Department of Civil Engineering at Heriot-Watt University, Edinburgh from June 25th to July 6th 1989 and was attended by eighty participants from Universities and Research Institutes around the world. A number of practising civil and structural engineers also attended. The lectures and papers have been divided into two volumes to reflect the dual themes of the Institute namely Optimization and Decision Support Systems in Civil Engineering. Planning for this ASI commenced in late 1986 when Andrew Templeman and I discussed developments in the use of the systems approach in civil engineering. A little later it became clear that much of this approach could be realised through the use of knowledge-based systems and artificial intelligence techniques. Both Don Grierson and John Gero indicated at an early stage how important it would be to include knowledge-based systems within the scope of the Institute. The title of the Institute could have been: 'Civil Engineering Systems' as this would have reflected the range of systems applications to civil engineering problems considered by the Institute. These volumes therefore reflect the full range of these problems including: structural analysis and design; water resources engineering; geotechnical engineering; transportation and environmental engineering. |
| ai and civil engineering: Leveraging Artificial Intelligence in Engineering, Management, and Safety of Infrastructure M.Z. Naser, 2022-11-17 The design, construction, and upkeep of infrastructure is comprised of a multitude of dimensions spanning a highly complex paradigm of interconnected opportunities and challenges. While traditional methods fall short of adequately accounting for such complexity, artificial intelligence (AI) presents novel and out-of-the-box solutions that effectively tackle the growing demands of our infrastructure. The convergence between AI and civil engineering is an emerging frontier with tremendous potential. The book is likely to provide a boost to the state of infrastructure engineering by fostering a new look at civil engineering that capitalizes on AI as its main driver. It highlights the ongoing push to adopt and leverage AI to realize contemporary, intelligent, safe, and resilient infrastructure. The book comprises interdisciplinary and novel works from across the globe. It presents findings from innovative efforts supplemented with physical tests, numerical simulations, and case studies – all of which can be used as benchmarks to carry out future experiments and/or facilitate the development of future AI models in structural engineering, traffic engineering, construction engineering, and construction materials. The book will serve as a guide for a wide range of audiences, including senior undergraduate and graduate students, professionals, and government officials of civil, traffic, and computer engineering backgrounds, as well as for those engaged in urban planning and human sciences. |
| ai and civil engineering: Optimization and Artificial Intelligence in Civil and Structural Engineering B.H. Topping, 2013-03-14 This volume and its companion volume includes the edited versions of the principal lectures and selected papers presented at the NATO Advanced Study Institute on Optimization and Decision Support Systems in Civil Engineering. The Institute was held in the Department of Civil Engineering at Heriot-Watt University, Edinburgh from June 25th to July 6th 1989 and was attended by eighty participants from Universities and Research Institutes around the world. A number of practising civil and structural engineers also attended. The lectures and papers have been divided into two volumes to reflect the dual themes of the Institute namely Optimization and Decision Support Systems in Civil Engineering. Planning for this ASI commenced in late 1986 when Andrew Templeman and I discussed developments in the use of the systems approach in civil engineering. A little later it became clear that much of this approach could be realised through the use of knowledge-based systems and artificial intelligence techniques. Both Don Grierson and John Gero indicated at an early stage how important it would be to include knowledge-based systems within the scope of the Institute. The title of the Institute could have been: 'Civil Engineering Systems' as this would have reflected the range of systems applications to civil engineering problems considered by the Institute. These volumes therefore reflect the full range of these problems including: structural analysis and design; water resources engineering; geotechnical engineering; transportation and environmental engineering. |
| ai and civil engineering: Artificial Intelligence in Structural Engineering Ian Smith, 1998-07-15 This book presents the state of the art of artificial intelligence techniques applied to structural engineering. The 28 revised full papers by leading scientists were solicited for presentation at a meeting held in Ascona, Switzerland, in July 1998. The recent advances in information technology, in particular decreasing hardware cost, Internet communication, faster computation, increased bandwidth, etc., allow for the application of new AI techniques to structural engineering. The papers presented deal with new aspects of information technology support for the design, analysis, monitoring, control and diagnosis of various structural engineering systems. |
