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This paper aims to propose a new deep learning technique to detect the type of material to improve automated construction quality monitoring.

A new data augmentation approach that has improved the model robustness against different illumination conditions and overfitting is proposed. This study uses data augmentation at test time and adds outlier samples to training set to prevent over-fitted network training. For data augmentation at test time, five segments are extracted from each sample image and fed to the network. For these images, the network outputting average values is used as the final prediction. Then, the proposed approach is evaluated on multiple deep networks used as material classifiers. The fully connected layers are removed from the end of the networks, and only convolutional layers are retained.

The proposed method is evaluated on recognizing 11 types of building materials which include 1,231 images taken from several construction sites. Each image resolution is 4,000 × 3,000. The images are captured with different illumination and camera positions. Different illumination conditions lead to trained networks that are more robust against various environmental conditions. Using VGG16 model, an accuracy of 97.35% is achieved outperforming existing approaches.

It is believed that the proposed method presents a new and robust tool for detecting and classifying different material types. The automated detection of material will aid to monitor the quality and see whether the right type of material has been used in the project based on contract specifications. In addition, the proposed model can be used as a guideline for performing quality control (QC) in construction projects based on project quality plan. It can also be used as an input for automated progress monitoring because the material type detection will provide a critical input for object detection.

Several studies have been conducted to perform quality management, but there are some issues that need to be addressed. In most previous studies, a very limited number of material types were examined. In addition, although some studies have reported high accuracy to detect material types (Bunrit et al., 2020), their accuracy is dramatically reduced when they are used to detect materials with similar texture and color. In this research, the authors propose a new method to solve the mentioned shortcomings.

Digital learning systems are crucial for education and data collected can analyse students learning performances to improve support. The purpose of this study is to design and build an asynchronous hardware and software system that can store data on a local device until able to share. It was developed for staff and students at university who are using the limited internet access in areas such as remote Northern Territory. This system can asynchronously link the users’ devices and the central server at the university using unstable internet.

A Learning Box has been build based on minicomputer and a web learning management system (LMS). This study presents different options to create such a system and discusses various approaches for data syncing. The structure of the final setup is a Moodle (Modular Object Oriented Developmental Learning Environment) LMS on a Raspberry Pi which provides a Wi-Fi hotspot. The authors worked with lecturers from X University who work in remote Northern Territory regions to test this and provide feedback. This study also considered suitable data collection and techniques that can be used to analyse the available data to support learning analysis by the staff. This research focuses on building an asynchronous hardware and software system that can store data on a local device until able to share. It was developed for staff and students at university who are using the limited internet access in areas such as remote Northern Territory. This system can asynchronously link the users’ devices and the central server at the university using unstable internet. Digital learning systems are crucial for education, and data collected can analyse students learning performances to improve support.

The resultant system has been tested in various scenarios to ensure it is robust when students’ submissions are collected. Furthermore, issues around student familiarity and ability to use online systems have been considered due to early feedback.

Monitoring asynchronous collaborative learning systems through analytics can assist students learning in their own time. Learning Hubs can be easily set up and maintained using micro-computers now easily available. A phone interface is sufficient for learning when video and audio submissions are supported in the LMS.

This study shows digital learning can be implemented in an offline environment by using a Raspberry Pi as LMS server. Offline collaborative learning in remote communities can be achieved by applying asynchronized data syncing techniques. Also asynchronized data syncing can be reliably achieved by using change logs and incremental syncing technique.

Focus on audio and video submission allows engagement in higher education by students with lower literacy but higher practice skills. Curriculum that clearly supports the level of learning required for a job needs to be developed, and the assumption that literacy is part of the skilled job in the workplace needs to be removed.

To the best of the authors’ knowledge, this is the first remote asynchronous collaborative LMS environment that has been implemented. This provides the hardware and software for opportunities to share learning remotely. Material to support low literacy students is also included.

