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Cracks are prevalent signs of pavement distress found on highways globally. The use of artificial intelligence (AI) and deep learning (DL) for crack detection is increasingly considered as an optimal solution. Consequently, this paper introduces a novel, fully connected, optimised convolutional neural network (CNN) model using feature selection algorithms for the purpose of detecting cracks in highway pavements.

To enhance the accuracy of the CNN model for crack detection, the authors employed a fully connected deep learning layers CNN model along with several optimisation techniques. Specifically, three optimisation algorithms, namely adaptive moment estimation (ADAM), stochastic gradient descent with momentum (SGDM), and RMSProp, were utilised to fine-tune the CNN model and enhance its overall performance. Subsequently, the authors implemented eight feature selection algorithms to further improve the accuracy of the optimised CNN model. These feature selection techniques were thoughtfully selected and systematically applied to identify the most relevant features contributing to crack detection in the given dataset. Finally, the authors subjected the proposed model to testing against seven pre-trained models.

The study's results show that the accuracy of the three optimisers (ADAM, SGDM, and RMSProp) with the five deep learning layers model is 97.4%, 98.2%, and 96.09%, respectively. Following this, eight feature selection algorithms were applied to the five deep learning layers to enhance accuracy, with particle swarm optimisation (PSO) achieving the highest F-score at 98.72. The model was then compared with other pre-trained models and exhibited the highest performance.

With an achieved precision of 98.19% and F-score of 98.72% using PSO, the developed model is highly accurate and effective in detecting and evaluating the condition of cracks in pavements. As a result, the model has the potential to significantly reduce the effort required for crack detection and evaluation.

The proposed method for enhancing CNN model accuracy in crack detection stands out for its unique combination of optimisation algorithms (ADAM, SGDM, and RMSProp) with systematic application of multiple feature selection techniques to identify relevant crack detection features and comparing results with existing pre-trained models.

Challenges of conducting site visit activities, a vital component of architecture learning during the recent pandemic have proved our unreadiness in facing the digital future. The lack of understanding of learning technology has affected the education experience. Thus, there is a need to investigate immersive learning technology such as immersive virtual reality (IVR) to replace students’ concrete experience in the current learning setting. This study aims to answer: (1) What is the influence of IVR in experiential learning (EL) in enhancing the personal spatial experience? (2) Does IVR in EL influence students' approach to learning during the architecture design process?

The research was conducted as an action research design approach. Action research was employed in the first-year architecture design studio by the lecturer as a practitioner-researcher. The personal spatial experience survey was performed in the earlier phase to identify the students’ prior spatial experience. Architectural Spatial Experience Simulation (ASES) a learning tool was implemented and assessed with Architecture Design Learning Assessment (ADLA) rubric, which was developed to evaluate EL and student’s approach to learning during the architecture design learning process.

The outcomes revealed that ASES as a learning tool in EL could improve the participants’ spatial experience, particularly those with minimal prior personal spatial experience. ASES was recognized to enhance the participants’ EL experience and encourage changes in student’s approach to learning from surface to deep learning.

This research benefits the architecture design learning process by offering a learning tool and a framework to resolve challenges in performing site visit activities and digital learning. It also contributes by expanding the EL theory and students’ approach to learning knowledge in the architecture education field.

This study investigates how organizational control systems induce emotional labor in frontline service employees (FLEs). Drawing on the stimulus–organism–response (S-O-R) theory, we hypothesized that two control systems, an outcome-based control system (OBCS) and a behavior-based control system (BBCS), trigger work engagement rather than organizational dehumanization in FLEs, leading them to choose deep acting rather than surface acting as an emotional labor strategy.

This study employed three-wave online surveys conducted 3–4 months apart to assess the time-lagged effects of S-O-R. We measured OBCS, BBCS (stimuli) and control variables at Time 1 (T1); work engagement and organizational dehumanization (organisms) at Time 2 (T2) and emotional labor strategies (responses) at Time 3 (T3). A total of 218 employees completed the T1, T2 and T3 surveys.

OBCS increased work engagement, leading to increased deep acting. BBCS enhanced organizational dehumanization, leading to increased surface acting. Post-hoc analysis confirmed that the indirect effect of OBCS on deep acting through work engagement and the mediation effect of BBCS on surface acting through organizational dehumanization were statistically significant.

This study collected three-wave data to reveal how organizational control systems affect FLEs’ emotional labor in the S-O-R framework. It illustrated how organizations induce FLEs to perform effective emotional strategies by investigating the effects of organizational control systems on their internal states.

