FIDIC Future Leaders –Technology and AI 2024
A FIDIC Conference Booklet prepared by the FIDIC
Future Leaders Advisory Council
September 2024
Introduction
The FIDIC Future Leaders Advisory Council (FLAC) was established to bring together a group of professionals under the age of 40 and is appointed by the FIDIC board to advise FIDIC on a number of activities and operations and provide opportunities for future leaders to participate actively in FIDIC with their peers and to develop the next generation of leaders in the consulting engineering and wider infrastructure sector.
The primary functions of the council are to:
• Engage with future leaders in the consultancy and engineering sector to promote FIDICs activities.
• Work with FIDIC to create targeted activities for its Future Leaders programme.
The FLAC provides opportunities for future leaders to participate actively in FIDIC with their peers and to develop as the next generation of leaders in the consulting engineering and wider infrastructure sector.
This publication forms part of this remit. It is important that future leaders’ voices are heard if the industry is to move towards the UN sustainable development goals (SDGs), net zero and beyond.
The contributions in this report explore the issues currently faced by future leaders but also considers the issues that the next generation of future leaders may face.
Introduction
Foreword from FIDIC
Future Leaders Advisory Council
Chair
I am pleased that the FIDIC Future Leaders Advisory Council (FLAC) is represented strongly at the conference through the Future Leaders Symposium and the publication of the Future Leaders’ Booklet.
This year marks an important milestone and a first for us, as in this eighth year of publishing our booklet we have had such a great number of good quality applications that we will be publishing not one but three Future Leaders’ Booklets around different themes.
What an achievement! I would like to take this opportunity to thank those that led the Future Leaders before me, the team within the Future Leaders’ Advisory Council and the secretariat at FIDIC who have all supported the bold aim for this programme to go from an ambitious idea to the achievement it is today.
The three booklet themes this year, I believe, represent the breadth and importance of the challenges we are facing. They are:
• Technology and AI
• Sustainability
• Challenges and opportunities for infrastructure delivery
As we fast approach 2030 and the need to hit the SDGs, net zero is also increasingly just over the horizon. The work we are doing today will form part of our net zero future and so it is important we are proactive in everything we design to meet such a goal.
Technology also continues to evolve it was not long ago that remote working was thrown into the future and artificial intelligence seems to be another significant shift that we refuse to ignore at our own peril.
There are several ways in which the world could potentially change as technology and AI continues to evolve:
Enhanced decision-making: AI can provide valuable insights and recommendations, helping you make informed choices in various areas of life.
Improved efficiency: Technology and AI-powered tools can automate routine tasks, saving you time and effort.
Access to information: technology and AI can help you find relevant information quickly and efficiently.
New opportunities: AI like the technology that came before it will open up new career paths and entrepreneurial ventures.
It is therefore fantastic to see the sector, be it the leaders at the top or the young engineers entering the industry, aligned to an extent which has never happened before. Tomorrow’s challenges are now today’s challenges - and we must deliver.
The conference theme, Transforming lives with infrastructure: Investing in and building a better world for all, could not be more appropriate given that the challenges above will have significant effects on how we, live, work and interact and we must not leave anyone behind.
Having the voices of upcoming and young engineers will be important as they continue to bring new and innovative points of view towards project delivery and the development of wider infrastructure.
We hope that you enjoy reading the articles that future leaders have prepared and find the content and context both interesting and valuable as we move towards a more sustainable, equitable and possibly technology-driven future.
Dr Rodrigo Juarez Chair - FIDIC Future Leaders Council
Presenting Authors
Acknowledging the seriousness of the sector to address the challenges we face in implementing, understanding, utilising and evolving the next generation of technology and AI to aid in the delivery of infrastructure will be important.
The FIDIC Future Leaders Advisory Council provides a platform for future leaders in the consulting engineering industry to share, reflect and come forward with new ideas or challenges.
We invited Future Leaders to reflect on the challenges and how we can not only approach the future but also consider that a different approach will also have additional or new benefits to economies, societies, and nature as a whole.
It is important that as a sector and as a society, individuals look forward to the opportunities in the V-U-C-A (volatility, uncertainty, complexity, and ambiguity) world despite how it impacts consulting engineering, infrastructure development, attraction, retention, and development of Future Leaders.