| ai and civil engineering: Artificial Intelligence and Expert Systems for Engineers C.S. Krishnamoorthy, S. Rajeev, 2018-04-24 This book provides a comprehensive presentation of artificial intelligence (AI) methodologies and tools valuable for solving a wide spectrum of engineering problems. What's more, it offers these AI tools on an accompanying disk with easy-to-use software. Artificial Intelligence and Expert Systems for Engineers details the AI-based methodologies known as: Knowledge-Based Expert Systems (KBES); Design Synthesis; Design Critiquing; and Case-Based Reasoning. KBES are the most popular AI-based tools and have been successfully applied to planning, diagnosis, classification, monitoring, and design problems. Case studies are provided with problems in engineering design for better understanding of the problem-solving models using the four methodologies in an integrated software environment. Throughout the book, examples are given so that students and engineers can acquire skills in the use of AI-based methodologies for application to practical problems ranging from diagnosis to planning, design, and construction and manufacturing in various disciplines of engineering. Artificial Intelligence and Expert Systems for Engineers is a must-have reference for students, teachers, research scholars, and professionals working in the area of civil engineering design in particular and engineering design in general. |
| ai and civil engineering: Expert Systems for Civil Engineers Satish Mohan, Mary Lou Maher, 1989 |
| ai and civil engineering: Artificial Intelligence and Civil Engineering B. H. V. Topping, 1991 Included in this volume are papers presented at the Second International Conference on the Application of Artificial Intelligence to Civil & Structural Engineering, 3-5 September, 1991, Oxford. |
| ai and civil engineering: Harmony Search Algorithm Joong Hoon Kim, Zong Woo Geem, 2015-08-08 The Harmony Search Algorithm (HSA) is one of the most well-known techniques in the field of soft computing, an important paradigm in the science and engineering community. This volume, the proceedings of the 2nd International Conference on Harmony Search Algorithm 2015 (ICHSA 2015), brings together contributions describing the latest developments in the field of soft computing with a special focus on HSA techniques. It includes coverage of new methods that have potentially immense application in various fields. Contributed articles cover aspects of the following topics related to the Harmony Search Algorithm: analytical studies; improved, hybrid and multi-objective variants; parameter tuning; and large-scale applications. The book also contains papers discussing recent advances on the following topics: genetic algorithms; evolutionary strategies; the firefly algorithm and cuckoo search; particle swarm optimization and ant colony optimization; simulated annealing; and local search techniques. This book offers a valuable snapshot of the current status of the Harmony Search Algorithm and related techniques, and will be a useful reference for practising researchers and advanced students in computer science and engineering. |
| ai and civil engineering: Optimization and Artificial Intelligence in Civil and Structural Engineering B.H. Topping, 1992-09-30 These volumes comprise the edited versions of the principal lectures and selected papers presented at the NATO Advanced Study Institute on Optimization and Decision Support Systems in Civil Engineering. The Institute was held in the Department of Civil Engineering at Heriot-Watt University, Edinburgh, United Kingdom, from June 25th to July 6th 1989. Both volumes reflect the full range of the systems approach to civil and structural engineering problems including: structural analysis and design; water resources engineering; geotechnical engineering; transportation and environmental engineering. This system approach, discussed in the first volume, includes a number of common threads: mathematical programming, game theory, utility theory, statistical decision theory, networks, and fuzzy logic. A most important feature of this volume is the examination of similar representations of different civil engineering problems and their solutions using general systems approaches. The decision support aspect of the institute is reflected in the second volume by the knowledge-based systems and their artificial intelligence approach. Papers discussing many aspects of knowledge-based systems in civil and structural engineering are included in the second volume. |
| ai and civil engineering: ARTIFICIAL INTELLIGENCE AND MACHINE LEARNING IN CIVIL ENGINEERING DR M.S.V.K.V.PRASAD, AZHARUDDIN AHMED, DR.M.VADIVEL, DR. NITYANAND S. KUDACHIMATH, MR.P.JAYARAJ, .. |
