Construction defects can exert significant impacts on project performance. These defects are typically traced back to their initiating root causes, formulated as infectious decisions, practices, or circumstances. The root causes often act as pathogens that propagate to spread numerous other risky conditions, thereby making projects prone to defects. Accordingly, it is essential to quantify the pathogenic capacity held by each root cause to determine the scope of potential risk associated with its existence. Although several studies have striven to identify project pathogens, research on determining the most pathogenic causes to develop further defect management efforts is lacking. To address this deficiency, the aim of this study was to develop a methodology that captures the complex mechanisms of defect generation and quantifies their pathogenic capacities in accordance to their position within the network of sequential events leading to defects. Specifically, the study uses a social network analysis (SNA) approach as a platform to formulate the interrelationships of defect causes, and review SNA metrics to develop a formal mathematical expression of pathogenicity, forming a new project pathogens network (PPN) methodology. Application to four residential projects in Dubai confirmed that PPN has great potential for revealing the pathogenic capacities held by the infectious conditions resulting in construction defects. This paper contributes to knowledge in the area of construction engineering and management by proposing a methodology that is deliberated to providing both mathematical and visualization analyses needed to address the complex mechanisms of defect generation, which thus enables project practitioners and academics to better understand the vulnerability of a construction-project system to pathogenic conditions.
Purpose To proactively draw efficient maintenance plans, road agencies should be able to forecast main road distress parameters, such as cracking, rutting, deflection and International Roughness Index (IRI). Nonetheless, the behavior of those parameters throughout pavement life cycles is associated with high uncertainty, resulting from various interrelated factors that fluctuate over time. This study aims to propose the use of dynamic Bayesian belief networks for the development of time-series prediction models to probabilistically forecast road distress parameters. Design/methodology/approach While Bayesian belief network (BBN) has the merit of capturing uncertainty associated with variables in a domain, dynamic BBNs, in particular, are deemed ideal for forecasting road distress over time due to its Markovian and invariant transition probability properties. Four dynamic BBN models are developed to represent rutting, deflection, cracking and IRI, using pavement data collected from 32 major road sections in the United Arab Emirates between 2013 and 2019. Those models are based on several factors affecting pavement deterioration, which are classified into three categories traffic factors, environmental factors and road-specific factors. Findings The four developed performance prediction models achieved an overall precision and reliability rate of over 80%. Originality/value The proposed approach provides flexibility to illustrate road conditions under various scenarios, which is beneficial for pavement maintainers in obtaining a realistic representation of expected future road conditions, where maintenance efforts could be prioritized and optimized.
Life cycle assessment methodology was applied in this study to calculate environmental impacts of a 3.5-km-long dual carriageway asphalt highway section case study in Abu Dhabi across following life cycle stages: material extraction and production, material and equipment transport, construction, maintenance and rehabilitation; assuming a 30 years lifetime. Environmental impact assessment for air emissions and energy consumption generated by complete roadworks, namely: earthworks; pavement courses; concrete works for traffic barriers, kerbs, parapets, traffic signs, and light systems. A comprehensive analysis of environmental impact reduction was performed using recycled construction waste; reclaimed asphalt pavement; warm-mix asphalt with synthetic zeolite additives; and, slag as alternate material and production options. Actual field data for the road section using virgin materials and traditional asphalt production mix for pavement works and Portland cement concrete for the complete concrete works were used as the baseline case. Routine maintenance and periodic rehabilitation by milling and repaving wearing course (<4.5 cm depth) every 5 years was also analysed from an environmental impact reduction perspective. Environmental assessment considered all indicators from ReCiPe midpoint method. Results show that earthworks account for a significant portion (26% of CO2eq.) of the environmental impacts for complete roadworks. The life cycle impact results of hot-mix asphalt and warm-mix asphalt were almost equal due to addition of synthetic zeolites. Results showed significant environmental impact reduction across all indicators, after coupling all alternate options as: 34% in CO2eq.; 48% in energy consumption; 24.4% in NOxeq.; 21.53% in PM2.5eq.; 21.2% in acidification; and, 10.4% in land use. Monte Carlo simulations confirm these results and the sensitivity of environmental benefits to the allocation methodology was also investigated, which showed that the results were only marginally sensitive to the allocation approach. This study noted higher environmental benefits than reported in roadworks literature due to alternate material and asphalt production options.