For this, the FLC selected, as presenters, the authors whose articles better reflect the above principles.
Authors:
• Mariana Prysthon Moraes, Brazil
• Carlos Amaral, Brazil
• Thiago Dantas, Brazil
Presenting Authors
Assets AI: Use of AI in Road Asset Management
Mariana Prysthon Moraes is a project manager at TPF Engenharia in Brazil. A civil engineer from the UFPE - Federal University of Pernambuco, exchange student at Ecole des Ingénieurs de la Ville de Paris (EIVP), Postgraduated in IT management at Faculdade de Informática e Administração Paulista (FIAP) and certified as a Project Management Professional (PMP®) by PMI.
Mariana is involved in the consulting area, integrating civil engineering with IT solutions. In her current post, she is actively involved in the development of many systems to support TPF’s contracts. Her work in this area has sparked her curiosity and driven her to become actively involved in innovation within the company.
Carlos Amaral is a systems analyst and works as a coordinator in information technology projects at TPF Engenharia in Brazil. He holds a Bachelor's degree in information systems from the Universidade Federal Rural de Pernambuco. Carlos develops different technology solutions, such as Robotic Process Automation (RPA) and Artificial Intelligence (AI), to support TPF's contracts.
His work in this field has helped to enhance internal processes and the operational efficiency of TPF's contracts.
Thiago Dantas is a project manager at TPF Engenharia, the Brazilian subsidiary of the multinational Belgian group, TPF S.A. He graduated in civil engineering and post-graduated in transport infrastructure. He is also certified as a Project Management Professional (PMP®) by PMI.
At TPF, Thiago started his career 14 years ago as an inspector and has worked on roadways and railways projects over the years. Today, he is a reference in the company as a manager and as a Building Information Modelling (BIM) and infrastructure expert.
Mariana Prysthon Moraes, Brazil
Carlos Amaral, Brazil
Thiago Dantas, Brazil
Presenting Authors
Assets AI: Use of AI in Road Asset Management
The network of roads that cross our cities are critical infrastructure and they are directly responsible for various engineering, economic activities and improving the movement and wellbeing of the population. Within this network, thousands of assets, ranging from signs, lights to road defences, work tirelessly to ensure safe and efficient transportation.
The efficacy and longevity of these assets, however, are not self-sustaining. Proper maintenance and responsible disposal of road assets are essential practices that directly impact road safety, sustainability, economics and transportation systems.
Brazil has a huge network of roads, one of the biggest in the world and to manage them well and keep them in good condition a Federal Highway Concessions Programme was initiated in 1993. The programme was established in a Brazilian standard by Ministerial Order No. 10/93, which created a working group aimed at studying the possibility of granting the private sector access to part of the federal highways. As a result of this , over the past few years, a significant portion of the network has been managed through road concessions, where private entities are granted the right to operate and maintain roadways in exchange for toll revenues.
Regular maintenance is essential not only to meet safety standards but also to uphold the quality and reliability of the infrastructure and regular and reliable income streams adding to the benefit to these private concession entities.
The biggest challenge for private companies taking on these concessions is right at the beginning, where they must survey existing assets, assess their status and then create a well-controlled maintenance process going forward. Therefore, finding a way to facilitate and speed up the survey process is fundamental.
The traditional surveys in Brazil are conducted manually for existing traffic sign registries, which are time-consuming and not as accurate as most would like. To achieve fast and precise detection, TPF Engenharia developed an artificial intelligence (AI) by using computer vision technologies concepts to speed up and bring more quality to the survey.
Computer vision is a field of AI that enables machines to interpret and understand visual information from their surroundings. By utilising machine learning algorithms and neural networks, computer vision systems can analyse images and videos to identify objects and classify them, in this case, identify the existing traffic signs. Integrating AI enhances these capabilities and the AI model is trained by a technical team which traditionally undertake manual analysis.
