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The massive number of pavements and buildings coupled with the limited inspection resources, both monetary and human, to detect distresses and recommend maintenance actions lead to rapid deterioration, decreased service life, lower level of service and increased community disruption. Therefore, this paper aims at providing a state-of-the-art review of the literature with respect to deep learning techniques for detecting distress in both pavements and buildings; research advancements per asset/structure type; and future recommendations in deep learning applications for distress detection.

A critical analysis was conducted on 181 papers of deep learning-based cracks detection. A structured analysis was adopted so that major articles were analyzed according to their focus of study, used methods, findings and limitations.

The utilization of deep learning to detect pavement cracks is advanced compared to assess and evaluate the structural health of buildings. There is a need for studies that compare different convolutional neural network models to foster the development of an integrated solution that considers the data collection method. Further research is required to examine the setup, implementation and running costs, frequency of capturing data and deep learning tool. In conclusion, the future of applying deep learning algorithms in lieu of manual inspection for detecting distresses has shown promising results.

The availability of previous research and the required improvements in the proposed computational tools and models (e.g. artificial intelligence, deep learning, etc.) are triggering researchers and practitioners to enhance the distresses’ inspection process and make better use of their limited resources.

A critical and structured analysis of deep learning-based crack detection for pavement and buildings is conducted for the first time to enable novice researchers to highlight the knowledge gap in each article, as well as building a knowledge base from the findings of other research to support developing future workable solutions.

This paper aims to realize a fully distributed multi-UAV collision detection and avoidance based on deep reinforcement learning (DRL). To deal with the problem of low sample efficiency in DRL and speed up the training. To improve the applicability and reliability of the DRL-based approach in multi-UAV control problems.

In this paper, a fully distributed collision detection and avoidance approach for multi-UAV based on DRL is proposed. A method that integrates human experience into policy training via a human experience-based adviser is proposed. The authors propose a hybrid control method which combines the learning-based policy with traditional model-based control. Extensive experiments including simulations, real flights and comparative experiments are conducted to evaluate the performance of the approach.

A fully distributed multi-UAV collision detection and avoidance method based on DRL is realized. The reward curve shows that the training process when integrating human experience is significantly accelerated and the mean episode reward is higher than the pure DRL method. The experimental results show that the DRL method with human experience integration has a significant improvement than the pure DRL method for multi-UAV collision detection and avoidance. Moreover, the safer flight brought by the hybrid control method has also been validated.

The fully distributed architecture is suitable for large-scale unmanned aerial vehicle (UAV) swarms and real applications. The DRL method with human experience integration has significantly accelerated the training compared to the pure DRL method. The proposed hybrid control strategy makes up for the shortcomings of two-dimensional light detection and ranging and other puzzles in applications.

The world is a volatile, uncertain, complex, and ambiguous (VUCA) environment (Carvan, 2015) that calls for leaders who can effectively navigate the complexity of leadership today. Students of leadership studies must not only learn leadership information content, but also be able to effectively implement the content and process, requiring deep approaches to their learning (Petrie, 2014). This quantitative research study used the ASSIST Inventory to measure approaches to learning (surface, deep, or strategic) for students enrolled in an Organizational Leadership undergraduate program. Students showed a preference for deeper approaches, though, many continue to use surface approaches, which may lead to shallow understandings and the inability to put content into practice. Specific strategies are provided for instructors to help students move toward deeper approaches.

Decision-making in human beings is affected by emotions and sentiments. The affective computing takes this into account, intending to tailor decision support to the emotional states of people. However, the representation and classification of emotions is a very challenging task. The study used customized methods of deep learning models to aid in the accurate classification of emotions and sentiments.

The present study presents affective computing model using both text and image data. The text-based affective computing was conducted on four standard datasets using three deep learning customized models, namely LSTM, GRU and CNN. The study used four variants of deep learning including the LSTM model, LSTM model with GloVe embeddings, Bi-directional LSTM model and LSTM model with attention layer.

The result suggests that the proposed method outperforms the earlier methods. For image-based affective computing, the data was extracted from Instagram, and Facial emotion recognition was carried out using three deep learning models, namely CNN, transfer learning with VGG-19 model and transfer learning with ResNet-18 model. The results suggest that the proposed methods for both text and image can be used for affective computing and aid in decision-making.

The study used deep learning for affective computing. Earlier studies have used machine learning algorithms for affective computing. However, the present study uses deep learning for affective computing.

The digital construction transformation requires using emerging digital technology such as deep learning to automate implementing tasks. Therefore, this paper aims to evaluate the current state of using deep learning in the construction management tasks to enable researchers to determine the capabilities of current solutions, as well as finding research gaps to carry out more research to bridge revealed knowledge and practice gaps.

