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Data-Driven Project Planning and Control: Advancing Cost, Schedule, and Risk...
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Data-Driven Project Planning and Control: Advancing Cost, Schedule, and Risk Management

A special issue of Buildings (ISSN 2075-5309). This special issue belongs to the section "Construction Management, and Computers & Digitization".

Deadline for manuscript submissions: 31 July 2025 | Viewed by 2079

Special Issue Editors

Dr. Giulio Mangano

E-Mail Website
Guest Editor
Department of Management and Production Engineering, Politecnico di Torino, 10129 Torino, Italy
Interests: construction project management; public-private partnership; facilities management; digital twins
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Alberto De Marco

E-Mail Website
Guest Editor
Department of Management, Politecnico di Torino, 10129 Torino, Italy
Interests: construction project management; public-private partnership; facilities management; cost/quality control
Special Issues, Collections and Topics in MDPI journals
Dr. Filippo Maria Ottaviani

E-Mail Website
Guest Editor
Department of Management and Production Engineering, Politecnico di Torino, 10129 Torino, Italy
Interests: construction/project management; cost/quality control; artificial intelligence; machine and deep learning; neural networks; mathematical modeling

Special Issue Information

Dear Colleagues,

The complexity of construction management is increasing due to the uncertainties inherent in modern project environments. Effective project planning and control are essential to optimize decision-making and achieve project success. These processes involve not only managing costs, durations, and risks but also addressing additional factors such as safety, energy efficiency, sustainability, and social impacts.

This Special Issue focuses on the application of data-driven technologies and techniques that leverage both qualitative and quantitative data, including the following:

  • Regression analysis;
  • Bayesian inference;
  • Fuzzy logic;
  • Monte Carlo simulation;
  • Time series analysis;
  • Artificial intelligence (e.g., machine learning and deep learning);
  • Probabilistic and network models;
  • Natural language processing and text mining;
  • Building information modeling;
  • Blockchain;
  • Internet of Things.

Potential applications include the following:

  • Cost and duration forecasting;
  • Scheduling optimization;
  • Risk identification, evaluation, and mitigation;
  • Methods for integrating uncertainty into project decisions;
  • Advanced techniques for enhancing resource allocation;
  • Safety monitoring and improvement;
  • Ensuring regulatory compliance;
  • Energy applications (e.g., renewable energy integration and efficiency optimization);
  • Sustainability-focused solutions (e.g., carbon footprint, waste management and water usage);
  • Social impact analysis;
  • Smart infrastructure planning to support resilient and adaptive systems.

We welcome submissions of technical reports, literature reviews, case studies, empirical research, conceptual papers, and position papers that explore innovative, data-driven approaches to improving project planning and control. This Special Issue aims to bridge theory and practice, contributing to enhanced outcomes in construction project management.

Dr. Giulio Mangano
Prof. Dr. Alberto De Marco
Dr. Filippo Maria Ottaviani
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 100 words) can be sent to the Editorial Office for announcement on this website.

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. Buildings 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 2600 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

  • regression analysis
  • Bayesian inference
  • fuzzy logic
  • Monte Carlo simulation
  • time series analysis
  • artificial intelligence
  • probabilistic and network models
  • natural language processing and text mining
  • building information modeling
  • blockchain
  • Internet of Things

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Further information on MDPI's Special Issue policies can be found here.

Published Papers (3 papers)