| ai and civil engineering: A Primer on Machine Learning Applications in Civil Engineering Paresh Chandra Deka, 2019-10-28 Machine learning has undergone rapid growth in diversification and practicality, and the repertoire of techniques has evolved and expanded. The aim of this book is to provide a broad overview of the available machine-learning techniques that can be utilized for solving civil engineering problems. The fundamentals of both theoretical and practical aspects are discussed in the domains of water resources/hydrological modeling, geotechnical engineering, construction engineering and management, and coastal/marine engineering. Complex civil engineering problems such as drought forecasting, river flow forecasting, modeling evaporation, estimation of dew point temperature, modeling compressive strength of concrete, ground water level forecasting, and significant wave height forecasting are also included. Features Exclusive information on machine learning and data analytics applications with respect to civil engineering Includes many machine learning techniques in numerous civil engineering disciplines Provides ideas on how and where to apply machine learning techniques for problem solving Covers water resources and hydrological modeling, geotechnical engineering, construction engineering and management, coastal and marine engineering, and geographical information systems Includes MATLAB® exercises |
| ai and civil engineering: Watershed Management and Applications of AI Sandeep Samantaray, Abinash Sahoo, Dillip K. Ghose, 2021-05-16 Land use and water resources are two major environmental issues which necessitate conservation, management, and maintenance practices through the use of various engineering techniques. Water scientists and environmental engineers must address the various aspects of flood control, soil conservation, rainfall-runoff processes, and groundwater hydrology. Watershed Management and Applications of AI provides the necessary principles of hydrology to provide practical strategies useful for the planning, design, and management of watersheds. The book also synthesizes novel new approaches, such as hydrological applications of machine learning using neural networks to predict runoff and using artificial intelligence for the prediction of groundwater fluctuations. Features: Presents hydrologic analysis and design along with soil conservation practices through proper watershed management techniques Provides analysis of land erosion and sediment transport in watersheds from small to large scale Includes estimations for runoff using different methodologies with systematic approaches for each Discusses water harvesting and development of water yield catchments This book will be a valuable resource for students in hydrology courses, environmental consultants, water resource engineers, and researchers in related water science and engineering fields. |
| ai and civil engineering: Handbook of Research on Digital Transformation, Industry Use Cases, and the Impact of Disruptive Technologies Wynn, Martin George, 2021-10-15 Companies from various sectors of the economy are confronted with the new phenomenon of digital transformation and are faced with the challenge of formulating and implementing a company-wide strategy to incorporate what are often viewed as “disruptive” technologies. These technologies are sometimes associated with significant and extremely rapid change, in some cases with even the replacement of established business models. Many of these technologies have been deployed in unison by leading-edge companies acting as the catalyst for significant process change and people skills enhancement. The Handbook of Research on Digital Transformation, Industry Use Cases, and the Impact of Disruptive Technologies examines the phenomenon of digital transformation and the impact of disruptive technologies through the lens of industry case studies where different combinations of these new technologies have been deployed and incorporated into enterprise IT and business strategies. Covering topics including chatbot implementation, multinational companies, cloud computing, internet of things, artificial intelligence, big data and analytics, immersive technologies, and social media, this book is essential for senior management, IT managers, technologists, computer scientists, cybersecurity analysts, academicians, researchers, IT consultancies, professors, and students. |
| ai and civil engineering: Probabilistic Machine Learning for Civil Engineers James-A. Goulet, 2020-04-14 An introduction to key concepts and techniques in probabilistic machine learning for civil engineering students and professionals; with many step-by-step examples, illustrations, and exercises. This book introduces probabilistic machine learning concepts to civil engineering students and professionals, presenting key approaches and techniques in a way that is accessible to readers without a specialized background in statistics or computer science. It presents different methods clearly and directly, through step-by-step examples, illustrations, and exercises. Having mastered the material, readers will be able to understand the more advanced machine learning literature from which this book draws. The book presents key approaches in the three subfields of probabilistic machine learning: supervised learning, unsupervised learning, and reinforcement learning. It first covers the background knowledge required to understand machine learning, including linear algebra and probability