Employing Artificial Intelligence and Virtual Reality to Support the Decision-Making Process in AEC Industry.

The Architecture, Engineering, and Construction (AEC) industry is becoming increasingly complex due to several challenges, requiring the imperative adoption and implementation of innovative methods to mitigate these challenges. This research analysis of the current industry practices identifies serious challenges associated with inefficiencies in communication during the design phase, leading to higher numbers of change orders during construction. The current study examines the integration of virtual reality (VR) and machine learning (ML) applications to support the decision-making process and enhance project outcomes. The findings not only confirm the positive impact on spatial comprehension, confidence levels, and design review time; they also confirm the positive impact on the project delivery process by detecting errors and missing data at an early design stage. This indicates that integrating immersive environments with data-driven insights provides a more innovative and user-centric design outcome. The early integration of VR and ML in the project delivery process can improve both design and construction outcomes. This research underscores the industry's obligation to allocate more resources towards research that facilitates the creation and evaluation of innovative machine learning algorithms and virtual reality applications designed to address specific challenges in design and construction.

Publication Details

Journal
ThinkTech (Texas Tech University)
Published
2024-12-01
Primary Topic
BIM and Construction Integration
Type
article
Field-Weighted Citation Impact
0.00
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Employing Artificial Intelligence and Virtual Reality to Support the Decision-Making Process in AEC Industry.

ThinkTech (Texas Tech University)
BIM and Construction Integration
article

Employing Artificial Intelligence and Virtual Reality to Support the Decision-Making Process in AEC Industry.

article en

Abstract

The Architecture, Engineering, and Construction (AEC) industry is becoming increasingly complex due to several challenges, requiring the imperative adoption and implementation of innovative methods to mitigate these challenges. This research analysis of the current industry practices identifies serious challenges associated with inefficiencies in communication during the design phase, leading to higher numbers of change orders during construction. The current study examines the integration of virtual reality (VR) and machine learning (ML) applications to support the decision-making process and enhance project outcomes. The findings not only confirm the positive impact on spatial comprehension, confidence levels, and design review time; they also confirm the positive impact on the project delivery process by detecting errors and missing data at an early design stage. This indicates that integrating immersive environments with data-driven insights provides a more innovative and user-centric design outcome. The early integration of VR and ML in the project delivery process can improve both design and construction outcomes. This research underscores the industry's obligation to allocate more resources towards research that facilitates the creation and evaluation of innovative machine learning algorithms and virtual reality applications designed to address specific challenges in design and construction.

ThinkTech (Texas Tech University)
Industry, innovation and infrastructure
Openalex Percentile: Top 55%
BIM and Construction Integration
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.