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Research

17 pages, 2847 KiB  
Article
An Alternative Representation of Project Activity Networks: Activity on Arcs and Nodes (AoAaN)
by Fernando Grande-González, Pablo Ballesteros-Pérez, Maria Carmen González-Cruz and Gunnar Lucko
Buildings 2025, 15(8), 1358; https://doi.org/10.3390/buildings15081358 - 19 Apr 2025
Viewed by 358
Abstract
Activity-on-arc (AoA) and activity-on-node (AoN) project network representations have been used in construction scheduling for many decades. But due to the primary information that they emphasize—the activities themselves in the AoA graphs, and the precedence relationship structure in the AoN graphs—they also have [...] Read more.
Activity-on-arc (AoA) and activity-on-node (AoN) project network representations have been used in construction scheduling for many decades. But due to the primary information that they emphasize—the activities themselves in the AoA graphs, and the precedence relationship structure in the AoN graphs—they also have significant limitations. In this paper, we develop a hybrid representation approach named Activity-on-Arcs-and-Nodes (AoAaN). This novel network representation transforms all project activities into arcs (as in the AoA representation) but retains all precedence relationships between activities (as in AoN). To develop this alternative network representation, first, we establish its theoretical drawing principles, which mostly involve how to deal with different precedence relationship types (FS, SS, SF, FF) in basic networks. Then, we proceed with the calculation and analysis of more realistic project examples with a larger number of activities. Advantages of the new AoAaN is that it allows a simpler and more fine-grained determination of the critical path, while facilitating computer calculation via a Dependency Structure Matrix (DSM) that purely contains numerical information. Additionally, the proposed AoAaN allows handling coupled (interdependent) activities, a type of relationship that had previously hampered analysis of networks. Due to its flexible modeling capabilities and calculation simplicity, we suggest the AoAaN representation be added to project management courses as well as be used by project schedulers as a more capable alternative to the traditional AoA and AoN representations. Full article
(This article belongs to the Special Issue Data-Driven Project Planning and Control: Advancing Cost, Schedule, and Risk Management)
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22 pages, 1347 KiB  
Article
The Impact of Using Information Systems on Project Management Success Through the Mediator Variable of Project Risk Management: Results from Construction Companies
by Noor Shaheed Sachit Taresh, Mahboobeh Golestanizadeh, Hadi Sarvari and David J. Edwards
Buildings 2025, 15(8), 1260; https://doi.org/10.3390/buildings15081260 - 11 Apr 2025
Viewed by 648
Abstract
Construction projects in developing countries indicate many implementation problems, such as the technical incompatibility of the implemented structure with the design, incorrect management, the prolongation of a very high percentage of projects up to several times of the planned period, and the increase [...] Read more.
Construction projects in developing countries indicate many implementation problems, such as the technical incompatibility of the implemented structure with the design, incorrect management, the prolongation of a very high percentage of projects up to several times of the planned period, and the increase in costs; it is vital for construction firms to gather, integrate, and communicate the results of project management procedures using tools and methods, including information systems, in order to reduce these problems. Evaluating the results of project management procedures, using tools and methods such as information systems, can be helpful to avoid implementation problems, technical incompatibility of the constructed structure with the design, improper management, delays, and cost overruns. Hence, this study aims to evaluate the influence of information systems on project management success through the mediator variable of project risk management in construction firms. To accomplish this, 95 Iraqi building specialists were picked as a statistical sample using snowball sampling. Three questionnaires were used as data collection tools including an information systems questionnaire with four dimensions and 27 questions, a project management success questionnaire with 27 questions, and a project risk management questionnaire with six dimensions and 25 questions based on a five-point Likert scale measurement. The validity and reliability of the questionnaires were checked and confirmed. Smart PLS 4 and SPSS 28 softwares were used for analyzing the data. Finally, the findings indicated that the impact effect as well as the full effect of information system variables on project management success without the presence of a mediator is significant. Moreover, the indirect effect of information system variables on project management success with the presence of a mediator is also significant. In addition, project risk management has a partial mediator effect on the effect of information system variables on project management success. Also, there is a considerable correlation between the use of information systems and the success of project and risk management. Moreover, in the first phase of stepwise regression, capacity development predicts project management success and risk management variables. The regression analysis revealed that among the dimensions of information systems, the Capacity Development dimension has the ability to predict the success of project management and project risk management. Full article
(This article belongs to the Special Issue Data-Driven Project Planning and Control: Advancing Cost, Schedule, and Risk Management)
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29 pages, 3827 KiB  
Article
A Systematic Multi-Criteria Quantitative Model for Evaluating the Change Order Impact on Contractors’ Cash Flow
by Altayeb Mohd Jamil Qasem
Buildings 2025, 15(8), 1246; https://doi.org/10.3390/buildings15081246 - 10 Apr 2025
Viewed by 554
Abstract
Construction projects in Saudi Arabia are frequently plagued by cost overruns and time delays, with change orders being a major contributing factor. These modifications, which can occur at various stages throughout the project lifecycle, tend to negatively impact costs and cash flows. This [...] Read more.
Construction projects in Saudi Arabia are frequently plagued by cost overruns and time delays, with change orders being a major contributing factor. These modifications, which can occur at various stages throughout the project lifecycle, tend to negatively impact costs and cash flows. This study developed a classification impact index model to assess the potential impact of change orders on contractors’ cash flow in building construction projects. This study identified the factors and subfactors affecting contractors’ cash flow, organizing them into a hierarchical structure of main and sub-main factors. The relative importance of these factors was then assessed using the Analytical Hierarchical Process (AHP). The findings showed that project financing schemes had the most substantial impact (44%), followed by contract type (30%) and characteristics of change orders (26%). Among the subfactors, cash availability under project financing had the highest influence at 56%, while the value of change orders was the most significant subfactor under change order characteristics, contributing 30%. The Multi-Attribute Utility Theory (MAUT) was subsequently used to evaluate the attributes of these factors, applying utility scores to provide a more comprehensive assessment. The resulting Change Order Impact Index integrates AHP and MAUT to quantify the impacts of change orders. This model categorizes the effects of change orders into four levels: minor, moderate, significant, and severe. The developed Change Order Impact Index provides a systematic approach for proactively managing financial risks, optimizing project financing decisions, and supporting contractors in mitigating the impacts of change orders to maintain financial stability and reduce negative impacts on their cash flow. Full article
(This article belongs to the Special Issue Data-Driven Project Planning and Control: Advancing Cost, Schedule, and Risk Management)
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