theory. It goes on to present Bayesian estimation, which is behind the formulation of both supervised and unsupervised learning methods, and Markov chain Monte Carlo methods, which enable Bayesian estimation in certain complex cases. The book then covers approaches associated with supervised learning, including regression methods and classification methods, and notions associated with unsupervised learning, including clustering, dimensionality reduction, Bayesian networks, state-space models, and model calibration. Finally, the book introduces fundamental concepts of rational decisions in uncertain contexts and rational decision-making in uncertain and sequential contexts. Building on this, the book describes the basics of reinforcement learning, whereby a virtual agent learns how to make optimal decisions through trial and error while interacting with its environment. |
| ai and civil engineering: Artificial Intelligence Techniques and Applications for Civil and Structural Engineers B. H. V. Topping, 1989 Included in this volume are papers presented at the First International Conference on the Application of Artificial Intelligence to Civil & Structural Engineering, 19-21 September, 1989, London. |
| ai and civil engineering: New Materials in Civil Engineering Pijush Samui, Dookie Kim, Nagesh R. Iyer, Sandeep Chaudhary, 2020-07-07 New Materials in Civil Engineering provides engineers and scientists with the tools and methods needed to meet the challenge of designing and constructing more resilient and sustainable infrastructures. This book is a valuable guide to the properties, selection criteria, products, applications, lifecycle and recyclability of advanced materials. It presents an A-to-Z approach to all types of materials, highlighting their key performance properties, principal characteristics and applications. Traditional materials covered include concrete, soil, steel, timber, fly ash, geosynthetic, fiber-reinforced concrete, smart materials, carbon fiber and reinforced polymers. In addition, the book covers nanotechnology and biotechnology in the development of new materials. - Covers a variety of materials, including fly ash, geosynthetic, fiber-reinforced concrete, smart materials, carbon fiber reinforced polymer and waste materials - Provides a one-stop resource of information for the latest materials and practical applications - Includes a variety of different use case studies |
| ai and civil engineering: Artificial Intelligence and Machine Learning Applications in Civil, Mechanical, and Industrial Engineering Gebrail Bekdas, Sinan Melih Nigdeli, Melda Yucel, 2019 This book examines the application of artificial intelligence and machine learning civil, mechanical, and industrial engineering-- |
| ai and civil engineering: Information Technology for Civil and Structural Engineers B. H. V. Topping, A. I. Khan, 1993 Included in this volume are a selection of papers presented at the Fifth International Conference on Civil and Structural Engineering Computing and the Third International Conference on the Application of Artificial Intelligence to Civil and Structural Engineering held concurrently 17-19 August 1993, Edinburgh. |
| ai and civil engineering: Applications of Artificial Intelligence in Engineering Xiao-Zhi Gao, Rajesh Kumar, Sumit Srivastava, Bhanu Pratap Soni, 2021-05-10 This book presents best selected papers presented at the First Global Conference on Artificial Intelligence and Applications (GCAIA 2020), organized by the University of Engineering & Management, Jaipur, India, during 8–10 September 2020. The proceeding will be targeting the current research works in the domain of intelligent systems and artificial intelligence. |
| ai and civil engineering: Civil Engineering Body of Knowledge Civil Engineering Body of Knowledge 3 Task Committee, 2019 This report outlines 21 foundational, technical, and professional practice learning outcomes for individuals entering the professional practice of civil engineering. |
| ai and civil engineering: Infrastructure Computer Vision Ioannis Brilakis, Carl Thomas Michael Haas, 2019-11-28 Infrastructure Computer Vision delves into this field of computer science that works on enabling computers to see, identify, process images and provide appropriate output in the same way that human vision does. However, implementing these advanced information and sensing technologies is difficult for many engineers. This book provides civil engineers with the technical detail of this advanced technology and how to apply it to their individual projects. - Explains how to best capture raw geometrical and visual data from infrastructure scenes and assess their quality - Offers valuable insights on how to convert the raw data into actionable information and knowledge stored in Digital Twins - Bridges the gap between the theoretical aspects and real-life applications of computer vision |