The solution construction occurs in three stages, which are continuously and repetitively improved:
1. Planning.
2. Artificial Intelligence Training.
3. Model Application.
In the planning stage, the need for objects to be identified and classified by AI was recognised, such as the vertical signage of highways, including signs and lights in this case. Also, relevant parameters were defined, such as the field team, equipment, labelling team, methodology for capturing the location of assets, deliverables and work development methodology. Given the project's uncertainty, an agile methodology was chosen for managing the work. The field activity involves using a camera attached to the car and GPS, requiring standard procedures such as maintaining a constant vehicle speed, image quality, good weather conditions and a defined time for clarity, among other variables.
For the second stage, training, a custom computer vision model was developed using the architecture of You Only Look Once (YOLO) v8, which is a Convolutional Neural Network (CNN), for the identification of signs and lights. This involves several phases such as data collection, preparation and labelling, model training, and finally, validation. The first phase is data collection, which consists of obtaining highway data previously collected without the use of AI. With the acquired data, in the preparation and labelling phase, the team manually identifies the signs and lights in the data collected in the previous phase. This information is the input into the model for training, and then the model self-analyses its robustness in the validation phase.
Presenting Authors
Assets AI: Use of AI in Road Asset Management
The data collected in the first phase is divided between training, validation and testing, with the specific proportions of 70%, 20%, and 10%, which is fundamental for measuring the model's robustness, providing a solid framework for application.
The final stage, application of the model, is the phase of using the trained AI, that is, after the field survey carried out through the filming of highways, the model is executed. Thus, this phase is divided as follows: field data collection, model application and result generation.
To ensure a good result, the data collection hardware must have high-resolution cameras mounted on vehicles, and the processing units need to be of a high standard containing powerful processors and GPUs. The software platform integrates AI algorithms and analyses all information compiling the results in two formats - a map and a report with information on objects identified, each classification and location.
Accurate geolocation is a crucial aspect of this solution. By integrating GPS data, the system can point to the location of each identified asset. The deliverable in map is geolocation data mapped onto geographic information systems (GIS), providing a visual representation of the position of the asset distribution along the roads.
In the first application, the results showed a satisfactory accuracy of 91% in detecting traffic signs. There are, however, challenges to be overcome, such as improving performance in adverse conditions, especially in urban areas, that have significantly greater visual information to analyse and to improve the training for lights. The use of AI provided a more efficient and automated data collection process, with the potential to assist in the maintenance and continuous monitoring of road traffic signs and lights, contributing to road safety and accident reduction. This study highlights the potential of AI as a powerful tool to improve road infrastructure management and ensure the safety of road users.
FIGURE 1: FOLLOWING THE SEQUENCE OF IMAGES: PHOTO OF THE CAMERA IN THE VEHICLE, AI MODEL, AND DELIVERABLE MAP.
Recognised Authors
In this section, we would like to highlight the contribution of notable authors with exciting articles.
They have provided us with opinions, experiences, and innovative ideas on how to evolve and adapt to changing technologies, the challenge of net zero and develop the skills and talent the industry needs to lead such transformative infrastructure development.
Authors:
• Lorena Oliveira, Brazil
• Sanderllan Costa, Brazil
• Bárbara Vilar, Brazil
• Cynthia Lewis Torres, Colombia
• Theerapon Jiratammakun, Thailand
Recognised Authors
Artificial intelligence in engineering: Transforming processes and overcoming challenges
Lorena Oliveira is director of quality and innovation at TPF Engineering, with almost ten years' experience in the company. She has a PhD in production engineering and a master’s degree in civil engineering from the Federal University of Pernambuco. She also has a postgraduate degree in project management from the Getúlio Vargas Foundation and in advanced topics in business management from Pearson College London.
Lorena is PMP, ACP and Scrum Fundamentals certified, with expertise in project management, quality and BIM. In addition to her work at TPF Engineering, she is an educator at the Project Management Institute, coordinates ABCE's Innovation Committee and is a mentor at Porto Social.
Cartographer and surveyor engineer, specialist in geoprocessing and data analysis, and master's student in geodetic sciences and geoinformation technologies. He has expertise in BIM with GIS, GIS in a web environment and relational databases.
He took part in the development of the SUAPE Master Plan, applying machine learning and artificial intelligence techniques. Responsible for the study of Vale's suitability areas and the regularisation of SUAPE's land ownership. Coordinated the spatialisation of Pernambuco's road network and its representation in a web environment.
