Related papers
APPLICATION OF ARTIFICIAL INTELLIGENCE FOR CONSTRUCTION PROJECT PLANNING
Louis Oghenemaro Sota
Artificial Intelligence is a field that is becoming widely used in almost all industries, its applications are proven to increase efficiency in the workplace. The construction industry is one industry that has been slow to adapt to the digital era, with companies failing to utilize advanced technologies and adopt Artificial Intelligence systems. Construction projects are unique projects and often exceedingly large, complex and safety critical making it difficult to introduce change, thereby relying on traditional processes. The processes used to plan construction projects involve a wide variety of knowledge areas and several stakeholders that must all be aligned in-order for the project to be successful. Artificial Intelligence is a promising technological advancement that is expected to drastically improve project management for construction projects. In spite of these promising benefits, AI has not been properly explored due to several factors such as its large implementation costs, data preparation needs, lack of AI strategies and lack of skilled personnel. This research work is a pioneer in this regard and is an evaluative investigation into how artificial intelligent solutions and techniques can be used to support project managers and related professionals to properly plan and manage construction projects. In order to achieve this objective, a mixed method research design was used to collect both quantitative and qualitative data using structured online survey questionnaires, this provides an in-depth understanding of the topic as well as identifying the challenges faced by professionals and organizations, followed by recommended steps to implement AI in their workflow. The research proved fruitful in understanding the needs of construction project professionals and organizations and has been successful in establishing a framework for AI experts to use for the purpose of developing future AI solutions for construction project planning
View PDFchevron_right
Role of Artificial Intelligence in Construction Project Management
bhagyashree khartode
E3S Web of Conferences
The construction business currently contributes 13% of the world's Gross Domestic Product (GDP), and it is anticipated that by the year 2030, its value would have increased by 85%, reaching $15.5 billion globally. China, the United States of America, and India are the three countries that are most responsible for the demand in the building business. Keeping subcontractors, contractors, designers, clients, and other parties routinely supplied with vast amounts of information has been one of the most challenging difficulties in the construction industry. The application of Information Technology (IT) has significantly contributed to the integration of disparate pieces of information within the context of widely dispersed construction projects. The construction sector, including the full construction value chain, is presently going through a period of transformation. The amount of money that is being invested into Artificial Intelligence (AI) is rising at a rate that is almost impo...
View PDFchevron_right
A COMPREHENSIVE LITERATURE REVIEW OF RESEARCH TRENDS OF APPLYING AI TO CONSTRUCTION PROJECT MANAGEMENT
Iman MerdzanovicSee AlsoAfter the Birds - redheadgrrl1960THE INQUISITION, THE DARK SIDE OF THE CHURCH38 Best Stops Between Newport and Cardigan40 Best Stops Between San Francisco and Portland
Artificial Intelligence (AI) is universally reaching every industry thus far, it is coming to the fore in construction industry by redesigning project world and altering the role of project managers. It provides the ability for computers to simulate human-like thinking and is comprised of machine learning, Internet of things (IoT), automation, natural language processing and robotics. In construction industry, AI is affecting project planning, time and cost management, optimization of resources as well as post-construction activities. Along with assessing teamwork patterns and making recommendations, it also has an impact on the workforce and how individuals handle projects. Despite a broad range of research papers written about this theme there are still not so much of practical applications conducted in practice. Therefore, the aim of this study is to provide an overview of using AI in Project Management and to show how it is affecting construction industry along with exhibiting future trends. This article will also show positive and negative sides of AI in Project Management and how does it manage competencies. It will propose possible utilitarian solutions including a critical review over the research topic. The outcomes of this study contribute to the body of knowledge on AI in project management through a comprehensive investigation of literature and provide guidelines for further research on the phenomenon of AI in project management.
View PDFchevron_right
Artificial Intelligence in AEC Industry: Construction Project Management Processes
Ilkim Güven, Osman Balli
1st International Conference on Innovative Academic Studies, 2022
Artificial intelligence is widely used in many fields. The use of artificial intelligence in the AEC (architecture engineering and construction) industry can be made through various construction projects and project management processes. With the expansion of the AEC industry in the face of technological developments, deep learning, which is included in the sector, has created new opportunities to use the existing big data to solve the problems that occur in terms of project management. The selection of the appropriate artificial intelligence model in the projects and the creation of the data set significantly affect the forecasts. In this study, studies on estimating values such as cost and duration that should be calculated before the project are discussed. The databases used in the study are The American Society of Civil Engineers (ASCE), IEEE Xplore Digital Library, Scopus, and Google Scholar. Out of a total of 103 articles explaining the project construction management processes, 8 studies were discussed, each of which contains a data set and provides information about the success rate. Information about the preferred artificial intelligence models and the data sets used for performance estimation is given. The aim of the study is to compare the performance values of the projects discussed and deal with the use of artificial intelligence, which has become popular with the technological developments in today's world, in the construction management processes, and to contribute to the development of new theories that can be a basis for interdisciplinary studies on this area.
