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This systematic literature review aims to investigate the application and integration of Industry 4.0 (I4.0) technologies in transportation infrastructure construction projects focusing on sustainability pillars.

The study employs a systematic literature review approach, combining qualitative review and quantitative analysis of 142 academic articles published between 2011 and March 2023.

The findings reveal the dominance of Building Information Modelling (BIM) as a central tool for sustainability assessment, while other technologies such as blockchain and autonomous robotics have received limited attention. The adoption of I4.0 technologies, including Internet of Things (IoT) sensors, Augmented Reality (AR), and Big Data, has been prevalent for data-driven analyses, while Unmanned Aerial Vehicle (UAVs) and 3D printing are mainly being integrated either with BIM or in synergy with Artificial Intelligence (AI). We pinpoint critical challenges including high adoption costs, technical barriers, lack of interoperability, and the absence of standardized sustainability benchmarks.

This research distinguishes itself by not only mapping the current integration of I4.0 technologies but also by advocating for standardization and a synergistic human-technology collaborative approach. It offers tailored strategic pathways for diverse types of transportation infrastructure and different project phases, aiming to significantly enhance operational efficiency and sustainability. The study sets a new agenda for leveraging cutting-edge technologies to meet ambitious future sustainability and efficiency goals, making a compelling case for rethinking how these technologies are applied in the construction sector.

This paper presents a novel method for identifying damage in reinforced concrete (RC) bridges, utilizing macro-strain data from distributed long-gauge sensors installed on the concrete surface.

The method relies on the principle that heavy vehicles induce larger dynamic vibrations, leading to increased strain and crack formation compared to lighter vehicles. By comparing the absolute macro-strain ratio (AMSR) of a reference sensor with a network of distributed sensors, damage locations can be effectively pinpointed from a single data collection session. Finite-element modeling was employed to validate the method's efficacy, demonstrating that the AMSR ratio increases significantly in the presence of cracks. Experimental validation was conducted on a real-world bridge in Japan, confirming the method's reliability under normal traffic conditions.

This approach offers a practical and efficient means of detecting bridge damage, potentially enhancing the safety and longevity of infrastructure systems.

Original research paper.

The paper aims to analyze the structural integrity of an existing offshore platform located in the Northern Adriatic Sea, followed by the topside decommissioning and the re-utilization of the jacket as a wind turbine support. The structural integrity assessment against the in-place and the long-term actions is accomplished by using a reduced basis finite element method (RB-FEA) software program assessing the capability of the jacket to be used as a support for wind turbines at the end of its life cycle as oil and gas (O&G) platform.

The project starts by modeling the jacket, and subsequently, the structural analyses for the in-place loads in operative and extreme conditions are performed. Then, the fatigue analysis is carried out in order to define the cumulative damage necessary to evaluate the possibility to use the jacket as a wind turbine support.

The results show that the jacket, at the end of the service life as O&G platform, is able to withstand the loads produced by the installation of the wind turbine since the analyses are satisfied even with the conservative approach used which overestimates the thickness loss assuming a linear increasing value during the service life.

Because of the chosen approach, the study presents some limitations, especially concerning the real state of the platform which has been defined considering the thickness loss only. Additionally, a 1D model was used to perform the analyses, and hence, a 3D model could help in evaluating the critical points with higher precision.

The assessment of the structure could be improved by modeling a digital twin of the asset allowing a real-time monitoring which, however, involves a huge amount of data to be processed, so a suitable simulation technology must be used.

The RB-FEA proposed by Akselos is suitable to perform the analyses speeding up the processing of the data even in real time.

Passive chipless RFID (radio frequency identification) sensors, devoid of batteries or wires for data transmission to a signal reader, demonstrate stability in severe conditions. Consequently, employing these sensors for metal crack detection ensures ease of deployment, longevity and reusability. This study aims to introduce a chipless RFID sensor design tailored for detecting metal cracks, emphasizing tag reusability and prolonged service life.

The passive RFID sensor is affixed to the surface of the aluminum plate under examination, positioned over the metal cracks. These cracks alter the electrical length of the sensor, thereby influencing its amplitude-frequency characteristics. Hence, the amplitude-frequency profile generated by various metal cracks can effectively ascertain the occurrence and orientation of the cracks.

Simulation and experimental results show that the proposed crack sensing tag produces different frequency amplitude changes for four directions of cracks and can recognize the crack direction. The sensor has a small size and simple structure, which makes it easy to deploy.

This research aims to deploy crack detection on metallic surfaces using passive chipless RFID sensors, analyze the amplitude-frequency characteristics of crack formation and distinguish cracks of varying widths and orientations. The designed sensor boasts a straightforward structural design, facilitating ease of deployment, and offers a degree of reusability.

Recent emerging information technologies like digital twin (DT) provide new concepts and transform information management processes in the architecture, engineering and construction (AEC) industry. Although numerous articles are pertinent to DT applications, existing research areas and potential future directions related to the state-of-the-art DT in project operation and maintenance (O&M) are yet to be studied. Therefore, this paper aims to review the state-of-the-art research on DT applications in project O&M.