Recent years have seen a considerable shift in the focus of public investment agencies from extensive roadway networks to a more planned approach that meets environmental, cost, and social dimensions more aptly. Past research has mainly explored the engineering aspect and cost parameters, while the human or social component is often neglected. This study aims to identify the trip-making behaviour of residents in an urban area towards bus transport network enhancement. Abu Dhabi, the location of study, is heavily dependent upon car travel, creating much congestion, which the local government seeks to address by enhanced public transport. This work examined eight public-transport routes in two zones, with data collected on both weekdays (n = 751) and weekends (n = 769). Multinomial logistic regression models showed that respondents highlighted overcrowded buses and traffic congestion as two of the main hurdles pertinent to urban routes in the bus network influencing their mode choice. Proposals pertinent to the local authority for further consideration need to factor in current low satisfaction with bus transit network coverage, low satisfaction with the quality of bus rides, inhibiting a mode shift from cars/taxis towards buses, cumulative income profiles of public-transport users, with findings that the low-income bracket is already at saturation, and that reducing congestion needs innovative (sociodynamic rather than technical road network) public-transport solutions.
There have been numerous research efforts to minimize construction defects and a variety of suggestions have been provided. However, while all of these suggestions are valuable and have the potential to prevent defects, a construction company may have difficulty adopting them due to financial and practical constraints. Thus, this calls for the identification and characterization of the most influential causes of defects, in order to prioritize defect prevention strategies. To address this necessity, this paper aims to identify the most important causes of defects in terms of frequency, magnitude, and pathogenicity. For this goal, a questionnaire survey of 106 industry professionals was conducted to examine 30 causes of defects, collected through an extensive literature review. High frequency and high magnitude causes were identified and traced back to their initiating causes. Accordingly, the five most pathogenic causes were found to be (1) organizational culture, (2) time pressure and constraints, (3) workplace quality system, (4) financial constraints on operational expenses, and (5) inadequate employee training or learning opportunities. This paper is valuable to researchers in terms of developing a theoretical foundation to analyze and visualize the complex mechanisms of defect generation in construction. Further, this paper is of value to practitioners in terms of providing an effective tool to set defect prevention strategies and prioritize investment areas for quality improvements.
To deal with the complexity of today's engineering demands, leading universities intend to investigate educational advances and facilities to provide students a creative academic experience that integrates disciplinary knowledge and student transition to the world of the competitive job environment. By tackling a challenging design problem, the capstone project (CP) harnesses creative learning and transfer hands-on abilities into the industrial environment. Effective coaching on the understanding of course objectives aids both advisors and students to properly plan their tasks in CP design. Students and advisors must have a shared understanding of their roles and responsibilities, as well as the extent of independence anticipated from students. This study identifies specific areas in the capstone project that require direct advisor mentoring, aside with other areas in which students are anticipated to work independently, through a questionnaire survey consisting of 206 participants from both segments: advisors and final year engineering students. The survey results outline the discrepancies and consensus of perceptions amongst both segments, provide insights about current CP delivery, and highlight potential modifications that may be considered for improving the delivery of the capstone project where optimum levels of knowledge and skills are obtained by students. The results of this study serve as basis for guiding faculty members about finding the right balance of mentorship and students' independence, as students progress throughout different stages of their final year design capstone projects.
It is common for a construction schedule to deviate from its original-planned baseline, as uncertainty is inherent in all construction activities. Accordingly, planners are required to perform periodic schedule updates that learn from retrospective progress to more accurately schedule remaining activities and draw optimum recovery plans. This research proposes a method that utilizes a neural network regression model to forecast upcoming productivity rates based on retrospective progress and accordingly updates the schedule on a regular time interval with the required resource adjustments to meet the planned end date of the project with optimal cost. The method was tested on brickwork activities at a residential complex construction project in the UAE, using retrospective progress data of 1487 working days for 132 masons, and was found to be 98% accurate in predicting labor productivity, which was thus used as a basis to draw schedule recovery plans according to the proposed framework. In essence, this research provides a platform toward an automated self-recovering scheduling system, which serves construction managers in proactively preventing potential schedule deficiencies.
Design, operation, maintenance and rehabilitation of mass mobility projects require holistic environmental, energy and monetary action; transit solutions depend on public authorities' influence over user-adoptability and sustainability. New technically advanced automated vehicles (AVs) and alternate fuel technology require consideration by planners, where such urban-settlement transportation systems are increasingly charged to be efficient, reliable, interconnected traffic pathways marked by in-out nodes able to direct people effectively. Prototype mass-transit alternative(s) choice is complex, with the infrastructure decisions largely bound to the different objectives of consumers, policy and government agencies. Three factors must be considered: user-stakeholder appraisal; and both environment; and, economic factors during the entire asset life-cycle of production, construction, operation, maintenance, rehabilitation (OM&R) and final disposal or salvage value. To assess this life-cycle impact, a new research project is presented that proposes a framework consisting of: (i) an analysis of traffic flow patterns within an intra-city settlement, addressing the suitability of any preferred alternative for direct real-world application alongside user-opinion; (ii) the life-cycle cost and environmental in/out-flows for any proposed holistic AV-based alternative(s) assessed against existing systems; and, (iii) stakeholder expert-opinion involvement through a multi-criteria decision-making (MCDM) framework assigning weightage to cost, energy and emissions. Towards this, user-preference data collected from intra-city bus passengers in Abu Dhabi notes a majority of passengers as full-time workers, in which the notion of reduced fare level and an increased network coverage was not homogenously supported, thus it is argued that municipal agency and policy-makers need to target work commutes supported by innovative solutions to enhance user-experience by reducing journey time and fluctuating service frequency around office hours. It is suggested that regional road transport system energy, cost and emission issues may be resolved by using the proposed framework that builds upon life-cycle impact strategies integrated directly into decision-analyses. It's argued that decision-making frameworks, if they are to be successfully implemented, must allow ongoing feedback loops included in this framework.
