Today’s roadways are subject to traffic congestion, the deterioration of surface-assets (often due to the overreliance on private vehicle traffic), increasing vehicle-operation and fuel costs, and pollutant emissions. In Abu Dhabi, private car traffic forms the major share on urban highways, as the infrastructure was built to a high quality and the public transport network needs expansion, resulting in traffic congestion on major highways. These issues are arguably addressable by appropriate decisions at the planning stage. Microsimulation modeling of driving behavior in Abu Dhabi is presented for empirical assessment of traffic management scenarios. This paper presents a technique for developing, calibrating, validating, and the scenario analysis of a detailed VISSIM-based microsimulation model of a 3.5 km section of a 5-lane divided highway in Abu Dhabi. Traffic-count data collected from two sources, i.e., the local transport department (year 2007) and municipality (2007 and 2015–2016) were used. Gaps in traffic-counts between ramps and the highway mainline were noted, which is a common occurrence in real-world data situations. A composite dataset for a representative week in 2015 was constructed, and the model was calibrated and validated with a 15% (<100 vehicles per hour) margin of error. Scenario analysis of a potential public bus transport service operating at 15 min headway and 40% capacity was assessed against the base case, for a 2015–2020 projected period. The results showed a significant capacity enhancement and improvement in the traffic flow. A reduction in the variation between vehicle travel times was observed for the bus-based scenario, as less bottlenecking and congestion were noted for automobiles in the mainline segments. The developed model could be used for further scenario analyses, to find optimized traffic management strategies over the highway’s lifecycle, whereas it could also be used for similar evaluations of other major roads in Abu Dhabi post-calibration.
In the present paper, we demonstrate the engagement and the achievements of the undergraduate engineering students in research through different undergraduate research programs offered by internal university fund as well as through external sponsors. The aim was to encourage the students to develop their technical and research skills through participating in this unique opportunity under the supervisor of a principal investigator. Two groups worked successfully on recycling waste CFRP scrap material produced by domestic industries in order to investigate the potential of the waste to produce helpful products through understanding the mechanical properties of the recycled material. Hence, trying to come up with innovative solutions to resolve the undesirable and the negative impact of the recycled waste on the environment and to comply with the local legislation that enforces waste producers to handle the waste of their industries.
Project-based learning (PBL) is a student-centered approach in which students learn by solving real-life problems in a teamwork environment. Little is known about how students in different academic levels perceive the effectiveness of PBL approach. This study aims to compare and analyze students' perceptions of the use of PBL in different undergraduate- and graduate-level civil engineering courses. Students' perceptions of the effectiveness of the partial use of PBL in civil engineering courses to improve their understanding of course topics and develop their life skills were collected through a questionnaire distributed at the end of the semester. Data was collected from 104 students enrolled in five different undergraduate and graduate civil engineering courses. Results showed that students of the junior-level course held more positive attitude towards collaboration and teamwork rather than students of senior-level or graduate-level courses. The mean scores of the survey questions related to teamwork, collaboration, and communication skills tended to decrease with an increase in the course level. Junior-level students exhibited the highest degree of satisfaction in response to the survey question related to the degree of enjoyment when working in groups. Due to the difficulty in scheduling meetings, ineffective communications were noted by graduate students. They recorded the lowest mean scores for the survey questions related to teamwork/collaboration skills and degree of enjoyment when working in groups. Survey results indicated, however, that PBL was more effective in improving understanding of course topics, self-regulation and self-learning skills of the graduate students rather than undergraduate students.
This study proposes an innovative neural network framework, ASENN (Attention-based Selective Embedding Neural Network), for the prediction of pavement deterioration. Considering the complexity and uncertainty associated with the pavement deterioration process, two fundamental frameworks, SEL (Selective Embedding Layer) and MDAL (Multi-Dropout Attention Layer), are combined to enhance feature abstraction and prediction accuracy. This approach is significant while analyzing the pavement deterioration process due to the high variability of the contributing deterioration factors. These factors, represented as tabular data, undergo filtering, embedding, and fusion stages in the SEL, to extract crucial features for an effective representation of pavement deterioration. Further, multiple attention-weighted combinations of raw data are obtained through the MDAL. Several SELs and MDALs were combined as basic cells and layered to form an ASENN. The experimental results demonstrate that the proposed model outperforms existing tabular models on four road distress parameter datasets corresponding to cracking, deflection, international roughness index, and rutting. The optimal number of cells was determined using different ablation settings. The results also show that the feature learning capabilities of the ASENN model improved as the number of cells increased; however, owing to the limited combination space of feature fields, extreme depths were not preferred. Furthermore, the ablation investigation demonstrated that MDAL can improve performance, particularly on the cracking dataset. Notably, compared with mainstream transformer models, ASENN requires significantly less storage and achieves faster execution speed.