The purpose of this study is to explore the design of outdoor play spaces in Cairo that provide an enjoyable play experience, along with opportunities for enhancing child social and cognitive developmental skills through play features incorporated in their play spaces to achieve this goal.

The study adopts a qualitative methodology to examine the effectiveness of natural, customized and elevated features on social and cognitive play behaviors of 6–8 year-olds. Data were gathered in three different play settings; a play space inside a social club, a park and a schoolyard. Data gathering relied on observations, written descriptions of play patterns and recordings of children's conversations. Additionally, the researcher utilized sketching diagrams to illustrate children's preferences for play with each feature.

The results of the study indicate that incorporating natural, elevated and customized play features into children's play spaces can enhance their environment and provide opportunities for fostering their social and cognitive skills.

This study reports the occurrence of indicative behaviors and not the exact measurement of skill development. Research involving children can have limitations in terms of reliability of results due to slight variations affected by unmeasurable circumstances.

The study makes a valuable contribution towards enhancing the quality of children's play spaces in Cairo by emphasizing the significance of providing opportunities for social and cognitive in addition to physical play.

This paper presents a new conceptualization of digital service anchored in a coconstitutive ontology of digital “x” phenomena, illuminating the pivotal role of the digital qualifier in the service context. Our objective is to provide a theoretically grounded conceptualization of digital service and its impact on the nature of the value cocreation process that characterizes digital phenomena.

Drawing from scholarly works on digital phenomena and fundamental principles of service-dominant logic, this paper delineates the essence of digital service based on the interplay between digitization and digitalization as well as the operational dynamics of generativity and its constitutive dimensions (architecture, community, governance).

The paper defines digital service as a sociotechnical process of value cocreation, where participants dynamically architect, govern and leverage digital resources. This perspective highlights the organic development of digital service and the prevalence of decentralized control mechanisms. It also underscores how the intersection between generativity’s dimensions—architecture, community and governance—shapes the dynamic evolution and outcomes of digital services.

Our conceptual framework sheds light on our understanding of digital service, offering a foundation to further explore its nature and implications for research and practice, which we illustrate using the case of ChatGPT.

Monitoring of the quality of precast concrete (PC) components is crucial for the success of prefabricated construction projects. Currently, quality monitoring of PC components during the construction phase is predominantly done manually, resulting in low efficiency and hindering the progress of intelligent construction. This paper presents an intelligent inspection method for assessing the appearance quality of PC components, utilizing an enhanced you look only once (YOLO) model and multi-source data. The aim of this research is to achieve automated management of the appearance quality of precast components in the prefabricated construction process through digital means.

The paper begins by establishing an improved YOLO model and an image dataset for evaluating appearance quality. Through object detection in the images, a preliminary and efficient assessment of the precast components' appearance quality is achieved. Moreover, the detection results are mapped onto the point cloud for high-precision quality inspection. In the case of precast components with quality defects, precise quality inspection is conducted by combining the three-dimensional model data obtained from forward design conversion with the captured point cloud data through registration. Additionally, the paper proposes a framework for an automated inspection platform dedicated to assessing appearance quality in prefabricated buildings, encompassing the platform's hardware network.

The improved YOLO model achieved a best mean average precision of 85.02% on the VOC2007 dataset, surpassing the performance of most similar models. After targeted training, the model exhibits excellent recognition capabilities for the four common appearance quality defects. When mapped onto the point cloud, the accuracy of quality inspection based on point cloud data and forward design is within 0.1 mm. The appearance quality inspection platform enables feedback and optimization of quality issues.

The proposed method in this study enables high-precision, visualized and automated detection of the appearance quality of PC components. It effectively meets the demand for quality inspection of precast components on construction sites of prefabricated buildings, providing technological support for the development of intelligent construction. The design of the appearance quality inspection platform's logic and framework facilitates the integration of the method, laying the foundation for efficient quality management in the future.