This study explores how emotional intelligence, customer orientation, deep acting and surface acting influence job satisfaction among middle managers in their interactions with customers, colleagues and business partners. By examining these factors, we aim to provide insights into their collective impact on job satisfaction and interpersonal dynamics within organizational contexts.

By involving 95 middle managers at Indonesian Internet service providers as respondents, this research used a questionnaire to collect data. Next, the data were analyzed using the partial least square-structural equation modeling (PLS-SEM) technique, which evaluated measurement models and structural models. A total of twelve hypotheses were tested in this study.

This study found that customer orientation does not have a significant effect on deep acting, thereby nullifying its indirect effect on job satisfaction. Conversely, it's demonstrated that both deep acting and surface acting serve as partial mediators in the relationship between emotional intelligence and job satisfaction. Furthermore, surface acting emerges as a partial mediator in the connection between customer orientation and job satisfaction.

By exploring the relationship between customer orientation, emotional intelligence and job satisfaction among employees, this study seeks to reveal novel insights. The study examines the impact of these critical elements, which are necessary for middle managers to effectively manage their emotions and cultivate significant connections, on their overall job satisfaction and interpersonal dynamics in their diverse responsibilities.

Current solutions for monitoring the progress of pavement construction (such as collecting, processing and analysing data) are inefficient, labour-intensive, time-consuming, tedious and error-prone. In this study, an automated solution proposes sensors prototype mounted unmanned ground vehicle (UGV) for data collection, an LSTM classifier for road layer detection, the integrated algorithm for as-built progress calculation and web-based as-built reporting.

The crux of the proposed solution, the road layer detection model, is proposed to develop from the layer change detection model and rule-based reasoning. In the beginning, data were gathered using a UGV with a laser ToF (time-of-flight) distance sensor, accelerometer, gyroscope and GPS sensor in a controlled environment. The long short-term memory (LSTM) algorithm was utilised on acquired data to develop a classifier model for layer change detection, such as layer not changed, layer up and layer down.

In controlled environment experiments, the classification of road layer changes achieved 94.35% test accuracy with 14.05% loss. Subsequently, the proposed approach, including the layer detection model, as-built measurement algorithm and reporting, was successfully implemented with a real case study to test the robustness of the model and measure the as-built progress.

The implementation of the proposed framework can allow continuous, real-time monitoring of road construction projects, eliminating the need for manual, time-consuming methods. This study will potentially help the construction industry in the real time decision-making process of construction progress monitoring and controlling action.

This first novel approach marks the first utilization of sensors mounted UGV for monitoring road construction progress, filling a crucial research gap in incremental and segment-wise construction monitoring and offering a solution that addresses challenges faced by Unmanned Aerial Vehicles (UAVs) and 3D reconstruction. Utilizing UGVs offers advantages like cost-effectiveness, safety and operational flexibility in no-fly zones.

This paper aims to find the best method to predict the friction coefficient of textured 45# steel by comparing different machine learning algorithms and analytical calculations.

Five machine learning algorithms, including K-nearest neighbor, random forest, support vector machine (SVM), gradient boosting decision tree (GBDT) and artificial neural network (ANN), are applied to predict friction coefficient of textured 45# steel surface under oil lubrication. The superiority of machine learning is verified by comparing it with analytical calculations and experimental results.

The results show that machine learning methods can accurately predict friction coefficient between interfaces compared to analytical calculations, in which SVM, GBDT and ANN methods show close prediction performance. When texture and working parameters both change, sliding speed plays the most important role, indicating that working parameters have more significant influence on friction coefficient than texture parameters.

This study can reduce the experimental cost and time of textured 45# steel, and provide a reference for the widespread application of machine learning in the friction field in the future.

This study aimed to illustrate the conditions under which cultural diversity enhances workgroup creativity. It does so by investigating the impact of ethnic diversity on workgroup creativity through the mediating mechanisms of cognitive diversity and information elaboration, while also exploring the role of inclusive leadership in this process.

Multi-source data was collected from a sample of 338 employees nested within 56 workgroups across three distinct organizations. Conditional process analysis was used to empirically test the proposed hypotheses.

The results show that ethnic diversity, a surface-level cultural attribute, contributed to diversity in deep-level cognitive resources, and that workgroups were able to capitalize on these variations in deep-level cognitive resources to enhance their creativity when they engaged in the elaboration of task-relevant information. Results also demonstrated that the effective management of workgroup processes through inclusive leadership helped materialize the performance-promoting effects of cultural diversity. Overall, the findings support the notion that cultural diversity is indeed beneficial to workgroups.