View PDFchevron_right
The Impact of Artificial Intelligence on Construction Costing Practice
Saka Abdullahi
39th Annual ARCOM Conference, 2023
Cost estimation is a crucial process in the construction sector as the efficiency of the overall project cost serves as one metric in determining project success. Prevailing traditional approach suffers from human subjectivity and bias which affect accuracy. With the development and adoption of Artificial Intelligence (AI) such as the use of machine learning (ML) and deep learning (DL) algorithms, the construction industry is experiencing brisk technological change and new ways of working, particularly in terms of cost predictions and estimations. However, the application of AI is still in its infancy and the industry still prioritises traditional cost modelling approaches in determining early estimates. This research explores the application of the various ML methods for costing and assesses their usage and application in the costing practice via an exploratory critical review. Findings indicate that ML algorithms would improve the accuracy and efficiency of costing practice but cannot replace the professionals and data availability
View PDFchevron_right
MACHINE LEARNING -BASED FRAMEWORK FOR CONSTRUCTION DELAY MITIGATION
Muizz O Sanni-Anibire
Journal of Information Technology in Construction, 2021
The construction industry, for many decades, has been underperforming in terms of the success of project delivery. Construction delays have become typical of many construction projects leading to lawsuits, project termination, and ultimately dissatisfied stakeholders. Experts have highlighted the lack of adoption of modern technologies as a cause of underproductivity. Nevertheless, the construction industry has an opportunity to tackle many of its woes through Construction 4.0, driven by enabling digital technologies such as machine learning. Consequently, this paper describes a framework based on the application of machine learning for delay mitigation in construction projects. The key areas identified for machine learning application include "cost estimation", "duration estimation", and "delay risk assessment". The developed framework is based on the CRISP-DM graphical framework. Relevant data were obtained to implement the framework in the three key areas identified, and satisfactory results were obtained. The machine learning methods considered include Multi Linear Regression Analysis, K-Nearest Neighbours, Artificial Neural Networks, Support Vector Machines, and Ensemble methods. Finally, interviews with professional experts were carried out to validate the developed framework in terms of its applicability, appropriateness, practicality, and reliability. The main contribution of this research is in its conceptualization and validation of a framework as a problem-solving strategy to mitigate construction delays. The study emphasized the cross-disciplinary campaign of the modern construction industry and the potential of machine learning in solving construction problems.
View PDFchevron_right
Building Information Models’ data for machine learning systems in construction management
Dimosthenis Kifokeris, Docent, PhD
2019 Creative Construction Conference Proceedings, 29 June – 2 July 2019, Budapest, Hungary, 2019
Qualitative and quantitative data are important in construction management. However, despite the capabilities of construction informatics, such data and its sources have scarcely been fully and systematically utilized for predictive purposes. Building Information Models (BIM) are such a data source. Within BIM, information structures enabling interoperability and providing utilizable data throughout the various Levels of Development (LODs) of a building – for example, Industry Foundation Classes (IFCs) – can be fully and meaningfully exploited through data mining, and more particularly, via machine learning. In this paper, the capabilities of the information structures found in IFCs to be used as data sources for developing machine learning predictive models, will be examined. In addition, and by conceptually tying such data with constructability, their suitability for predicting – through such machine learning models – the delivery cost and time overheads of a construction project, will be considered.
View PDFchevron_right
Role of Artificial Intelligence in the Construction Industry – A Systematic Review
Avaneesh Mohapatra
IJARCCE
Artificial intelligence (AI) is crucial in promoting Industry 4.0 worldwide. AI has the potential to revolutionize the engineering and construction industry by automating tasks, improving project efficiency and accuracy, and enabling new capabilities. One application of AI in engineering and construction is in the design and planning phase of projects. AI algorithms can analyse data from previous projects and make recommendations for optimal designs, materials, and construction methods. This can lead to cost savings and improved project outcomes. AI can also be used in the construction phase to assist with surveying, quality control, and equipment maintenance tasks. For example, drones equipped with AI can survey construction sites and generate accurate 3D models, which can be used for progress tracking and identifying potential issues. Another area where AI can have a significant impact is in operation and maintenance of buildings. AI-powered building management systems can optimize energy usage, detect and diagnose equipment malfunctions, and predict maintenance needs. Overall, integrating AI into engineering and construction can improve project efficiency, reduce costs, and increase the safety and reliability of projects. However, it is essential to consider the ethical implications of using AI in the industry, such as potential job displacement and the need for proper training and oversight.
View PDFchevron_right
Using Machine Learning to Predict Cost Overruns in Construction Projects
Arkar Htet
Journal of Technology Innovations and Energy
Addressing the persistent issue of cost overruns in construction projects, our study explores the potential of machine learning algorithms for accurately predicting these overruns, utilizing an expansive set of project parameters. We draw a comparison between these innovative techniques and traditional cost estimation methods, unveiling the superior predictive accuracy of machine learning approaches. This research contributes to existing literature by presenting a data-driven, reliable strategy for anticipating and managing construction costs. Our findings have significant implications for project management, offering a path towards more efficient and financially sound practices in the construction industry. The improved prediction capabilities could revolutionize cost management, facilitating better planning, risk mitigation, and stakeholder satisfaction.
View PDFchevron_right
A taxonomy of machine learning techniques for construction cost estimation
Panagiotis Karadimos, Leonidas Anthopoulos
Innovative Infrastructure Solutions, 2024
Construction projects require significant funding and are exposed to several risks. Public construction projects require a major proportion of the annual government budget. Their actual cost estimation concerns a known and existing problem for the construction sector, while several project failures in terms of budget extension can be documented around the world. Accurate construction cost predictions are essential in mitigating time-related risks and play a crucial role in the decision-making process for managers. Inaccurate cost estimations can result in investment project disruptions. Research about machine learning (ML) techniques regarding construction cost estimation is intensifying, which aims to develop new ML techniques or update existing ones. This article contains a systematic literature review of ML techniques for construction project cost estimation. This review included an in-depth analysis of 219 studies, which contain the most prominent machine learning techniques. This article attempts to define a classification of the identified ML techniques, with the following criteria: intelligent technique that was followed and the application domain. The taxonomy that was generated contains ML techniques about construction cost estimation and their application, which offers useful guidance for both researchers and practitioners.
View PDFchevron_right