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Sustainable Construction Management Operations and Construction Engineering

A special issue of Sustainability (ISSN 2071-1050). This special issue belongs to the section "Sustainable Engineering and Science".

Deadline for manuscript submissions: 31 July 2026 | Viewed by 561

Special Issue Editor


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Guest Editor
Department of Economics, Science, Engineering and Design, University of the Republic of San Marino, Via Consiglio dei Sessanta 99, 47891 Dogana, San Marino
Interests: steel; bridges; shear lag; robustness and resilience; stability; fatigue; welded and bolted joints; durability
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Special Issue Information

Dear Colleagues,

In construction engineering, we can identify well-defined phases that, if correctly studied, contribute to the optimization of an executive project. The optimization of the design phase, the planning of production in the factory of macro-elements that are as standardized and pre-assembled, and the organization of the yard are key for the success of works management operations and are subject to careful planning and strategic purchasing of the material and its possible reuse. The operational and management controls in the production chain are also key, such as the design and planning of the various phases used to obtain a final benefit in technical–economic terms. The concept of sustainability also fits into this context, considering all aspects that, in some way, have an impact on the environment and its resources. In the case of a construction, these aspects affect different phases, ranging from the raw material to the final installation, including the traceability of the raw material, in any form.

In this Special Issue, original research articles and reviews are welcome. Research areas may include (but not limited to) the following:

  • Design strategies that impact the operational implementation of a construction;
  • New materials or innovative uses of materials that can be used in construction;
  • Design optimization analysis of construction and assembly phases;
  • Development of production lines that allow savings in the use of key materials and resources linked;
  • Simplified construction and site management;
  • Improvement of the resources on site;
  • Use of new control methodologies in the operational management of construction;
  • Traceability and reuse of materials;
  • Efficiency and structural monitoring.

I look forward to receiving your contributions.

Dr. Emanuele Maiorana
Guest Editor

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-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Sustainability 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 2400 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

  • design optimization
  • management and planning
  • saving resources
  • alternative solutions
  • monitoring
  • new materials
  • mass production
  • recycling
  • traceability
  • efficiency

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Published Papers (1 paper)

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Research

32 pages, 1895 KB  
Article
A Hybrid AI-Stochastic Framework for Predicting Dynamic Labor Productivity in Sustainable Repetitive Construction Activities
by Naif Alsanabani, Khalid Al-Gahtani, Ayman Altuwaim and Abdulrahman Bin Mahmoud
Sustainability 2025, 17(24), 11097; https://doi.org/10.3390/su172411097 - 11 Dec 2025
Viewed by 183
Abstract
Accurate real-time prediction of labor productivity is crucial for the successful management of construction projects. However, it remains a significant challenge due to the dynamic and uncertain nature of construction environments. Existing models, while valuable for planning and post-analysis, often rely on historical [...] Read more.
Accurate real-time prediction of labor productivity is crucial for the successful management of construction projects. However, it remains a significant challenge due to the dynamic and uncertain nature of construction environments. Existing models, while valuable for planning and post-analysis, often rely on historical data and static assumptions, rendering them inadequate for providing actionable, real-time insights during construction. This study addresses this gap by suggesting a novel hybrid AI-stochastic framework that integrates a Long Short-Term Memory (LSTM) network with Markov Chain modeling for dynamic productivity forecasting in repetitive construction activities. The LSTM component captures complex, long-term temporal dependencies in productivity data, while the Markov Chain models probabilistic state transitions (Low, Medium, High productivity) to account for inherent volatility and uncertainty. A key innovation is the use of a Bayesian-adjusted Transition Probability Matrix (TPM) to mitigate the “cold start” problem in new projects with limited initial data. The framework was rigorously validated across four distinct case studies, demonstrating robust performance with Mean Absolute Percentage Error (MAPE) values predominantly in the “Good” range (10–20%) for both the training and test datasets. A comprehensive sensitivity analysis further revealed the model’s stability under data perturbations, though performance varied with project characteristics. By enabling more efficient resource utilization and reducing project delays, the proposed framework contributes directly to sustainable construction practices. The model’s ability to provide accurate real-time predictions helps minimize material waste, reduce unnecessary labor costs, optimize equipment usage, and decrease the overall environmental impact of construction projects. Full article
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