The current review adopted four methodological steps, including literature search, literature selection, science mapping analysis and qualitative discussion to gain a deeper understanding of DT in project O&M. The impact and contribution of keywords and documents were examined from a total of 444 journal articles retrieved from the Scopus database.

Five mainstream research topics were identified, including (1) DT-based artificial intelligence technology for project O&M, (2) DT-enabled smart city and sustainability, (3) DT applications for project asset management, (4) Blockchain-integrated DT for project O&M and (5) DT for advanced project management. Subsequently, research gaps and future research directions were proposed.

This study intends to raise awareness of future research by summarizing the current DT development phases and their impact on DT implementation in project O&M among researchers and practitioners.

This article introduces SigBERT, a novel approach that fine-tunes bidirectional encoder representations from transformers (BERT) for the purpose of distinguishing between intact and impaired structures by analyzing vibration signals. Structural health monitoring (SHM) systems are crucial for identifying and locating damage in civil engineering structures. The proposed method aims to improve upon existing methods in terms of cost-effectiveness, accuracy and operational reliability.

SigBERT employs a fine-tuning process on the BERT model, leveraging its capabilities to effectively analyze time-series data from vibration signals to detect structural damage. This study compares SigBERT's performance with baseline models to demonstrate its superior accuracy and efficiency.

The experimental results, obtained through the Qatar University grandstand simulator, show that SigBERT outperforms existing models in terms of damage detection accuracy. The method is capable of handling environmental fluctuations and offers high reliability for non-destructive monitoring of structural health. The study mentions the quantifiable results of the study, such as achieving a 99% accuracy rate and an F-1 score of 0.99, to underline the effectiveness of the proposed model.

SigBERT presents a significant advancement in SHM by integrating deep learning with a robust transformer model. The method offers improved performance in both computational efficiency and diagnostic accuracy, making it suitable for real-world operational environments.

The process of performance measurement provides support to company management to achieve the objectives established in strategic planning. Through the definition of critical success factors and related key performance indicators, performance measurement verifies the gap between planned objectives and the results achieved, informing the responsible bodies to enable them to evaluate performance and, if necessary, implement improvement actions. Although many types of companies adopt performance measurement, this process is challenging when applied to national health services. This paper aims to identify the evolution of performance measurement and the critical success factors of national health services.

The authors conducted an explorative case study of a leading national health service to delineate the evolutionary path of performance measurement and the main critical success factors.

The results indicate a significant increase in the maturity of performance measurement of a national health service that has been motivated by international reforms and national regulations. This research highlights performance measurement features such as a balanced set of metrics, targets, and incentives linked to strategic objectives and regular and frequent performance reviews. Furthermore, it identifies the performance measurement model of a leading national health service.

The evolution of performance measurement and numerous critical success factors of national health services are described; the critical success factors cover a wide range of financial to operational aspects such as patient safety, organizational appropriateness, and clinical appropriateness.

The performance governance model, based on the use of performance information to improve governance, is particularly complex, considering the inter-institutional relationships and community involvement that it requires. In order to contribute to the scientific debate on this model, the paper aims to highlight what are the challenges in developing three variables of performance governance (operational performance measurement tools, governance integration and citizen involvement) in the public health context.

Based on the previous literature, we identified three variables that characterize the performance governance model. We then explored them through the case study methodology applied to the Apulia Regional Health System.

The analysis allows us to conclude that the healthcare system under consideration is still far from applying the performance governance model, but it highlights some factors that are preparatory to the same. Moreover, starting from the empirical evidence, some considerations arise regarding managerial and policy making implications for the variables investigated.

The paper contributes to a growing body of critical literature on performance governance contextualizing the analysis in a particularly relevant area of the public sector, namely public health. Furthermore, the research also identifies some potential risks of the performance governance model (rigidity in performance measurement tool, organization-objective-responsibility relationship, participatory illusion).

The main objective of the study is to address the lack of sustainability assessments of smart connected health systems in the academic literature by presenting an assessment model to determine the alignment of these systems with the 17 Sustainable Development Goals (SDGs) proposed in the 2030 Agenda.

An evaluation model based on decision analysis is proposed that includes three phases: alignment framework, information gathering and assessment. This model measures the alignment of the connected health system with each of the 17 SDGs, identifying the goals and criteria associated with each SDG that the system achieves to satisfy.

The analysis reveals that the system has achieved more than 24% of the targets among the 17 SDGs. In addition, it identifies four sustainability challenges that the system potentially addresses in relation to the SDGs, providing valuable guidance for researchers and practitioners interested in sustainable health technology development.

The study's results have significant implications for policymakers and stakeholders in the health and technology sectors.

The originality of this study lies in its comprehensive approach to assessing the sustainability of connected health systems in the context of the SDGs, filling an important gap in the existing literature.