The University of Brighton (England) works as innovation coordinator at TPF Engineering, managing multidisciplinary teams to develop and disseminate innovation initiatives applying artificial intelligence, drones, virtual reality, augmented reality and digital twins.
Leads the Innovation Quality Circle and the company's Innovation Acceleration Programme, furthermore, was winner of InovaInfra Award in 2020 and 2021.
Lorena Oliveira, Brazil
Sanderllan Costa, Brazil
Bárbara Vilar, Brazil
Recognised Authors
Artificial intelligence in engineering: Transforming processes and overcoming challenges
Artificial Intelligence (AI) has become one of the most discussed and relevant topics of the day. Its presence is so extensive that practically every industry is being transformed by it in some way. It is essential, however, to understand that although AI is a recent phenomenon in terms of popularisation, its roots and development began many decades ago.
The history of AI dates back to the 1950s, when Alan Turing and other pioneers began to explore the possibility of creating intelligent machines. Since then, AI has evolved significantly, going through several phases of development, from the creation of the first machine learning algorithms to current advances in deep learning and generative AI. The first AI systems were limited and required vast computing resources, but as technology has advanced, the capabilities of AI have expanded exponentially.
In recent years, there has been a significant leap in investment in AI and companies and governments around the world are allocating substantial resources to the development and implementation of this technology. This investment is driven by AI's promise to transform entire industries and, in Brazil, 74% of companies already use AI, demonstrating the growing adoption and impact of this technology in the Brazilian market.
Brazil has stood out in Latin America as a leader in technological innovation, with startups and large companies adopting AI solutions to solve complex problems and improve operational efficiency. This leap in investment is being justified by several factors.
Firstly, there is high global connectivity, and the availability of Big Data has provided a rich foundation for the development of AI solutions. Secondly, the cost of computing has fallen significantly, making it more viable for companies of different sizes to invest in cutting-edge technology.
Additionally, another crucial factor is the structuring moment for AI, as the Natural Language Processing (NLP) field of study allows machines to better understand human language, enabling easier communication and, consequently, more assertive results. The promise of increasing productivity and efficiency, reducing costs and improving the quality of products and services has been a major driver for the adoption of AI, which has revolutionised many areas.
In health, for example, AI is being used for more accurate diagnoses and the development of personalized treatments. In education, it is personalising the learning experience to better meet students' individual needs. In the financial sector, AI is helping to detect fraud and manage risks more effectively.
In engineering, AI is playing a crucial role by transforming processes and optimising operations. The integration of AI with geographic techniques has transformed the way spatial data is analysed and used. The combination of machine learning algorithms with satellite images and GIS data allows for more accurate and efficient analysis of geographical phenomena, including changes in land use, prediction of natural disasters and management of natural resources.
TPF Engineering is an example of a company that has adopted AI to tackle various engineering challenges, integrating it with maps and other technologies such as drones and mini-ROVs. In assessing the quality of canal works, AI was used to automatically identify non-conformities in images captured by drones, such as cracked concrete slabs, rockslides and exposed waterproofing blankets, enabling efficient inspection of vast construction sites and reducing the time needed to identify pathologies by 65%.
In hydrological studies on dams, an AI was developed to detect pipes in reservoirs from aerial surveys carried out with drones, achieving 89% accuracy in this identification, speeding up studies, optimising the field process and increasing the accuracy of the results. In inspections of underwater port structures, the catches obtained with a mini-ROV have been linked to AI to identify and count pests, such as sun coral, with an accuracy of 88%, optimising the control and removal of these corals, reducing the diving time required and promoting greater safety in operations.
FIGURE 1: COLONY COUNT BY AI
Recognised Authors
Artificial intelligence in engineering: Transforming processes and overcoming challenges
In geotechnical mapping projects, AI has been used to identify potential areas for urbanisation, taking into account critical geofactors such as slope, drainage density, climate, as well as restrictions such as environmental reserve areas. These projects not only increased the efficiency and accuracy of operations, but also provided tangible benefits such as minimising environmental impact by avoiding sensitive areas and reducing the need for major interventions on the ground.