Prior research has typically examined cultural diversity in workgroups from the perspective of either surface-level or deep-level cultural attributes, leading to conflicting findings. Our study takes a multifaceted approach to cultural diversity and its influence on workgroup creativity, offering a more nuanced understanding. Additionally, by integrating the concept of inclusive leadership, a relatively new conceptualization of leadership specifically relevant to diverse workgroups, we clarified strategies for fostering positive workgroup performance.

Transitions to and within university studies can be associated with heightened distress in students. This study focusses on the less studied transition from a bachelor’s to a master’s degree. During a master’s degree, study requirements and autonomy increase compared to bachelor’s studies. The present study examines how students’ experiences of study-related burnout, their approaches to learning and their experiences of the teaching and learning environment (TLE) change during this transition. Moreover, the study examines how approaches to learning and the TLE can affect study-related burnout.

Questionnaire data were collected from 335 university students across two timepoints (bachelor’s degree graduation and the second term of their master’s degree).

The results show that students’ overall experience of study-related burnout increases, as does their unreflective learning, characterised by struggling with a fragmented knowledge base. Interestingly, students’ experiences of the TLE seem to have an effect on study-related burnout in both master’s and bachelor’s degree programmes, irrespective of learning approaches. These effects are also dependent on the degree of context.

The study implies that students’ experiences of study-related burnout could be mitigated by developing TLE factors during both bachelor’s and master’s degree programmes. Practical implications are considered for degree programme development, higher education learning environments and student support.

In the purpose of the section, the cracks that are in the construction domain may be common and usually fixed with the human inspection which is at the visible range, but for the cracks which may exist at the distant place for the human eye in the same building but can be captured with the camera. If the crack size is quite big can be visible but few cracks will be present due to the flaws in the construction of walls which needs authentic information and confirmation about it for the successful completion of the wall cracks, as these cracks in the wall will result in the structure collapse.

In the modern era of digital image processing, it has captured the importance in all the domain of engineering and all the fields irrespective of the division of the engineering, hence, in this research study an attempt is made to deal with the wall cracks which are found or searched during the building inspection process, in the present context in association with the unique U-net architecture is used with convolutional neural network method.

In the construction domain, the cracks may be common and usually fixed with the human inspection which is at the visible range, but for the cracks which may exist at the distant place for the human eye in the same building but can be captured with the camera. If the crack size is quite big can be visible but few cracks will be present due to the flaws in the construction of walls which needs authentic information and confirmation about it for the successful completion of the wall cracks, as these cracks in the wall will result in the structure collapse. Hence, for the modeling of the proposed system, it is considered with the image database from the Mendeley portal for the analysis. With the experimental analysis, it is noted and observed that the proposed system was able to detect the wall cracks, search the flat surface by the result of no cracks found and it is successful in dealing with the two phases of operation, namely, classification and segmentation with the deep learning technique. In contrast to other conventional methodologies, the proposed methodology produces excellent performance results.

The originality of the paper is to find the portion of the cracks on the walls using deep learning architecture.

Modern human society has continuous advancements that have a negative impact on the quality of the air. Daily transportation, industrial and residential operations churn up dangerous contaminants in our surroundings. Addressing air pollution issues is critical for human health and ecosystems, particularly in developing countries such as Egypt. Excessive levels of pollutants have been linked to a variety of circulatory, respiratory and nervous illnesses. To this end, the purpose of this research paper is to forecast air pollution concentrations in Egypt based on time series analysis.

Deep learning models are leveraged to analyze air quality time series in the 6th of October City, Egypt. In this regard, convolutional neural network (CNN), long short-term memory network and multilayer perceptron neural network models are used to forecast the overall concentrations of sulfur dioxide (SO₂) and particulate matter 10 µm in diameter (PM₁₀). The models are trained and validated by using monthly data available from the Egyptian Environmental Affairs Agency between December 2014 and July 2020. The performance measures such as determination coefficient, root mean square error and mean absolute error are used to evaluate the outcomes of models.

The CNN model exhibits the best performance in terms of forecasting pollutant concentrations 3, 6, 9 and 12 months ahead. Finally, using data from December 2014 to July 2021, the CNN model is used to anticipate the pollutant concentrations 12 months ahead. In July 2022, the overall concentrations of SO₂ and PM₁₀ are expected to reach 10 and 127 µg/m³, respectively. The developed model could aid decision-makers, practitioners and local authorities in planning and implementing various interventions to mitigate their negative influences on the population and environment.

This research introduces the development of an efficient time-series model that can project the future concentrations of particulate and gaseous air pollutants in Egypt. This research study offers the first time application of deep learning models to forecast the air quality in Egypt. This research study examines the performance of machine learning approaches and deep learning techniques to forecast sulfur dioxide and particular matter concentrations using standard performance metrics.