Effective teamwork is crucial for patient safety in healthcare. The TeamSTEPPS Teamwork Perceptions Questionnaire (T-TPQ) is a widely used tool for assessing teamwork perceptions. The T-TPQ has been adapted and validated for hospital setting use in several countries. This study aimed to translate and validate the T-TPQ into French for use among Tunisian healthcare professionals, enhancing teamwork assessment and patient safety initiatives.

A rigorous process ensured cultural and linguistic adaptation of the T-TPQ, including back-translation, expert panel review, and pilot testing. 459 healthcare professionals from four hospitals in Kairouan, Tunisia participated. Confirmatory factor analysis (CFA) compared the original five-factor structure with a revised structure based on exploratory factor analysis (EFA).

Both CFA models demonstrated good fit, with no significant difference between them (∆χ² = 22.51, p = 0.79). The original five-factor structure was retained due to its established theoretical foundation. The French T-TPQ exhibited strong internal consistency (α = 0.9). Two-way Random ICCs indicated fair to good test-retest reliability for all the five dimensions (0.633–0.848).

Several limitations should be acknowledged. The use of a questionnaire as a data collection tool is the source of a reporting bias, for fear of being identified or for reasons of “social desirability”. Nevertheless, this social desirability was minimal, as Baker et al. (2010) took steps to mitigate this during the instrument's development. Additionally, for assessing attitudes and perceptions, self-reported measures are deemed more effective, whereas objective measures are advocated for behavioral assessments. Furthermore, the participants were informed of the absence of good or bad answers, the importance of answering as closely as possible, and the confidentiality. Moreover, considering the data collection period, the COVID- 19 pandemic and its potential impact on recruitment, data collection, and participant responses. Although the sample size of 459 met the recommended criteria for conducting confirmatory factor analysis, as suggested by Bentler and Chou (1987) and (Floyd and Widaman, 1995), the COVID-19 pandemic presented challenges in recruitment. The increased workload and stress on healthcare professionals, coupled with staff redeployment and research restrictions within hospitals and care units, likely hindered achieving an even larger sample size. These circumstances also necessitated adjustments to data collection methods to ensure safety and adherence to pandemic protocols. This involved incorporating online surveys option with paper-based questionnaires and implementing stricter hygiene measures during in person data collection. Furthermore, the pandemic impacted the teamwork perceptions as significantly redefined the healthcare environment, placing immense pressure on professionals due to surging patient volumes, staff shortages, and the emotional burden of caring for critically ill individuals. This heightened stress and workload likely influenced teamwork dynamics, potentially fostering both positive adaptations, such as increased cohesion and support, as well as negative consequences like communication breakdowns and decreased morale (Terregino et al., 2023).

We outline significant practical implications for leaders in health care for improving teamwork and patient safety. Or, healthcare leaders can significantly enhance teamwork and patient safety by incorporating the validated French T-TPQ into their improvement strategies. This reliable tool enables the assessment of staff perceptions regarding teamwork strengths and weaknesses, specifically in areas like communication and leadership. By identifying these crucial areas, leaders can implement targeted training programs and interventions. In fact, the existing body of research consistently demonstrates the positive impact of team training interventions, on both teamwork processes and patient outcomes. These interventions have been shown to enhance teamwork skills (Baker et al., 2010; Thomas and Galla, 2013; Weaver et al., 2014). In areas such as communication, leadership, situation monitoring, and mutual support, leading to decreased mortality and morbidity rates (Weaver et al., 2014). Implementing team training programs fosters trust and collaboration around shared goals, contributing to a more effective and safer healthcare environment for both patients and professionals. Additionally, the culturally adapted T-TPQ not only benefits individual healthcare settings but also unlocks opportunities for broader research and collaboration on a global scale. By enabling cross-cultural comparisons and benchmarking, the T-TPQ can deepen our understanding of how teamwork dynamics vary across diverse healthcare environments and cultural contexts. This knowledge is invaluable for tailoring teamwork interventions and training programs to specific populations and settings, ensuring their effectiveness and cultural relevance. Moreover, integrating teamwork training into continuing professional development, interprofessional and medical education initiatives is crucial for cultivating collaborative competencies and building high-performing healthcare teams. Research has shown that interprofessional teamwork experiences significantly enhance collaborative competencies among nursing and medical students, emphasizing the importance of incorporating teamwork training early in healthcare education. This approach equips future healthcare professionals with the necessary skills to navigate complex team environments, ultimately improving patient care quality and mitigating workload issues that contribute to burnout (Simin et al., 2010; Ceylan, 2017; Fox et al., 2018).

The French version of the T-TPQ was semantically equivalent and culturally relevant with adequate test-retest reliability as compared to the English version, expanding its applicability and contributing to understanding teamwork perceptions in this context. The French T-TPQ offers a valuable tool for assessing teamwork, identifying areas for improvement, and implementing interventions to enhance teamwork and patient safety in Tunisia and potentially other French-speaking regions.