For example, similar to the application of AI in the zoning review of a Port Industrial Complex, optimising the organisation of space based on a detailed geotechnical suitability map.
But despite its revolutionary innovations, AI brings up important discussions about ethics, security and cybersecurity. The use of AI must be accompanied by the need to guarantee data privacy and ensure transparency. Cybersecurity is a growing concern, as AI systems can be vulnerable to attack and manipulation.
Additionally, there are concerns about the impact of AI on the labour market, with the potential to replace manual jobs, but also create opportunities in areas such as systems development and maintenance. Guidance and education on the use and employment of this technology are crucial. Companies and educational institutions need to work together to ensure that professionals are properly trained to use and develop AI technologies ethically and effectively. Regulations and jurisdictions also needed to establish standards and guidelines that protect the rights of individuals and promote its responsible use.
The future of AI is promising, with projections of continued growth and innovation. In the coming years, AI is expected to continue to integrate further into our daily and professional lives, bringing new solutions and challenges. The next steps include developing more advanced and safer AIs, creating appropriate policies and regulations, and promoting continuous education to prepare future generations for an increasingly AI-driven world.
As Kai-Fu Lee, one of the leading AI experts and author of the book AI Superpowers, said, "AI will transform all industries and the world will be reinvented by AI", a reality that is becoming increasingly evident. With AI, engineering is being redefined, offering new ways of tackling challenges and improving project efficiency. It is essential that the sector continues to adopt and integrate these technologies to remain competitive and sustainable. We are amid a technological revolution, and it is essential to follow and actively participate in this movement in order to make the most of the opportunities that this technology offers.
FIGURE 2: MAP OF POTENTIAL AREAS
Recognised Authors
How can companies approach the free time of their employees who use Gen AI to optimise their processes?
Cynthia Lewis Torres is an economist and specialist in organisational communication with over a decade of professional experience, currently pursuing a master's in internet business. She excels in corporate communication strategies, brand management and public relations, and possesses advanced skills in writing and content editing.
With a deep understanding of the digital ecosystem, including key technologies and agile methodologies, Cynthia has led multiple digital innovation projects. She maintains excellent interpersonal relationships and a vast network of contacts in media and industrial sectors, ready to drive strategic solutions and enhance organizational performance in competitive and digital environments.
Currently, she is the director of communications and marketing at Joyco1, a Colombian company providing expert advice throughout the entire lifecycle of civil infrastructure projects, from planning and construction to operation and maintenance.
Recently, the Generative Artificial Intelligence (Gen AI) has a greater degree of participation and use from the workers within companies. Its democratisation through the ease of its use for doing repetitive daily tasks has made more and more people incorporate conversations with questions such as: “what prompt did you use for this answer?” or, “how did you ask because this answer is not correct for the request?”
Thus, every day, those who use tools with Gen AI gain experience to achieve better results in each consultation and make life easier in their daily work. This implies a challenge, however, for companies that want to control the use of this type of AI, in order to be competitive and productive without losing their know how. Furthermore, it is important to progress without feeling that employees’ newly released free time may now be wasted as they now perform a task in minutes that previously involved hours of work.
In this case, the first thing we should reflect on is the role that tools such as ChatGPT, Copilot or Gemini should have in people's work roles. Although these tools are increasingly more powerful thanks to daily developments, one should not lose sight of the fact that their main function is to “be an assistant” to those who use them. Under this concept, it is also essential that they have the supervision of a human expert on the subject and who is knowledgeable about what companies do.
The concept of “having an assistant who helps you do your tasks faster and makes your life easier,” provides companies with a new great challenge in their culture and that is to identify actions to promote the good use of free time of your employees after process optimisation with Gen AI. The time and productivity gain if used in the best way, can contribute to strengthen the positioning of companies in the market and improve competitiveness by differentiating themselves from others.
