The notions of smart construction and smart or digital cities include many modern concepts that are advocated today, especially in countries with advanced economies, and depend on using information technology and the Internet of Things as a basis to automate processes and activate digital systems to manage activities and services related to the operation of buildings and urban structures. In light of the spread of digital technology and modern managerial approaches, the concept of a digital twin is being used on a large scale with the current trend and direction to digitalize activities providing many economic, social and technical advantages. A digital twin is a system in which a virtual representation of a real entity or physical system is used continuously by being fed with data and deriving outputs in the form of decisions and actions that are generated through the processes of machine learning, simulation, development and lifecycle management. This study aims to review the literature on construction project management through the lens of digital twins and ways to use them in the field to improve operational results. The authors propose a framework for analyzing and supervising the development of digital twins that uses three main stages: the commonly encountered Building Information Modeling (BIM); the existing monitoring and actuation digital twins; and an envisioned third stage that makes use of artificial intelligence, complex visualization instruments and advanced controls with the capability to exact change within a construction project on the building site.

The digital twin solution is an industry 4.0 specific tool that has grown in the past decade, stemming from the modelling and simulation approaches that existed before, complemented by new sensor capabilities, cloud processing, big data analytics, and implementation mechanisms. As it is being used mostly in the present by manufacturing companies, the primary focus of the solution is to enhance productivity and reduce costs by optimizing processes and enabling real-time problem-solving, sometimes based on decision-making systems and artificial intelligence. However, as companies are being faced with an increasingly steep list of environmental requirements and regulations, ranging from the classical pollution control and waste recycling to full-scale economic models based on circular economy and transformative carbon dioxide elimination programs, the features of the manufacturing digital twins must also evolve to provide an appropriate answer to these challenges. In this paper, the authors propose a framework for building better digital twins for production systems by incorporating environmental-related functions. The demarches start from analysing existing solutions presented in literature from the point of view of environmental suitability, based on the use of the MoSCoW method for differentiating attributes (into Must have, Should have, Could have, Will not have elements) and determining development alternatives based on the employment of Multi-Criteria Decision Analysis (MCDA) for feature selection, and the TRIZ method (Theory of Inventive Problem-Solving) for application guidelines. The MCDA was performed within a focus group of nine production specialists from regionally successful sectors. We arrive at the conclusion that environmental-related functions are poorly implemented in the digital twins of the present (although more so in integrated solutions and custom-built applications) and that the development of the proper tools, databases, and interpretation keys should proceed immediately in the fields of production engineering, industrial ecology, and software development to support them.

This research aims to develop an integrated approach to construction project management by integrating digital technology into monitoring and surveillance operations. Through the use of drones and image processing software, data can be updated regularly and accurately about the progress at the construction site, allowing managers and decision makers to have a clear view of the current situation and make effective decisions based on accurate. In addition, this approach contributes to improving communication and coordination among project team members, as data and images can be easily and effectively shared, reducing opportunities for error and enhancing effective interaction among different parties. Using digital twin technologies, planning and forecasting processes can also be improved, as comprehensive analysis of digital data provides a deeper understanding of project dynamics, identifies potential risks, and enables appropriate preventive measures to be taken. In conclusion, the integration of digital twins and the use of drones in construction projects represent a significant step towards achieving smarter and more efficient management, and successfully achieving the defined goals with greater effectiveness.

This study aims to analyze and discuss the risks facing construction projects by reviewing some of the processes and procedures that address risks through the use of the digital twin technology. The paper studies generic risks and their treatment, and it develops a proposal for risk management systematization using the Digital Twin for Construction Projects approach, previously developed by the authors. It addresses how to classify risks so that the digital system is fed with the proper information and data, which is based on processing and analysis, to reach understandable decisions and overcome anomalies. The research reached a set of results, the most prominent of which is that the digital twin can be used to enhance risk management in construction projects through adapted techniques such as the ones proposed in the paper; namely, a risk treatment procedure and a custom risk matrix. In addition, risk management treated according to a digital approach helps to improve the prediction capabilities, and this helps human decision-makers to avoid potential unplanned costs and failures, and to maximize efficiency. The study also recommends new investigations in the field of safeguarding shared information and data to protect from intentional and accidental mismanagement in order to reach a comprehensive digital system.

Daily life in today’s dynamic world requires fast adaptation of people’s behavior to new challenges emerged from environmental, health, urban housing, transportation, etc. problems. A strong and complex tool in changing behaviors, education consumes significant resources and requires time for effective impact. The present study proposes a roadmap, integrating advanced methods for industrial competitive development (QFD) in configuring efficient educational interventions for changing people’s behavior on special thematic areas, such as environment and recycling. A literature review on applicable behavioral theories led to more than 30 behavioral factors (constructs) targetable by education, their specific impact evaluation being the subject of a survey among education professionals. Finally, to reach a mapping of efficient interventions with low costs, high impact and fast results criteria, educational interventions and behavioral factors were correlated by the QFD method on three dimensions addressing: teachers and trainers, contents and tools, respectively the national & institutional level. The current research results are focused particularly on changing recycling behaviors, but the overall concept and methodology can be expanded to incorporate any preoccupation in the areas of environment and health, as long-term drivers of public well-being.

This paper addresses the degree of readiness of manufacturing companies in the well-defined area of Transylvania, Romania for tackling the challenges of the low-carbon economy (LCE) in view of the ambitious goals of the European Union. The presented survey aims to provide a better understanding about the management and reduction in the carbon footprint among production companies in Romania, as this sector is not usually included official strategies or studies. One hundred and three companies, selected based on voluntary sampling responses, were investigated using a 40-item questionnaire. The survey was applied to the manufacturing sector, including automotive, furniture production, and metal processing domains, which are locally representative and a good approximation of what small- and medium-sized firms look like across the EU, thus furnishing a good view of what takes place in other countries as well. The results obtained were analyzed using univariate descriptive statistics, multi-variate item analysis, and hypothesis testing to arrive at conclusions with a high degree of applicability. The purpose was to obtain an accurate overview about the actual situation and help companies find solutions in order to reduce the carbon footprint in the production field and achieve sustainable manufacturing. We arrive at the conclusion that manufacturing firms have a high degree of interest in decarbonization, but understand the efforts required to the same degree they understand the opportunities it brings. For example, 55% of respondents are interested in the benefits of LCE, while 90% of them observe at least one environmental standard, and ca. 70% implement at least common decarbonization measures, such as electricity savings or process optimization. While raising awareness and improving technological processes are accepted and embraced, other issues (i.e., involvement in RDI and CSR, change management, as well as financing investment efforts) should be addressed by proper policies.