The structural adaptive ability of the soft robot is fully demonstrated in the grasping task of the soft hand. A soft hand can easily realize the envelope operation of the object without planning. With the continuous development of robot applications, researchers are no longer satisfied with the ability of the soft hand to grasp. The purpose of this paper is to perceive the object’s shape while grasping to provide a decision-making basis for more intelligent robot applications.

This paper proposes a dual-signal comparison method to obtain the fingertip position. The dual signal includes the displacement calculated by the static model without considering the external load change and the displacement calculated by the bending sensor. The dual-signal comparison method can use the obvious change trend difference between the above two signals in the hover and contact states to identify the touch position. The authors make the soft hand scan around the object through touch operation to detect the object’s shape, and the tracks of every touch fingertip position can envelop the object’s shape.

The experimental results show that the dual-signal comparison method can accurately identify the contact moment of soft fingers. This detection method makes the soft hand develop the shape detection ability. The soft hand in the experiment can perceive squares, circles and a few other complex shapes.

The dual-signal comparison method proposed in this paper can detect a touch action by using the signal change trend when the working condition suddenly changes with the rough robotic model and sensing, thus improving the utilization value of the measured signal. The problems of large model errors and inaccurate sensors also negatively impact the use of other soft robots. It is generally difficult to achieve good results by directly using these models and sensors with the thinking of rigid robot analysis. The dual-signal comparison method in this paper can provide some reference for this aspect.

Universities are considered as learning institutions and their output is knowledge. Their main objectives are to promote knowledge and to integrate three main roles: (1) teaching and learning toward an award; (2) research and publication; and (3) activities centred toward work-based learning. Researchers generally categorize knowledge in three dimensions, cognitive, functional and social competence which are clearly consistent with the French paradigm- savoir, savoir faire, and savoir être. Delamare Le Deist and Winterton (2007) acknowledged that knowledge, that is, understanding is captured by cognitive competence, skills are captured by functional competence and behavioral and attitudinal competencies are captured by social competencies. This chapter describes some basic concepts of social competence in the tertiary education and examines the relationship that exists among knowledge, knowledge management, and social competence. Achieving personal goals and at the same time maintaining positive relationship over time and across situations is one of the main definition of social competence, as brought forward by Rubin and Rose-krasner (1992). Social competence also embraces all the social, emotional and cognitive knowledge and skills individuals require to achieve their goals and to be effective in their relations with others (Kostlenik et al., 2014).

The COVID-19 pandemic has compelled higher educational institutions to implement alternative educational strategies that rely heavily on internet accessibility and utilisation to monitor and evaluate students. This study aims to find certain indicators for planning and designing future courses of inclusive online education in the domain of disaster risk reduction (DRR).

The study reviews and analyses online teaching and learning experiences of DRR courses. It uses online surveys and interviews to derive the perspectives of selected students and educators in universities in Asia and the Pacific region.

Active engagement is considered to be achieved when students are active in chat boxes, through presentations, through assignments and when the video cameras of students are turned on. On the contrary, students perceive active engagement differently because they face emotional disturbances and health issues due to prolonged screen/digital device use, have inadequate information and communications technology infrastructure or have digital literacy deficiencies among others. The study finds that online courses have many sets of strengths, weaknesses, opportunities and threats, when they are balanced, they can improve DRR courses in the future.

The study is based on the outcome of interviews with 10 experienced educators in DRR courses as well as students from different schools taking courses in DRR education. However, the students are not necessarily taking the courses of the educators interviewed due to the inability of some educators to avail themselves and the challenge of contacting the students. This notwithstanding, the results of this study give a general overview of the situation to be considered in the planning and design of online and distance education.

The results do not reflect the reaction of students and tutors of the same course. Future studies of collecting and analyzing the responses from the students and the educators with the same course could provide tailored solutions.

This study attempts to find solutions to bridging two different perspectives on teaching and learning. The results would be important to strengthening and designing future online courses.