The scientometric analysis is conducted for 181 articles to assess the density of publications in different topics of deep learning-based construction management applications. After that, a thematic and gap analysis are conducted to analyze contributions and limitations of key published articles in each area of application.

The scientometric analysis indicates that there are four main applications of deep learning in construction management, namely, automating progress monitoring, automating safety warning for workers, managing construction equipment, integrating Internet of things with deep learning to automatically collect data from the site. The thematic and gap analysis refers to many successful cases of using deep learning in automating site management tasks; however, more validations are recommended to test developed solutions, as well as additional research is required to consider practitioners and workers perspectives to implement existing applications in their daily tasks.

This paper enables researchers to directly find the research gaps in the existing solutions and develop more workable applications to bridge revealed gaps. Accordingly, this will be reflected on speeding the digital construction transformation, which is a strategy over the world.

To the best of the authors’ knowledge, this paper is the first of its kind to adopt a structured technique to assess deep learning-based construction site management applications to enable researcher/practitioners to either adopting these applications in their projects or conducting further research to extend existing solutions and bridging revealed knowledge gaps.

A core challenge for leaders for deeper learning is scaling promising practices to provide students with systematic access to deeper learning experiences. This case illuminates how a group of researchers organized professional learning activities around conferring, a promising deeper learning practice.

The author examines how the leaders of a Networked Improvement Community (NIC) created the conditions for teachers to share their deeper learning practices through a case study. The case study centers on one school team’s learning through their participation in the NIC activities, as evidenced by the artifacts they created and their exchanges with their team, participants from other schools and researchers.

The trajectory of one team through three NIC activities – a video club, a pitch and user testing – shows how they examined their own conferring practice, got ideas for change and shifted their thinking and practice toward a more student-centered approach. Insights from the case suggest three design principles – a common problem of practice, shared representations of practice and intentional network configurations – for deeper professional learning, or learning experiences that engage educators in purposeful and collaborative inquiry into deeper learning practices.

Two limitations of the case are a lack of data on the perceived experience of participants, which could speak to the depth of Irving’s shift toward student-centered conferring, and the narrow time scope of the NIC, which limits exploration of the sustainability of the changes to conferring.

The design principles represent important features for researchers and leaders to consider in ongoing efforts to scale deeper learning. Leaders might use the principles to examine existing or future professional learning efforts.

This case study extends an understanding of one facet of leadership for deeper learning: fostering professional community. Future research is needed to examine the educator experience of participating in deeper professional learning and its sustained impact on practices.

This research aims to explore leadership approaches that foster deeper learning and facilitate the transition from traditional schooling to a model aligned with the demands of the post-industrial digital knowledge society.

Employing a mixed-methods approach, the authors conducted surveys among school principals within a network of schools embracing deeper learning based on ten distinct but interlocking criteria that define this particular model of deeper learning. Through in-depth follow-up interviews with school leaders, the authors investigated the factors and obstacles that support sustainable implementation and scalability of deeper learning, with a specific focus on the role of transformational leadership.

During the implementation of transformative practices like deeper learning, school leaders demonstrate diverse perspectives on the necessary changes for their successful integration. Leaders inclined toward a “transactional” leadership style concentrate on changes within individual classrooms. Conversely, leaders exemplifying “transformational leadership” possess a broader vision and address systemic factors such as teacher collaboration, assessment regulations and the effective utilization of time and space within schools. To achieve widespread adoption of deeper learning across schools and the education system, it is essential to recruit more transformational leaders for formal leadership positions and reorient leadership training toward transformational approaches.

The deeper learning model developed for this intervention encompasses a four-stage process: Teachers initially collaborate in small teams to co-design interdisciplinary, deeper learning units. The actual units consist of three sequences: knowledge acquisition, where students gain knowledge through direct instruction supplemented by personalized learning on digital platforms; team-based co-creative and co-constructive tasks facilitated by teachers once students have acquired a solid knowledge base and the completion of authentic tasks, products or performances in sequence III. While small groups of intrinsically motivated teachers have successfully implemented the model, achieving broader scalability and dissemination across schools requires significant “transformational leadership” to challenge traditional norms regarding teacher collaboration, assessment practices and the efficient use of time and space in schools.

This paper presents a structured model of deeper learning based on ten distinct but interlocking quality criteria tested within a network of 26 schools. The model has demonstrated transformative effects on participating schools, albeit primarily observed in smaller substructures of large secondary schools. Teachers who previously worked independently have begun to collaboratively design learning experiences, resulting in “hybrid” classrooms where physical and digital spaces merge and extend to include maker spaces and out-of-school learning environments. Traditional summative assessments have been replaced by various forms of embedded formative assessment. However, these innovations are currently driven by small groups of intrinsically motivated teachers. The research provides insights into the type of school leadership necessary for comprehensive scaling and system-wide dissemination of deeper learning.

