Leveraging ai in construction management

Abstract

A 2020 McKinsey analysis shows that the construction sector accounts for 13% of the global GDP. The sector affects economic growth and quality of life through its real estate, infrastructure, and industrial projects. Roughly 7.22% of the world's workforce works there. Nevertheless, the industry faces problems with labour productivity, which result in material and financial waste and generate delays, as well as cost overruns. Digital technologies like AI, BIM, and IoT have the potential to increase production and efficiency greatly, but the industry is adopting them slowly. Despite the difficulties, the demand for environmentally friendly building practices and the effects of recent occurrences such as the COVID-19 pandemic highlights how urgently digital transformation is required. Artificial Intelligence (AI) has a significant opportunity to improve decision-making and operational efficiency, potentially increasing industry output by $1.6 trillion annually. This thesis aims to investigate the incorporation of Artificial Intelligence (AI) in construction management to improve efficiency and tackle ongoing problems within the industry. Poor labour productivity and reluctance to digital change are problems facing the construction industry, which is vital to the global economy and employment. The research has been divided into two sections: a thorough literature review on artificial intelligence in construction management and a thorough analysis using bibliometric, text-mining, and content analysis tools. Through the lens of artificial intelligence (AI) technologies, the research focuses on the adoption and impact of digitalization on construction project management. The introduction, a thorough literature review, an explanation of the research methods in detail, a discussion of the findings, and a final chapter summarizing conclusions and suggestions comprise the five chapters that make up the thesis. The literature review dives into the development of AI and how it can be used from a management standpoint in the construction industry. It investigates the industry's innovation and digitisation trends, emphasising how slowly adoption is happening compared to other industries. The evaluation notes the increasing importance of AI in a range of construction-related operations. It emphasises the need for a more creative strategy to overcome the industry's historical resistance to change. This part of study examines artificial intelligence's (AI) involvement in the construction industry, focusing on how it developed, affected construction management, and how it was used throughout the lifecycles of projects. Its initial goal is to comprehend construction management; after that, it investigates how it relates to innovation and investigates artificial intelligence and cutting-edge technologies. The final section of the literature research explores the connection between artificial intelligence (AI) and construction project management, emphasising the potential applications of AI in the construction industry. The construction sector is gradually embracing digital technology, including artificial intelligence (AI), despite obstacles like an excessive dependence on manual labour and the unique nature of each project. Although the industry's inherent fragmentation and old procedures slow this transformation, technological developments have the potential to increase production and efficiency dramatically. AI is predicted to transform construction management by making it possible to execute projects more meticulously and effectively. To increase the efficiency and sustainability of the construction sector, future research will concentrate on integrating AI into the processes more thoroughly, overcoming obstacles like cost, cultural resistance, and technological difficulties. The review highlights AI's vital role in advancing industry standards and meeting complicated project needs, outlining its enormous potential and current limitations in construction management. AI has the potential to transform construction management significantly, but broad adoption and achieving the most achievable benefit still depend on overcoming enormous implementation challenges. A mixed-methods approach comprising content analysis, text mining, bibliometric analysis, and a comprehensive literature review is described in the methodology section. Identifyingkey research themes, patterns, and gaps in the literature facilitates a comprehensive understanding of the state of artificial intelligence (AI) in construction management. Bibliometric data analysis shows a growing trend in AI research in the construction industry, particularly after 2019, which suggests that people are becoming more aware of AI's potential. The most prominent journals in the discipline are addressed, together with yearly trends in article publication and citation analysis, to determine key publications. To investigate the relationship between collaboration and impact, the study analyzes important countries and organizations. The field's most important research subjects and hotspots were determined by using keyword co-occurrence analysis. While text-mining and content analysis provide a greater understanding of popular topics and the field's conceptual framework, bibliometric analysis highlights important research themes and trends. The text mining analysis carried out to identify important research topics in artificial intelligence applications for construction management is covered in the second section of the data analysis. This research was conducted with the help of keyword co-occurrence data. VOSviewer was used to evaluate the titles and abstracts of 71 papers. A network visualization illustrates the connections and frequencies of term interactions found through the co-occurrence analysis. The image demonstrated the phrases' interrelation with node size reflecting keyword frequency, line thickness indicating association strength, and node closeness indicating strong linkages,. The two primary areas of research themes were "Artificial Intelligence Applications" and "Construction Management." This classification helped to examine the use of AI in construction management in-depth by organizing the findings into coherent clusters. The investigation revealed various AI applications that are crucial for construction management: Building Information Modeling (BIM), deep learning, machine learning, support vector machines (SVM), fuzzy logic, genetic algorithms, neural networks, automation, and computer simulation were among the applications of artificial intelligence. Practical factors such as construction engineering, project management, scheduling and planning, cost estimation, and resource allocation are the main focus of construction management. To demonstrate the frequency and overall link strength of each AI application, each category was further examined. This revealed the applications' prominence and connectivity within the field of study. The study emphasized the direct relationship between AI applications and concepts like productivity and optimization across the industry. The text's accompanying figures and tables gave readers a thorough understanding of the AI technologies leading the industry and highlighted their widespread adoption and relevance in today's corpus of research. The final section of data analysis applied in this study is content analysis, utilizing qualitative data from academic publications to analyse research objectives related to creating a status-quo understanding of the area. The main goal is to investigate studies how the applications of artificial intelligence (AI) and construction management. To find scholarly works relevant to AI applications in construction management, a thorough search was carried out. To find relevant information, the search utilized key terms refined by text mining. An extensive annotation procedure followed discussions of different AI applications, highlighting examples that went beyond the initial search parameters. Subjects related to construction management were used to identify these articles. These applications improve decision-making, provide predictive analytics, and raise the project's general efficiency and safety. Cost management, project management, time management, and construction engineering are just a few areas in which artificial intelligence (AI) technologies significantly improve construction management. When AI approaches are included, construction projects benefit from increased accuracy, efficiency, and decision-making . The analysis shows how AI can completely transform the construction sector and offers a path forward for future developments. In the research study, robotics, computer vision, and machine learning are the three leading AI technologies revolutionising construction management. These technologies improve decision-making, provide predictive analytics, and raise the project's general efficiency and safety. The thesis acknowledges the difficulties of using AI in the construction industry, such as the lack of skilled labour, cultural opposition, and infrastructure limitations. LEVERAGING AI IN CONSTRUCTION MANAGEMENT SUMMARY LEVERAGING AI IN CONSTRUCTION MANAGEMENT SUMMARY The thesis concludes by highlighting the importance of adopting AI and digital solutions in the construction sector. It offers tactics for raising digital literacy, encouraging creativity, and creating laws that will encourage the use of AI. The real-world outcomes affect researchers, legislators, and business professionals alike, promoting cooperation, the creation of new laws, and additional research into the revolutionary possibilities of artificial intelligence. Future studies will focus on ethical issues, the integration of AI with other developing technologies, and the long-term effects of AI on occupational roles and skills. The thesis highlights the possibilities for higher efficiency, innovation, and a more resilient and sustainable economy, and it calls for a proactive strategy to harness AI in the construction sector.