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Quantum and AI Technologies in Engineering and Construction Projects: Design...
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Quantum and AI Technologies in Engineering and Construction Projects: Design Challenges and Opportunities

A special issue of Designs (ISSN 2411-9660). This special issue belongs to the section "Civil Engineering Design".

Deadline for manuscript submissions: 31 July 2027 | Viewed by 4840

Editors

Dr. Siddra Qayyum

E-Mail Website
Guest Editor
Faculty of Society & Design, Bond University, Gold Coast, QLD 4229, Australia
Interests: construction management; project management; Six Sigma; risk management; quality management; digital built environment
Dr. Mumtaz Ali

E-Mail Website
Guest Editor
UniSQ College, University of Southern Queensland, Brisbane, QLD 4305, Australia
Interests: data science; artificial intelligence; deep learning; applied computing; quantum computing

Special Issue Information

Dear Colleagues,

The introduction of disruptive technologies is revolutionizing the engineering and construction (E&C) industry and associated project designs. From traditional technologies to Big Nine Techs and now quantum technologies, digital technologies have rewritten the rules of work and reshaped the modus operandi of projects in the E&C industry. E&C projects are inherently complex and dynamic, involving multiple interdependent systems, data flows, and decision layers that require advanced analytical and computational approaches. As the E&C industry faces rapid technological transformation, traditional methods of project design are becoming insufficient to capture the scale, uncertainty, and interconnectedness of modern E&C ecosystems. This complexity has created the need for emerging technologies, notably quantum computing and Artificial Intelligence (AI), to enhance how we design E&C projects and the associated modelling and optimization.

The ongoing global shift toward Industry 5.0 has accelerated the demand for intelligent, data-driven, and sustainable approaches to E&C project designs. Integrating quantum and AI technologies further amplifies both the opportunities and challenges facing the industry. These technologies enable more powerful computation, optimization, sensing, and communication capabilities, offering new ways to improve design precision and quality in uncertain E&C environments.

This Special Issue aims to gather high-quality contributions on Quantum and AI frontiers in E&C project design, including their challenges and opportunities. We invite submissions from researchers, professionals, and industry leaders in engineering, construction management, project management, digital design, and related fields. The submitted articles may explore a wide range of topics related to the role of quantum and AI technologies in transforming E&C project design and delivery, including but not limited to the following:

  • Quantum computing for optimization of E&C project design.
  • AI and machine learning for predictive design and performance analytics in the E&C industry.
  • Quantum sensing, communication, and imaging in E&C project design.
  • Digital twins, BIM, and data-driven ecosystems powered by quantum–AI integration for E&C project design.
  • Quantum sensing and imaging for E&C infrastructure design, health monitoring, and geospatial analysis.
  • Generative design and AI-driven decision-support systems for E&C project design.
  • Machine learning and deep learning applications in E&C project design.
  • AI-based risk prediction, scheduling, and cost forecasting of E&C project designs.
  • Hybrid quantum–AI models for real-time E&C design optimization.
  • Quantum machine learning for predictive E&C project design analytics.
  • Quantum-enhanced reinforcement learning for E&C project control and design automation.
  • Multi-objective quantum–AI frameworks for sustainable and resilient E&C project design.
  • Opportunities for and barriers to the adoption of quantum and AI technologies in E&C project designs.
  • Digital skills, education, and workforce transformation for E&C project design.

Dr. Siddra Qayyum
Dr. Mumtaz Ali
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 250 words) can be sent to the Editorial Office for assessment.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-anonymized peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Designs is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 1600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • quantum computing
  • artificial intelligence (AI)
  • engineering projects
  • construction project design
  • digital twins
  • building information modelling (BIM)
  • quantum sensing and communication
  • lasers
  • solar cells
  • generative design

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Published Papers (2 papers)

