Applications of AI in Construction: From ambition to practice

Abstract

The topic of Artificial Intelligence (AI) in construction has sparked a lot of interest in recent years, with the emergence of new techniques, algorithms, and tools that have enhanced the way machines learn, reason, and interact with the real world. As a result, AI has moved from a largely theoretical field to a practical one, with a wide range of applications across industries. Despite an increasing interest in AI in the construction industry, a gap remains between the potential the technology holds and its actual implementation at scale; there appears to be more hype than practical application. Previous research has extensively explored the technical development of AI systems for specific areas of application, but more research is needed on the application of these systems in the construction context. More research on system design is necessary, and roadmaps and methodologies on how this can be done in practice are needed. This thesis explores the thematic intersection between the topics of project management, sustainability, and AI – an intersection that, until now, has remained relatively unexplored. Bridging the research gap is believed to help unlock the potential that AI holds for the construction industry. The thesis addresses the following Research Questions (RQs): • RQ1: What is the current state of the field, and what are the main challenges the field is facing? • RQ2: What are the main dimensions of AI development and deployment in a construction context? • RQ3: How can industry actors move from ambition to practice – starting today? The work presented in this thesis is an extended summary of the research activities carried out throughout the PhD project period. The thesis is built on six studies and the resulting papers. Paper I found that the biggest knowledge gap in the field is related to the practical implementation of AI technologies, and the implications related to the scalability and robustness of these technologies. Paper II proposed a set of effective AI-powered measures for waste reduction on construction sites and outlined relevant practical implications. The study defined a possible approach for developing a holistic implementation framework. Paper III illustrated how meaningful AI-based analyses can be conducted for low-resolution construction data. In Paper IV, the main barriers related to effective data management in the construction context were identified. Paper V explored how AI systems can be implemented as an integral part of existing processes, rather than an add-on. In Paper VI, AI proficiency and maturity among AI system developers, users, and implementers were assessed, and a system level implementation framework proposed.

Current state and main challenges The construction industry is widely considered less digitalised compared to other industries. Still, progress is demonstrated for construction by both researchers and industry actors. On the system level, a wide range of tools have been developed and successfully applied. However, few report on the use of AI beyond pilots and Proof of Concepts (PoCs); most research is focused on the potential use or technical development of AI models. On the project level, in-house or commercially available tools have been applied to one or more activities and processes. Findings indicate that this is generally done in isolation, meaning that next to no changes are made to how the project is planned or executed. This, in turn, means that the AI system simply becomes an add-on. On the organisational level, many actors are talking about digitalisation and utilization of AI. Yet, similarly to on the project level, required infrastructure is rarely established outside the group or department responsible for the development. Challenges found across the system, project, and organisation levels are uneven application of resources to problems; lack of data and metadata; lack of anchoring in strategy; application work becoming too resource intensive; gaps between the academic field and the industry; limited transferability; lack of contextualisation; and fragmentation. Main dimensions of implementation and integration Findings and discussions uncovered seven main dimensions of implementation and integration of AI systems and tools in the construction context. The dimensions are strongly interrelated and interdependent. The dimensions are identified as data management; characteristics of the AI model; deployment; monitoring and maintenance; the human factor; organisational structures, roles, and responsibilities; and ethical considerations. Proposed frameworks The proposed system level framework is built to facilitate streamlined integration with existing processes and activities. The framework consists of seven steps: (S1) identifying the problem, (S2) assessment of feasibility, (S3) data collection, (S4) data pre-processing, (S5) model development, (S6) integration, and (S7) maintenance and monitoring. Fifteen sub-steps are defined, to guide the development and implementation process. The framework for the project level is based on the NS 3467:2023 Stages and deliverables in the life cycle of construction works (Standard Norge, 2023) and outlines relevant areas of application, stakeholder management activities, and elements of infrastructure for each of the defined project phases. On the organisation level, establishing data warehouses is identified as the most effective way to facilitate sustainable development and deployment of AI – both on the system and project level. Data fetched from the data warehouse can be used for analytics, data mining, reports, and system development. The main contributions of the thesis can be summarised as follows: • Bridging a gap between the fields of project management, AI, and sustainability. • Empirical validation and detailed descriptions of practical implications as a supplement to conceptual theory. • Providing a comprehensive and practically oriented overview of the current state of the field and identify the eight perceived main challenges to hinder effective and efficient application. • Identifying the main dimensions of AI system development and implementation. • Proposing standardised frameworks for the system, project, and organisation level. The frameworks are expected to contribute to increasing transparency, collaboration between stakeholders and to ultimately increase the sustainability of the process of development and implementation. For academics, the thesis provides a well-defined starting point with many opportunities for future research. The thesis provides empirical validation of findings in a field that has previously been lacking empirical data and research on implementation and performance beyond small-scale testing and PoCs. Practitioners can gain a deeper understanding of the potential and limitations within their own practices to take the first of many steps towards effective application of AI.