| ai and civil engineering: Structural Fire Engineering Venkatesh Kodur, Mohannad Naser, 2020-02-28 Actionable strategies for the design and construction of fire-resistant structures This hands-on guide clearly explains the complex building codes and standards that relate to fire design and presents hands-on techniques engineers can apply to prevent or mitigate the effects of fire in structures. Dedicated chapters discuss specific procedures for steel, concrete, and timber buildings. You will get step-by-step guidance on how to evaluate fire resistance using both testing and calculation methods. Structural Fire Engineering begins with an introduction to the behavioral aspects of fire and explains how structural materials react when exposed to elevated temperatures. From there, the book discusses the fire design aspects of key codes and standards, such as the International Building Code, the International Fire Code, and the NFPA Fire Code. Advanced topics are covered in complete detail, including residual capacity evaluation of fire damaged structures and fire design for bridges and tunnels. Explains the fire design requirements of the IBC, IFC, the NFPA Fire Code, and National Building Code of Canada Presents design strategies for steel, concrete, and timber structures as well as for bridges and tunnels Contains downloadable spreadsheets and problems along with solutions for instructors |
| ai and civil engineering: Risk, Reliability and Sustainable Remediation in the Field of Civil and Environmental Engineering Thendiyath Roshni, Pijush Samui, Dieu Tien Bui, Dookie Kim, Rahman Khatibi, 2022-03-22 Risk, Reliability and Sustainable Remediation in the Field of Civil and Environmental Engineering illustrates the concepts of risk, reliability analysis, its estimation, and the decisions leading to sustainable development in the field of civil and environmental engineering. The book provides key ideas on risks in performance failure and structural failures of all processes involved in civil and environmental systems, evaluates reliability, and discusses the implications of measurable indicators of sustainability in important aspects of multitude of civil engineering projects. It will help practitioners become familiar with tolerances in design parameters, uncertainties in the environment, and applications in civil and environmental systems. Furthermore, the book emphasizes the importance of risks involved in design and planning stages and covers reliability techniques to discover and remove the potential failures to achieve a sustainable development. - Contains relevant theory and practice related to risk, reliability and sustainability in the field of civil and environment engineering - Gives firsthand experience of new tools to integrate existing artificial intelligence models with large information obtained from different sources - Provides engineering solutions that have a positive impact on sustainability |
| ai and civil engineering: Artificial Intelligence and IoT Kalaiselvi Geetha Manoharan, Jawaharlal Arun Nehru, Sivaraman Balasubramanian, 2021-02-12 This book projects a futuristic scenario that is more existent than they have been at any time earlier. To be conscious of the bursting prospective of IoT, it has to be amalgamated with AI technologies. Predictive and advanced analysis can be made based on the data collected, discovered and analyzed. To achieve all these compatibility, complexity, legal and ethical issues arise due to automation of connected components and gadgets of widespread companies across the globe. While these are a few examples of issues, the authors’ intention in editing this book is to offer concepts of integrating AI with IoT in a precise and clear manner to the research community. In editing this book, the authors’ attempt is to provide novel advances and applications to address the challenge of continually discovering patterns for IoT by covering various aspects of implementing AI techniques to make IoT solutions smarter. The only way to remain pace with this data generated by the IoT and acquire the concealed acquaintance it encloses is to employ AI as the eventual catalyst for IoT. IoT together with AI is more than an inclination or existence; it will develop into a paradigm. It helps those researchers who have an interest in this field to keep insight into different concepts and their importance for applications in real life. This has been done to make the edited book more flexible and to stimulate further interest in topics. All these motivated the authors toward integrating AI in achieving smarter IoT. The authors believe that their effort can make this collection interesting and highly attract the student pursuing pre-research, research and even master in multidisciplinary domain. |
| ai and civil engineering: AI-Based Services for Smart Cities and Urban Infrastructure Lyu, Kangjuan, Hu, Min, Du, Juan, Sugumaran, Vijayan, 2020-09-04 Cities are the next frontier for artificial intelligence to permeate. As smart urban environments become possible, probable, and even preferred, artificial intelligence offers the chance for even further advancement through infrastructure and industry boosting. Opportunity overflows, but without thorough research to guide a complicated development and implementation process, urban environments can become disorganized and outright dangerous for citizens. AI-Based Services for Smart Cities and Urban Infrastructure is a collection of innovative research that explores artificial intelligence (AI) applications in urban planning. In addition, the book looks at how the internet of things and AI can work together to enable a real smart city and discusses state-of-the-art techniques in urban infrastructure design, construction, operation, maintenance, and management. While highlighting a broad range of topics including construction management, public transportation, and smart agriculture, this book is ideally designed for engineers, entrepreneurs, urban planners, architects, policymakers, researchers, academicians, and students. |
