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Innovative Horizons: Digital Technologies in Modern Construction and Infrastructure
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Topic Editors

Offsite Construction Research Centre (OCRC), Department of Civil Engineering, University of New Brunswick, Fredericton, NB E3B 5A3, Canada
Dr. Ala Suliman
Architecture and Built Environment, Northumbria University, Ellison Pl, Newcastle upon Tyne NE1 8ST, UK
Civil and Construction Engineering, Western Michigan University, 1903 W Michigan Ave., Kalamazoo, MI 49008-5316, USA
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Innovative Horizons: Digital Technologies in Modern Construction and Infrastructure

Abstract submission deadline
30 March 2026
Manuscript submission deadline
30 May 2026
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808

Topic Information

Dear Colleagues,

The construction industry is experiencing a significant shift towards digital adoption, particularly in the realm of modern construction methods like offsite manufacturing. This digital revolution is restructuring how we approach building and infrastructure projects, offering new ways to enhance efficiency, productivity, sustainability, and overall project outcomes. Our proposed Topic Collection, about "Innovative Horizons: Digital Technologies in Modern Construction and Infrastructure", seeks to shed light on the latest advancements driving this transformation. Through the lens of the People, Process, and Technology (PPT) framework, we are particularly interested in exploring how digital tools are supporting and advancing modern construction and building techniques, as well as their application across the broader construction and infrastructure sectors. We welcome high-quality research papers that address, but are not limited to, the following focus areas:

  • Reality capture technologies in construction planning and control;
  • Remote sensing technologies in infrastructure management;
  • Drone technology for project monitoring;
  • Mobile technologies for on-site quality control and safety management;
  • Data analytics for performance improvement in modern construction Blockchain applications in sustainable supply chain management for construction;
  • AI and machine learning for project planning, optimization, and control;
  • IoT applications in smart buildings and infrastructure management;
  • AR/VR use in design and offsite manufacturing;
  • Automation and robotics in prefabrication processes;
  • Digital twins for lifecycle management;
  • BIM integration in offsite and onsite construction;
  • Cloud-based collaboration in distributed construction teams.

We invite original research, reviews, and case studies that offer fresh insights into these technologies and their impact. Our goal is to create a valuable resource for researchers and practitioners, fostering innovation in this rapidly evolving field. We look forward to your contributions to this exciting collection

Dr. Zhen Lei
Dr. Ala Suliman
Dr. Hexu Liu
Topic Editors

Keywords

  • digital construction
  • building information modeling (BIM)
  • Internet of Things (IoT)
  • artificial intelligence
  • digital twin
  • smart infrastructure
  • Construction 4.0
  • automation
  • virtual reality
  • smart cities

Participating Journals

Journal Name Impact Factor CiteScore Launched Year First Decision (median) APC
Architecture
architecture 		- - 2021 36.3 Days CHF 1000 Submit
Buildings
buildings 		3.1 3.4 2011 15.3 Days CHF 2600 Submit
Infrastructures
infrastructures 		2.7 5.2 2016 17.8 Days CHF 1800 Submit
Sensors
sensors 		3.4 7.3 2001 18.6 Days CHF 2600 Submit
Sustainability
sustainability 		3.3 6.8 2009 19.7 Days CHF 2400 Submit
ISPRS International Journal of Geo-Information
ijgi 		2.8 6.9 2012 35.8 Days CHF 1900 Submit

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Published Papers (1 paper)

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22 pages, 4976 KiB  
Article
A Cloud-Based Framework for Creating Scalable Machine Learning Models Predicting Building Energy Consumption from Digital Twin Data
by Elham Mahamedi, Alaeldin Suliman and Martin Wonders
Architecture 2025, 5(2), 29; https://doi.org/10.3390/architecture5020029 - 23 Apr 2025
Viewed by 351
Abstract
Digital Twins (DTs) of buildings can generate large volumes of dynamic data from various sources (e.g., sensors and IoT devices), enabling real-time representation of physical building states in a digital environment. Although machine learning (ML) techniques are increasingly used to predict building energy [...] Read more.
Digital Twins (DTs) of buildings can generate large volumes of dynamic data from various sources (e.g., sensors and IoT devices), enabling real-time representation of physical building states in a digital environment. Although machine learning (ML) techniques are increasingly used to predict building energy consumption from this DT data, existing approaches often lack scalability in handling data growth (data scalability) and/or adapting to evolving data patterns (model scalability). This study aims to address both drawbacks by developing a scalable cloud-based framework for the prediction of the building energy consumption. A key contribution to the field is the inclusion of a “monitoring and maintenance” module, which continuously evaluates model performance and triggers retraining only when needed. This enables timely adaptation of the ML model while avoiding unnecessary retraining and the associated computational costs. The framework was implemented and tested in a case study of a commercial building for 90 days, demonstrating its applicability. In a practical setting, the developed model could detect anomalies in time when the accuracy declined below the set threshold (70%) for five days and prevented unnecessary retraining of ML models. The findings support the feasibility of using cloud-based approaches to implement scalable ML models for energy prediction in buildings. Full article
(This article belongs to the Topic Innovative Horizons: Digital Technologies in Modern Construction and Infrastructure)
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