Purpose The past few decades have produced a number of investigations into the correlation between project managers’ competencies and project success. As a result, competencies lists have become extensive “shopping lists.” The purpose of this paper is to define the most important competencies to project success and investigate their correlations. Design/methodology/approach The authors surveyed project managers on the importance of 28 project manager competencies to project success. Data were analyzed using univariate and multivariate procedures. Findings Data show that communication, commitment and leadership appear as the three most relevant aspects. Multivariate analysis identified seven groups of competencies: leadership, self-management, interpersonal, communication, technical, productivity and managerial. Practical implications The results confirm a growing trend toward soft skills and reinforce the need for an update on project management education to fill the gap between theory and practice. Originality/value Project manager competencies lists have become too extensive, and the field is in constant change; therefore, this study updates the discussion and downsizes the number of competencies to fewer, more relevant items.

Since the concept of smart cities was introduced, there has been a growing number of surveys aiming to identify the dimensions that characterize them. However, there is still no consensus on the main factors that should be considered to make a city more intelligent and sustainable. This report contributes to the topic by identifying the most important smart city drivers from the perspective of professionals from four broad areas of expertise: applied social sciences, engineering, exact and Earth sciences, and human sciences, which provide important insights for the understanding of smart and sustainable cities. In this study, we conducted a wide and detailed literature review, in which 20 potential smart city drivers were identified. The drivers were prioritized from the results of a survey conducted with 807 professionals that work in the concerned field. The results showed that the seven drivers identified as the most important to increase the intelligence of cities are related to the governance of cities.

The regulation of urban property use is a fundamental instrument for the development of cities. However, most of the norms that set general guidelines for urban policy predate the transformations that the smart city concept has brought about in the way cities are appropriated and perceived by society, and even today, studies on how these regulations collaborate to make cities smarter and more sustainable. This work contributes to filling this gap by investigating the main guidelines of the Brazilian City Statute that have the greatest potential to contribute to having smarter and more sustainable Brazilian cities. To prioritize the sixteen guidelines of the City Statute, the methodology used consisted of a survey carried out with professionals working in the concerned field. The results show that the sixteen guidelines were evaluated as important for increasing the intelligence of cities, of which five were evaluated as having the most priority, these five were related to the governance of cities. Considering the scarcity of resources in Brazilian cities, these five guidelines contribute so that municipal governments can direct their efforts towards what has the most priority.

Since the 1980s, smart buildings have aroused the interest of researchers. However, there is still no consensus on what the intelligence of a building is, and what enhances that intelligence. The purpose of this paper is to identify and correlate the main drivers and systems of smart buildings, by associating them with the main beneficiaries: users, owners, and the environment. To identify the main drivers and systems of these buildings, we carried out a comprehensive, detailed, and interpretative literature search. From the selected articles, we sorted the information, extracted the main concepts and knowledge, and, finally, identified the set of potential drivers and systems. Results showed eleven drivers and eight systems, and these can be enhanced by more than one driver. By analyzing the main beneficiaries, we grouped the drivers into three categories: users, owners, and the environment. Given the lack of consensus on the key drivers that make buildings smarter, this article contributes to filling this gap by identifying them, together with the key systems. It is also relevant for detecting the relationships between drivers and systems, and pointing out which drivers have the greatest potential to affect a particular system, keeping in mind the main beneficiary.

Annually, millions of dollars are spent to carry out defect detection in key infrastructure including roads, bridges, and buildings. The aftermath of natural disasters like floods and earthquakes leads to severe damage to the urban infrastructure. Maintenance operations that follow for the damaged infrastructure often involve a visual inspection and assessment of their state to ensure their functional and physical integrity. Such damage may appear in the form of minor or major cracks, which gradually spread, leading to ultimate collapse or destruction of the structure. Crack detection is a very laborious task if performed via manual visual inspection. Many infrastructure elements need to be checked regularly and it is therefore not feasible as it will require significant human resources. This may also result in cases where cracks go undetected. A need, therefore, exists for performing automatic defect detection in infrastructure to ensure its effectiveness and reliability. Using image processing techniques, the captured or scanned images of the infrastructure parts can be analyzed to identify any possible defects. Apart from image processing, machine learning methods are being increasingly applied to ensure better performance outcomes and robustness in crack detection. This paper provides a review of image-based crack detection techniques which implement image processing and/or machine learning. A total of 30 research articles have been collected for the review which is published in top tier journals and conferences in the past decade. A comprehensive analysis and comparison of these methods are performed to highlight the most promising automated approaches for crack detection.

No abstract

No abstract

The environmental damage arising from the construction and engineering services was responsible for the appearance of several norms and resolutions regulating and directing the sector's performance. In this article, we research how professionals with experience in public bids assess the difficulty degree of the implementation of those requirements and how they assess the environmental legislation regarding the protection and conservation of the environment, impact on costs, deadlines and the solution to environmental problems. The results show that industry professionals consider as "high" the level of difficulty to implement the addressed sustainability requirements, and that the Brazilian environmental legislation does not comply with its environmental protection role, increases the possibility of delays and costs of projects and services and hampers the emergence of solutions that could solve environmental problems.