This study aims to explore how educational leaders in South Korea adopted equity mindsets and how they organized changes to support students' deeper learning during COVID-19.

The authors developed a comprehensive framework of Equity Leadership for Deeper Learning, by revising the existing model of Darling-Hammond and Darling-Hammond (2022) and synthesizing equity leadership literature. Drawing upon this framework, this study analyzed data collected from individual interviews and a focus group with school and district administrators in the K-12 Korean education system.

The participants prioritized an equity stance of their leadership by critically understanding socio-political conditions, challenging unjust policies, and envisioning the big picture of equity-centered education. This led them to operationalize equity leadership in practice and create a more inclusive and supportive environment for student-centered deeper learning. District leaders established well-resourced systems by creating/developing instructional resources and making policies more useful. School leaders promoted quality teaching by strengthening access, developing student-centered curricula, and establishing individualized programs for more equitable deeper learning.

This study builds on scholarship of deeper learning and equity leadership by adding evidence from Korean educational leaders during COVID-19. First, the findings highlight the significance of leaders' equity mindsets in creating a safe and inclusive environment for deeper learning. This study further suggests that sharing an equity stance as a collective norm at the system level, spanning across districts and schools is important, which is instrumental to scale up innovation and reform initiatives. Second, this research also extends comparative, culturally informed perspectives to understand educational leadership. Most contemporary leadership theories originated from and are informed by Western and English-speaking contexts despite being widely applied to other contexts across the culture. This study's analysis underscores the importance of contextualizing leadership practices within the socio-historical contexts that influence how education systems are established and operate.

Leaders' adopting equity mindsets, utilizing bureaucratic resources in creative ways and implementing a school-wide quality curriculum are crucial to supporting students' deeper learning. District leaders can leverage existing vertical and horizontal networks to effectively communicate with teachers and local communities to establish well-recourced systems. As deeper learning is timeless and requires high levels of student engagement, school leaders' efforts to establish school-wide curricula is critical to facilitate deeper learning for students.

The study provides a nuanced understanding of how equity focused leaders responded to difficulties caused by the pandemic and strategized to support students' deeper learning. Existing studies tend to prioritize teacher effects on student learning, positing leadership effects as secondary or indirect. Alternatively, the authors argue that, without leadership supporting an inclusive environment, resourceful systems and student-centered school culture, deeper learning cannot be fully achieved in equitable ways.

This study aims to investigate the current state of research using deep learning methods for text classification in the tourism and hospitality field and to propose specific guidelines for future research.

This study undertakes a qualitative and critical review of studies that use deep learning methods for text classification in research fields of tourism and hospitality and computer science. The data was collected from the Web of Science database and included studies published until February 2022.

Findings show that current research has mainly focused on text feature classification, text rating classification and text sentiment classification. Most of the deep learning methods used are relatively old, proposed in the 20th century, including feed-forward neural networks and artificial neural networks, among others. Deep learning algorithms proposed in recent years in the field of computer science with better classification performance have not been introduced to tourism and hospitality for large-scale dissemination and use. In addition, most of the data the studies used were from publicly available rating data sets; only two studies manually annotated data collected from online tourism websites.

The applications of deep learning algorithms and data in the tourism and hospitality field are discussed, laying the foundation for future text mining research. The findings also hold implications for managers regarding the use of deep learning in tourism and hospitality. Researchers and practitioners can use methodological frameworks and recommendations proposed in this study to perform more effective classifications such as for quality assessment or service feature extraction purposes.

The paper provides an integrative review of research in text classification using deep learning methods in the tourism and hospitality field, points out newer deep learning methods that are suitable for classification and identifies how to develop different annotated data sets applicable to the field. Furthermore, foundations and directions for future text classification research are set.

This paper aims to utilize machine learning and soft computing to propose a new method of rough sets using deep learning architecture for many real-world applications.

The objective of this work is to propose a model for deep rough set theory that uses more than decision table and approximating these tables to a classification system, i.e. the paper propose a novel framework of deep learning based on multi-decision tables.

The paper tries to coordinate the local properties of individual decision table to provide an appropriate global decision from the system.

The rough set learning assumes the existence of a single decision table, whereas real-world decision problem implies several decisions with several different decision tables. The new proposed model can handle multi-decision tables.

The proposed classification model is implemented on social networks with preferred features which are freely distribute as social entities with accuracy around 91 per cent.

The deep learning using rough sets theory simulate the way of brain thinking and can solve the problem of existence of different information about same problem in different decision systems

This paper utilizes machine learning and soft computing to propose a new method of rough sets using deep learning architecture for many real-world applications.