The experience of several countries shows AI helps to improve the productivity and satisfaction of the employees. The latest Work Trend Index studies, conducted by Microsoft and LinkedIn in May 2024, highlighted that, in the words of its chairman and CEO Satya Nadella, “AI is democratising expertise across the workforce. Our latest research highlights the opportunity for every organisation to apply this technology to drive better decision-making, collaboration and ultimately business outcomes”2
1. Joyco’s website: https://en.joyco.co/
2. Microsoft and LinkedIn release the 2024 Work Trend Index on the state of AI at work: https://blogs.microsoft.com/blog/2024/05/08/microsoft-and-linkedin-release-the-2024-work-trend-index-on-the-state-of-ai-at-work/
Cynthia Lewis Torres, Columbia
Recognised Authors
How can companies approach the free time of their employees who use Gen AI to optimise their processes?
The result of this study shows that workers want to use AI and don’t wait for companies to act. For this reason they decide to learn individually and appropriate these tools in their jobs. There are some insights of the survey to 31,000 people across 31 countries, for identifying labour and hiring trends from LinkedIn 75% of knowledge workers around the world use generative AI at work, 78% of AI users are bringing their own AI to work, while 79% of leaders believe their company needs to adopt AI to stay competitive, 60% of leaders worry their organisation’s leadership lacks a plan and vision to implement it3 These insights are relevant for maintaining the reflection about cultural organisational challenges. Companies have to focus on the Gen AI advantages and promote actions around research and capacitation to be more competitive and mark the markets difference.
Regarding this, Karim R. Lakhani from the Digital Data Design Institute at Harvard and Dorothy and Michael Hintze Professors of Business Administration at Harvard Business School, said that "We’re at the forefront of integrating AI to not just work faster, but to work smarter. It’s our responsibility as organisational leaders to ensure that this technology elevates our teams’ creativity and aligns with our ethical values"4
Likewise, there is evidence that claims that employees, by freeing themselves from repetitive tasks, can have a stronger interest, motivation and improve energy levels because they have freed up mental power to think about creative solutions, which opens an opportunity for companies to take advantage of.
For this reason, company leaders, through their human talent teams, can create strategies that encourage this creativity and drive for all workers and, why not, break patterns in the ways of hiring and motivating them in a clear and open manner.
“Admittedly, AI is a disruptor in the marketplace. But instead of viewing AI as a threat to human capital, employers, employees and gig workers, we should consider leveraging AI to excel at work. By delegating repetitive, boring tasks to AI, we can increase morale and energy and allow individuals to focus on tasks that truly showcase their capabilities”, said Mary Purk, executive director of AI at Wharton School of the University of Pennsylvania, in February of 20245
So, in addition to delimiting and establishing ethical guidelines around the use of AI in organisations, the execution of strategies that allow people's potential to be elevated are neededwhile also contributing to the achievement of strategic business goals to raise the potential of people, therefore contributing to the strengthening of corporate reputation. Ultimately, this is very important for improving customer attraction and retention.
3. 2024 Work Trend Index Annual Report from Microsoft and LinkedIn: https://assets-c4akfrf5b4d3f4b7.z01.azurefd.net/assets/2024/05/2024_Work_Trend_Index_Annual_Report_Executive_Summary_663b2135860a9.pdf
4. AI at Work Is Here. Now Comes the Hard Part: https://www.microsoft.com/en-us/worklab/work-trend-index/ai-at-work-is-here-now-comes-the-hard-part
5. AI and the Workforce: How Gen AI Can Help Employees Flourish: https://knowledge.wharton.upenn.edu/article/ai-and-the-workforce-how-gen-ai-can-help-employees-flourish/
Recognised Authors
Enhancing construction project performance through AI-based delay management system
Theerapon Jiratammakun is a civil engineer specialising in technology-driven solutions to improve construction efficiency. During his master’s degree studies at Chulalongkorn University, he conducted research on the application of UAVs, point cloud technology, and construction simulation for optimised planning.
Currently, he serves as an R&D engineer at Project Alliance Co., Ltd., focusing on developing a machine learning recommendation system for construction projects. He continues to seek innovative solutions, leveraging his multidisciplinary knowledge to drive efficiency in the construction sector.
Introduction
Construction projects commonly face delays due to various factors. AI-based delay management systems offer a solution to identify and address potential delays through data collection, analysis, predictive modelling and providing mitigation recommendations. In this paper, we present a comprehensive methodology for developing an AI-based delay management system.