| ai and civil engineering: Civil Engineering Materials Peter A. Claisse, 2015-09-03 Civil Engineering Materials explains why construction materials behave the way they do. It covers the construction materials content for undergraduate courses in civil engineering and related subjects and serves as a valuable reference for professionals working in the construction industry. The book concentrates on demonstrating methods to obtain, analyse and use information rather than focusing on presenting large amounts of data. Beginning with basic properties of materials, it moves on to more complex areas such as the theory of concrete durability and corrosion of steel. - Discusses the broad scope of traditional, emerging, and non-structural materials - Explains what material properties such as specific heat, thermal conductivity and electrical resistivity are and how they can be used to calculate the performance of construction materials. - Contains numerous worked examples with detailed solutions that provide precise references to the relevant equations in the text. - Includes a detailed section on how to write reports as well as a full section on how to use and interpret publications, giving students and early career professionals valuable practical guidance. |
| ai and civil engineering: Fluid Mechanics for Civil and Environmental Engineers Ahlam I. Shalaby, 2018-02-21 An ideal textbook for civil and environmental, mechanical, and chemical engineers taking the required Introduction to Fluid Mechanics course, Fluid Mechanics for Civil and Environmental Engineers offers clear guidance and builds a firm real-world foundation using practical examples and problem sets. Each chapter begins with a statement of objectives, and includes practical examples to relate the theory to real-world engineering design challenges. The author places special emphasis on topics that are included in the Fundamentals of Engineering exam, and make the book more accessible by highlighting keywords and important concepts, including Mathcad algorithms, and providing chapter summaries of important concepts and equations. |
| ai and civil engineering: Earth Science for Civil and Environmental Engineers Richard E. Jackson, 2019-01-24 Introduces the fundamental principles of applied Earth science needed for engineering practice, with case studies, exercises, and online solutions. |
| ai and civil engineering: Mechanics of Civil Engineering Structures Laszlo P. Kollar, Gabriella Tarjan, 2020-10-20 Practicing engineers designing civil engineering structures, and advanced students of civil engineering, require foundational knowledge and advanced analytical and empirical tools. Mechanics in Civil Engineering Structures presents the material needed by practicing engineers engaged in the design of civil engineering structures, and students of civil engineering. The book covers the fundamental principles of mechanics needed to understand the responses of structures to different types of load and provides the analytical and empirical tools for design. The title presents the mechanics of relevant structural elements—including columns, beams, frames, plates and shells—and the use of mechanical models for assessing design code application. Eleven chapters cover topics including stresses and strains; elastic beams and columns; inelastic and composite beams and columns; temperature and other kinematic loads; energy principles; stability and second-order effects for beams and columns; basics of vibration; indeterminate elastic-plastic structures; plates and shells. This book is an invaluable guide for civil engineers needing foundational background and advanced analytical and empirical tools for structural design. - Includes 110 fully worked-out examples of important problems and 130 practice problems with an interaction solution manual (http://hsz121.hsz.bme.hu/solutionmanual) - Presents the foundational material and advanced theory and method needed by civil engineers for structural design - Provides the methodological and analytical tools needed to design civil engineering structures - Details the mechanics of salient structural elements including columns, beams, frames, plates and shells - Details mechanical models for assessing the applicability of design codes |
| ai and civil engineering: Engineer Your Own Success Anthony Fasano, 2015-01-07 Focusing on basic skills and tips for career enhancement, Engineer Your Own Success is a guide to improving efficiency and performance in any engineering field. It imparts valuable organization tips, communication advice, networking tactics, and practical assistance for preparing for the PE exam—every necessary skill for success. Authored by a highly renowned career coach, this book is a battle plan for climbing the rungs of any engineering ladder. |