In this study, Bayesian Belief Networks (BBN) are proposed to model the relationships between factors contributing to pavement deterioration, where their values are probabilistically estimated based on their interdependencies. Such probabilistic inferences are deemed to provide a reasonable alternative over costly data collection campaigns and assist in road condition diagnoses and assessment efforts in cases where data are only partially available. The BBN models examined in this study are based on a vast database of pavement deterioration factors including road distress data, namely cracking, deflection, the International Roughness Index (IRI) and rutting, from major road sections in the United Arab Emirates (UAE) along with the corresponding traffic and climatic factors. The dataset for the analysis consisted of 3272 road sections, each of 10 m length. The test results showed that the most critical parameter representing the whole process of road deterioration is the IRI with the highest nodal force. Additionally, IRI is strongly correlated with rutting and deflection, with mutual information of 0.147 and 0.143, respectively. Furthermore, a Bayesian network structure with a contingency table fit of over 90% illustrates how the road distress parameters change in the presence of external factors, such as traffic and climatic conditions.
Capstone projects are integrated into engineering curricula to combine various subjects and impart essential professional skills that may be difficult to teach solely through traditional lecture-based courses. These projects play a crucial role in preparing students for their future roles as professional engineers, thereby significantly impacting a university’s industry reputation and ranking. The challenge in engineering education lies in aligning the teaching approach of educators with the diverse learning styles of their students. This study aims to examine the impact of the learning style of the students measured by their watching-doing scores using the 4MAT tool, on the attainment of the benefits of the graduation project (GP). The Bayesian Belief Networks (BBN) approach was adopted in this study to analyse the data collected from 271 students enrolled in both GP1 and GP2 semesters in the engineering department of United Arab Emirates University. Results show that regardless of learning style, both watching and doing category students share similar perspectives on various aspects of the GP course, such as the optimal team performance ratio. However, when assessing the overall effectiveness of the GP programme, doing students exhibit a higher level of agreement than watching students. The study provides valuable insights to faculty members, helping them navigate the optimal balance between providing mentorship and fostering students’ independence during the different stages of their final-year design capstone projects. These findings underscore the importance of tailored educational strategies to accommodate diverse learning styles, contributing to more effective engineering education and better-prepared graduates.
This paper examines the performance of 3D-printed concrete made with locally abundant desert dune sand. Cement was replaced by up to 10% silica fume and 30% fly ash to reduce its detrimental environmental footprint. The water-to-binder ratio used in the mix ranged...
Research on small-scale 3D printing has rapidly evolved, where numerous industrial products have been tested and successfully applied. Nonetheless, research on large-scale 3D printing, directed to large-scale applications such as construction and automotive manufacturing, yet demands a great a great deal of efforts. Large-scale 3D printing is considered an interdisciplinary topic and requires establishing a blended knowledge base from numerous research fields including structural engineering, materials science, mechatronics, software engineering, artificial intelligence and architectural engineering. This review article summarizes key topics of relevance to new research trends on large-scale 3D printing, particularly pertaining (1) technological solutions of additive construction (i.e. the 3D printers themselves), (2) materials science challenges, and (3) new design opportunities.
This paper develops a fuzzy-logic model to aggregate the numerous managerial risks driven by the various project stakeholders at different project stages. The model accounts for the various perceptions of assessors involved in the risk evaluation process. The proposed model provides means for performing scenario analysis at an early project procurement stage to manage macro risks. Four managerial processes or competencies were considered as critical success factors for infrastructure projects. These are communication, coordination, knowledge sharing and decision-making. A Sugeno fuzzy logic model was calibrated using three input variables representing the importance of the various projects stakeholder groups, the criticalness level of the management process as perceived by the various groups at the various project stages, and the effectiveness level of the management competency. The model was validated using surveys of various stakeholder groups of a mega project.
Defects can have a significant impact on construction performance. Numerous studies have attempted to identify their root causes, contending that the prevention of defects could be achieved by eliminating the root causes. Yet, their direct causes also need to be considered in order to identify the sequence of events leading to defects. This study aims to classify the defective acts that are directly linked to the occurrence of a defect, in order to provide insights about the nature and the impact of different types of direct causes. The study involves investigation into 272 defects from 81 disputes that occurred in the Dubai residential construction industry in 2009. Results from this study reveal that the majority of construction defects are driven by a violation of practices or workers’ lack of skill and competence. While it is difficult to prevent deliberate violations, increased effort toward growing skills and competencies would be effective in reducing defects, and therefore improving construction performance. Also, classification of defective acts is envisioned as a platform toward a more thorough causal tracking of construction defects, facilitating the identification of latent conditions resulting in defects.
