Building a Next Generation AI Platform for AEC: A Review and Research Challenges

Abstract

Transformation of the AEC industry has been found to face significant and persistent hurdles both internal and external to the industry, such as industrial fragmentation, difficulty in talent recruiting, inadequate collaboration and knowledge transfer. Meanwhile, advances and maturity in digital technologies recent years offer tremendous potential to tackle these challenges. Examples include Artificial Intelligence (AI) and deep learning, computer vision, big data analytics, Building Information Models (BIM), 3D scanning, Augmented Reality (AR), Unmanned Aerial Vehicles (UAVs), autonomous driving, and blockchain. We envision that the next generation platform for the AEC industry shall integrate this diverse list of digital technologies. In this paper, we review the stateof-the-art in related literature aiming at creating such an integrated platform. Our review is organized into the following four themes, and for each of them, we also identify gaps and challenges. 1. AI-based BIM model generation: this topic addresses how to make BIM models more ubiquitous, which includes the automatic generation of As-planned BIM model from 2D drawings and the automatic generation of As-built BIM model from site images/videos and point clouds. 2. Blockchain-based collaboration: this theme explores the possibilities of applying Blockchain for AEC to improve various essential aspects such as trust in multi-party collaboration and contract enforcement. 3. AR-based visualization: this topic studies the AR-based visualization for construction management, which aims to increase the efficiency of information transmission. 4. AI-based Construction management: this area reviews the application of AI in progress monitoring, safety management, quality management, and contract management. Based on the above analysis, we conclude by outlining potential topics of a research framework for creating a next generation AI platform for AEC.

Towards an AEC-AI Industry Optimization Algorithmic Knowledge Mapping: An Adaptive Methodology for Macroscopic Conceptual Analysis

“…We review some antecedents already in the literature regarding the use of AI in the AEC industry. We summarize the results from few reviews directly addressing the AEC and AI topic [1]- [4], [9]- [11], [13].…”

“…Advances taking place in the AEC industry and AI-related technologies have motivated several reviews, most of them focusing either on a specialized topic or in broad bibliometric overview [1]- [4], [10]. In [2], scientometric analysis on networks of selected keywords searches in Scopus resulted in almost 42.000 publications.…”

“…Different technologies such as blockchain, virtual and enhanced reality, cloud, fog, and distributed computing as well as sensing, all of them with enormous potential to be integrated into AI concert. Similarly, integrated systems using BIM platforms in combination with safety and risk monitoring and assessment are required [4], [43]- [45]. Probably these efforts and analyses make a bolder requirement to introducing Big-data into the AEC-AI industry as a cognitive platform for human development.…”

“…Communication and Information Technologies (CIT) will boost and reshape the industry in almost every aspect: from Edge Computing to 5G and blockchain technologies processes and operations will be substantially transformed. The results will be a migration towards an ever more important role of data generation, analysis, and integration [3], [4]. AI optimization will reach the more complex areas in the AEC industry, and robotic automation promise to recapture interest in an increasingly more sophisticated workforce along with new skills and talents emerging in the technological sectors [5].…”

“…At the time of also revealing where more effort and challenges need further developments, is still missing [2], [11], [12]. However, the research efforts addressing the strength and weakness in developing the AEC-AI industry requires advancing in multiple fronts in an integrated way across a very complex infrastructure of digital and physical components and processes [4], [13]. Here we develop a review in the state-of-the-art, complementary to previous efforts, addressing more specifically the developments and scenarios of algorithms' operation.…”