Our findings demonstrate the value of AI-based delay management systems as a powerful tool for construction project managers. By leveraging these systems, project managers can effectively identify and mitigate delays, leading to improved project performance, cost reduction and enhanced efficiency.
Literature review
Jaisue, Peansupap, and Tongthong (2019) propose a framework for utilising Big Data and Artificial Intelligence (AI) to identify resolutions for causes of delays in construction projects.6 By leveraging the power of AI, the industry can overcome the challenges posed by the high volume, variety and velocity of construction data. The proposed approach, demonstrated through the online Daily Delay Report (DDR) and expert feedback, provides a systematic and data-driven framework for identifying and resolving causes of delay, ultimately enhancing project delivery and minimising disputes.
Owner Changes in scope
Unrealistic expectations
Designer Inadequate planning and scheduling
Errors and omissions in drawings and specifications
Consultant Lack of experience or expertise
Poor communication with project team members
Contractor Inadequate planning and scheduling
Poor workmanship
Others Acts of God
Government regulations
Potential Use of Big Data and Artificial Intelligence Application to Identify Resolutions for Causes of Delays. In Proceedings of the 19th International Conference on Construction Applications of Virtual Reality (pp. 72-83). Bangkok, Thailand.
Theerapon Jiratammakun, Thailand
SOURCE CAUSES OF DELAY
TABLE 1. EXAMPLE CAUSES OF DELAY
6. Jaisue, N., Peansupap, V., & Tongthong, T. (2019).
Recognised Authors
Enhancing construction project performance through AI-based delay management system
Methodology
The following methodology outlines the steps involved in developing an AI-based delay management system for construction projects:
1. Data Collection: Collect data from daily reports of ongoing projects, capturing project-specific details such as name, type, construction stage and any reported delays.
2. Recommendations for resolutions: To generate recommendations for resolving identified delays. These recommendations may include adjusting the project schedule, allocating additional resources, or initiating negotiations with stakeholders.
3. Correction of AI suggestions: Review the AI-generated suggestions and correct any inaccuracies or discrepancies based on the project manager's expertise and experience, further refining the system's future recommendations.
4. Model retraining: Retrain the AI model using the corrected suggestions to enhance its accuracy and performance. The model can be retrained using the original data or by incorporating new data that has been collected since its initial development.
Result: PAC-DMS: Delay Management System7
To demonstrate the effectiveness of the AI-based delay management system, Project Alliance Co., Ltd. has developed a web application specifically designed for delay management. This in-house system is utilised in their projects to effectively address and mitigate delays.
The AI algorithms within the system analyse this data, identify potential delays and generate recommendations for resolution. These recommendations are based on historical project data, industry best practices and real-time insights gathered from ongoing projects.
Through the utilisation of the web application and its AI capabilities, Project Alliance has been able to reduce the average delay duration by 139 days per project per year. Additionally, the system enables faster identification and resolution of delays, resulting in a reduction of 34 days per cause per project per year in delay duration.
Conclusion
The AI-based delay management system offers significant benefits to construction project managers, including improved project performance, cost reduction and increased efficiency. By leveraging AI technology, project managers can make informed decisions, optimise resource allocation and foster effective stakeholder communication. The success of this study underscores the value of integrating AI into the construction industry, providing a valuable decision support tool that revolutionises project management practices and drives better project outcomes.
7. Project Alliance Co., Ltd., Bangkok, Thailand. (www.projectalliance.co.th/new)
FIGURE 1: PAC-DMS: DELAY MANAGEMENT SYSTEM
About FIDIC
FIDIC, the International Federation of Consulting Engineers, is the global representative body for national associations of consulting engineers and represents over one million engineering professionals and 40,000 firms in around 100 countries worldwide.
Founded in 1913, FIDIC is charged with promoting and implementing the consulting engineering industry’s strategic goals on behalf of its member associations and to disseminate information and resources of interest to its members. Today, FIDIC membership covers over 100 countries of the world.
FIDIC member associations operate in around 100 countries with a combined population in excess of 6.5bn people and a combined GDP in excess of $30tn. The global industry, including construction, is estimated to be worth over $22tn. This means that FIDIC member associations across the various countries are worth over $8.5tn.
Disclaimer
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