Understanding defect causalities is indispensable to its prevention. This paper aims to identify the correlations and inter-causalities amongst the root causes of construction defects, so as to obtain insights about the complex mechanics of defect causation and assist in developing effective defect prevention strategies. Data was collected through a questionnaire survey of 106 professionals in the construction industry. Correlations and inter-causalities analysis showed that time pressure, financial constraints and organizational culture were the most influential root causes.
• This study provides the first systematic and bibliometric review of LLM and GenAI applications in transportation. • A total of 65 studies (2023–2024) were examined and classified into themes of traffic management, autonomous driving, and safety. • Findings show LLMs’ transformative potential in traffic prediction, crash analysis, and risk perception using multimodal data. • Key challenges include high computational needs, limited data, scalability issues, and cybersecurity risks in transport. • Future research will focus on lightweight architectures, better multimodal integration, and privacy in smart mobility. The integration of Large Language Models (LLMs) and Generative AI (GenAI) in transportation has gained significant attention, particularly in applications related to traffic safety, intelligent transportation systems, and autonomous driving. This paper provides a bibliometric analysis and systematic review of the current literature on LLM-based applications in transportation, analyzing 65 relevant studies published between 2023 and 2024 from Scopus and Web of Science. The review categorizes existing applications into three primary thematic areas: Traffic (28 studies), Autonomous Driving (23), and Safety (15). The findings highlight the transformative role of LLMs in traffic prediction, crash analysis, and risk perception, demonstrating their ability to process large-scale, multimodal datasets with improved efficiency and adaptability. Additionally, this study explores challenges such as computational demands, data limitations, model scalability, and cybersecurity concerns, providing insights into emerging solutions for real-time traffic management, accident prevention, and human-AI interaction in autonomous systems. The review concludes by identifying key research gaps and future directions, emphasizing the need for lightweight AI models, enhanced multimodal integration, and privacy-preserving frameworks to advance LLM applications in smart and sustainable transportation systems.
Traffic growth and microsimulation models are utilised to assess four traffic management scenarios: business-as-usual; public bus transport case; public bus rapid transit (BRT) case; and, a traffic demand-responsive autonomous-BRT case, focusing on fuel energy efficiency, headways, fleet control and platooning for lifecycle analysis (2015–2045) of a case study 3.5 km long 5-lane dual-carriageway section. Results show that both energy consumption and exhaust emission rates depend upon traffic volume and flow rate factors of vehicle speed-time curves; acceleration-deceleration; and braking rate. Public transport promotion was found to be an effective and easy-to-implement environmental burden reduction strategy. Over 30-years analysis period, "Bus Case" reduced energy use, CO2, NOx and PM exhaust emissions by 25%, 24%, 29%, and 18%, respectively. BRT reduced 35%, 35%, 45% and 20%, respectively. Autonomous-BRT resulted in the highest reduction in energy consumption (56%), CO2 (55%), NOx (50%) and PM (25%) exhaust emissions.
This paper reports on student's quantitative and qualitative feedback on a flipped-classrooms pedagogy used to deliver an undergraduate Civil Engineering course. The flipped-classrooms pedagogy was used in teaching some of the course modules. Students were asked to reflect on the perceived difference of the new method compared to the traditional method, used for teaching the rest of the course modules. The online survey results showed that the vast majority of students Agreed or Strongly Agreed that the flipped classrooms approach improved their learning experience and level of interaction. While students were initially doubtful about the new format, they adapted quickly and found it to be satisfactory and effective. A worthwhile suggestion raised by plenty of students to the instructor is to spare 15-20 minutes of the beginning of the face-to-face session for questions and answers and to recap the material covered at the primarily online session before starting class problem solving activities. Results of this study provide a statement of encouragement for Civil Engineering faculty to implement flipped-classrooms as a contemporary and effective teaching method.