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35 pages, 12654 KB  
Article
An Integrated BIM–NLP Framework for Design-Informed Automated Construction Schedule Generation
by Mahmoud Donia, Emad Elbeltagi, Ahmed Elhakeem and Hossam Wefki
Designs 2026, 10(2), 43; https://doi.org/10.3390/designs10020043 - 7 Apr 2026
Viewed by 2252
Abstract
Artificial intelligence has attracted increasing attention in the construction industry; however, automated time scheduling remains limited in practical applications. Schedule development remains manual, requiring planners to analyze project documents, define activities, estimate durations, and identify relationships based on logical sequence. This process primarily [...] Read more.
Artificial intelligence has attracted increasing attention in the construction industry; however, automated time scheduling remains limited in practical applications. Schedule development remains manual, requiring planners to analyze project documents, define activities, estimate durations, and identify relationships based on logical sequence. This process primarily depends on individual experience and skills, making it both time-consuming and prone to human error. From an engineering design perspective, delayed or inconsistent schedule development weakens design-to-construction feedback, limiting the ability to evaluate constructability and time implications of alternative design decisions during early-stage planning. This study proposes an integrated BIM–Natural Language Processing (NLP) framework to automate activity identification, duration estimation, and logical sequencing for construction scheduling. The framework extracts project data from Revit, organizes it into a bill of quantities format, and then generates an activity list, each activity with a unique ID. Using Sentence-BERT (SBERT) embeddings, the framework estimates activity durations based on semantic similarity. The same semantic process is combined with rule-based reasoning to identify logical relationships, including sequences, supported by an Excel-based reference dictionary that includes logical relationships, productivity, and ID structure. Finally, the framework incorporates a crashing module that proportionally adjusts the duration of activities on the longest path to target the project’s completion time without violating relationships. The proposed framework was validated using real construction project data and produced reliable results. By producing a tool-ready schedule directly from design-model information, the proposed workflow enables earlier schedule feedback loops and supports design-informed planning by allowing designers and planners to assess the time consequences of model-driven scope changes. The results demonstrate that integrating BIM and NLP can transform conventional schedules into faster, more consistent processes, thereby supporting the construction industry. Full article
(This article belongs to the Special Issue Quantum and AI Technologies in Engineering and Construction Projects: Design Challenges and Opportunities)
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29 pages, 1851 KB  
Systematic Review
Technological Trends in Lean Construction for Engineering Design Improvement and Productivity in Civil Engineering Projects: A Systematic Literature Review
by Luis Mayo-Alvarez, Jorge Córdova-Maraví, Diego García-Gómez and Iván Paredes-Julca
Designs 2026, 10(2), 40; https://doi.org/10.3390/designs10020040 - 1 Apr 2026
Viewed by 1996
Abstract
Lean Construction has become a key strategy for improving productivity, reducing waste, and increasing efficiency in civil engineering projects. In parallel, advances in digital technologies have transformed the way engineering design and project planning processes are conceived and managed. However, there remains a [...] Read more.
Lean Construction has become a key strategy for improving productivity, reducing waste, and increasing efficiency in civil engineering projects. In parallel, advances in digital technologies have transformed the way engineering design and project planning processes are conceived and managed. However, there remains a limited systematic understanding of how emerging technologies support engineering design practices and influence the implementation and performance of Lean Construction in diverse civil engineering scenarios. This study presents a systematic literature review of 70 peer-reviewed articles published between 2019 and 2025, following the PRISMA 2020 guidelines. The selected studies were examined using a structured classification framework consisting of three analytical categories: Technologies and Tools, Construction Methods and Sustainability, and Production Philosophies and Management. From an engineering design perspective, this framework allows the identification of technological trends, design-support tools, and management strategies that influence the planning, modeling, and optimization of construction processes. The results show that digital technologies, such as Building Information Modeling (BIM), automation systems, Artificial Intelligence, and Industry 4.0 tools, play a significant role in supporting engineering design activities by improving project visualization, coordination, and decision-making during the design and planning stages. These technologies contribute to more integrated design processes aligned with Lean Construction principles. At the same time, the analysis reveals that the adoption of Lean Construction technologies varies depending on project characteristics, levels of digital maturity, and regional industry conditions. The main barriers identified in the literature include interoperability limitations, insufficient workforce training, and organizational resistance to technological change. Overall, the review provides a structured synthesis of recent research trends and highlights the technological and managerial factors that influence the successful integration of Lean Construction with engineering design practices in civil engineering. The findings contribute to bridging the gap between technological innovation, design methodologies, and Lean Construction implementation, offering insights for both researchers and practitioners seeking to improve efficiency, sustainability, and design performance in construction projects. Full article
(This article belongs to the Special Issue Quantum and AI Technologies in Engineering and Construction Projects: Design Challenges and Opportunities)
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