| ai and civil engineering: Artificial Intelligence in Mechatronics and Civil Engineering Ehsan Momeni, Danial Jahed Armaghani, Aydin Azizi, 2023-02-15 Recent studies highlight the application of artificial intelligence, machine learning, and simulation techniques in engineering. This book covers the successful implementation of different intelligent techniques in various areas of engineering focusing on common areas between mechatronics and civil engineering. The power of artificial intelligence and machine learning techniques in solving some examples of real-life problems in engineering is highlighted in this book. The implementation process to design the optimum intelligent models is discussed in this book. |
| ai and civil engineering: The Threats of Algorithms and AI to Civil Rights, Legal Remedies, and American Jurisprudence Alfred R. Cowger, 2020-10-06 The Threats of Algorithms and A.I. to Civil Rights, Legal Remedies, and American Jurisprudence addresses the many threats to American jurisprudence caused by the growing use of algorithms and artificial intelligence (A.I.). Although algorithms prove valuable to society, that value may also lead to the destruction of the foundations of American jurisprudence by threatening constitutional rights of individuals, creating new liabilities for business managers and board members, disrupting commerce, interfering with long-standing legal remedies, and causing chaos in courtrooms trying to adjudge lawsuits. Alfred R. Cowger, Jr. explains these threats and provides potential solutions for both the general public and legal practitioners. Scholars of legal studies, media studies, and political science will find this book particularly useful. |
| ai and civil engineering: Optimization and Artificial Intelligence in Civil and Structural Engineering Barry H. V. Topping, 1992 |
| ai and civil engineering: Human-Centered AI Ben Shneiderman, 2022 The remarkable progress in algorithms for machine and deep learning have opened the doors to new opportunities, and some dark possibilities. However, a bright future awaits those who build on their working methods by including HCAI strategies of design and testing. As many technology companies and thought leaders have argued, the goal is not to replace people, but to empower them by making design choices that give humans control over technology. In Human-Centered AI, Professor Ben Shneiderman offers an optimistic realist's guide to how artificial intelligence can be used to augment and enhance humans' lives. This project bridges the gap between ethical considerations and practical realities to offer a road map for successful, reliable systems. Digital cameras, communications services, and navigation apps are just the beginning. Shneiderman shows how future applications will support health and wellness, improve education, accelerate business, and connect people in reliable, safe, and trustworthy ways that respect human values, rights, justice, and dignity. |
| ai and civil engineering: Proceedings of the Eighth International Conference on the Application of Artificial Intelligence to Civil, Structural and Environmental Engineering B. H. V. Topping, 2005 Contains the abstracts of the contributed papers presented at the Eighth International Conference on the Application of Artificial Intelligence to Civil, Structural and Environmental Engineering, held in Rome, Italy, 30 August - 2 September 2005. The papers are available in electronic format on the accompanying CD-ROM. |
| ai and civil engineering: Life Cycle Analysis and Assessment in Civil Engineering: Towards an Integrated Vision Robby Caspeele, Luc Taerwe, Dan M. Frangopol, 2018-10-15 This volume contains the papers presented at IALCCE2018, the Sixth International Symposium on Life-Cycle Civil Engineering (IALCCE2018), held in Ghent, Belgium, October 28-31, 2018. It consists of a book of extended abstracts and a USB device with full papers including the Fazlur R. Khan lecture, 8 keynote lectures, and 390 technical papers from all over the world. Contributions relate to design, inspection, assessment, maintenance or optimization in the framework of life-cycle analysis of civil engineering structures and infrastructure systems. Life-cycle aspects that are developed and discussed range from structural safety and durability to sustainability, serviceability, robustness and resilience. Applications relate to buildings, bridges and viaducts, highways and runways, tunnels and underground structures, off-shore and marine structures, dams and hydraulic structures, prefabricated design, infrastructure systems, etc. During the IALCCE2018 conference a particular focus is put on the cross-fertilization between different sub-areas of expertise and the development of an overall vision for life-cycle analysis in civil engineering. The aim of the editors is to provide a valuable source of cutting edge information for anyone interested in life-cycle analysis and assessment in civil engineering, including researchers, practising engineers, consultants, contractors, decision makers and representatives from local authorities. |
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