Public transport can discourage individual car usage as a life-cycle asset management strategy towards carbon neutrality. An effective public transport system contributes greatly to the wider goal of a sustainable built environment, provided the critical transit system attributes are measured and addressed to (continue to) improve commuter uptake of public systems by residents living and working in local communities. Travel data from intra-city travellers can advise discrete policy recommendations based on a residential area or development’s public transport demand. Commuter segments related to travelling frequency, satisfaction from service level, and its value for money are evaluated to extract econometric models/association rules. A data mining algorithm with minimum confidence, support, interest, syntactic constraints and meaningfulness measure as inputs is designed to exploit a large set of 31 variables collected for 1,520 respondents, generating 72 models. This methodology presents an alternative to multivariate analyses to find correlations in bigger databases of categorical variables. Results here augment literature by highlighting traveller perceptions related to frequency of buses, journey time, and capacity, as a net positive effect of frequent buses operating on rapid transit routes. Policymakers can address public transport uptake through service frequency variation during peak-hours with resultant reduced car dependence apt to reduce induced life-cycle environmental burdens of buildings by altering residents’ mode choices, and a potential design change of buildings towards a public transit-based, compact, and shared space urban built environment.
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Over time, the pavement deteriorates due to traffic and the environment, resulting in poor riding quality and structural inadequacies. Evaluating pavement condition over time is thus a critical component of any pavement management system (PMS) to extend the service life of pavements. However, the uncertainty associated with the pavement deterioration process due to the heterogeneous nature of the pavement degradation factors makes the process difficult. The current work addresses this challenge of pavement management by developing an expert system framework based on Bayesian Belief Networks (BBN). This framework integrates data on existing road deterioration factors with knowledge gained from pavement experts to produce optimal decisions. The advantages of the BBN techniques lie in their ability to capture uncertainty, and probabilistically infer the values of variables in the domain, especially in the case of incomplete information where we only have data about some and not all variables. This has motivated the adoption of BBN in this study to optimize pavement maintenance decisions, on the basis of inferred road deterioration interpretations drawn from partial knowledge about road distress variables. This study presents the adoption of Bayesian methods to assist pavement maintenance engineers in determining the most successful and efficient maintenance and repair (M&R) tactics and the best time to apply them by means of a decision-support system. Data collected from 32 road sections in the United Arab Emirates in relation to road distress parameters (rutting, deflection, cracking, and international roughness index), as well as road characteristics, traffic, and environment data, has been used to demonstrate the applicability of the proposed decision-support tool.
This study aims to identify the defect generation rules between defects, to support effective defect prevention at construction sites. Numerous studies have been performed to identify the relations between defect causes, to prevent defects in construction projects. However, identifying the inter-causal pattern does not yet guarantee an ultimate grasp of what constitutes proper defect mitigation strategies, unless the underlying defect-to-defect generation rules are thoroughly understood too. Specifically, if a defect generated in a work process is ignored without taking necessary corrective action, then additional defects could be generated in its following works as well. Thus, to minimize defect generation, this study analyzes the defects in the sequence of a construction work. To achieve this, the authors collected 9054 defect data, and association rule mining is used to analyze the rules between the defects. Consequently, 216 rules are identified, and 152 rules are classified into 3 categories along with 4 experts (71 expected rules, 22 unexpected but explainable rules, and 59 unexpected and unexplainable rules). The generation rules between the defects identified in this study are expected to be used to regularize various defect types to determine those that require priority management.
Annually, a huge number of pilgrims visit Mecca to perform Al Hajj ritual. Crowd management is critical in this occasion in order to avoid crowd disasters (e.g., stampede and suffocation). Recent studies stated that various factors, such as the environment, fatigue level, health condition and emotional status have a significant effect on crowded events. This calls for a need for an automated data analytics system that feeds event organizers with information about those factors on real-time, at least from a generalizable sample of crowd subjects, in which proactive crowd management decisions are made to reduce overall risks. This paper develops a novel methodology that fuses mobile GPS and physiological data of Hajj pilgrims collected through wearable sensors to train three classification models: (a) current performed Hajj activity, (b) fatigue level, and (c) emotional level. In a pilot experiment conducted against two subjects, promising results of a minimum of 75% accuracy levels were achieved for the activity recognition and fatigue level classifiers, whereas the emotional level classifier still requires further refinements.
Construction errors can have significant effects on project performance. Yet, any attempt to tackle error should be driven by the ability to understand its archetypal nature. This study aims to analyze a variety of construction errors at the execution stage, in order to develop a comprehensive set of categories that shall provide insights about the effect of different error types on the industry. We investigate a database provided by an authoritative source, which includes a number of 256 construction errors that occurred in the year 2009 in Dubai construction industry. Results from this study reveal that the most common error arose from ‘poor workmanship’ which accounted for 21% of the total encountered faults. The next most common types arose from the ‘usage of impaired materials’, followed by the ‘deviation from an intended dimension’. These observations infer that the majority of construction errors are driven by workers’ lack of skill or competence. Moreover, it suggests that execution-oriented errors are the major cause of faults and accidents rather than design errors.
International Journal of Innovation, Management and Technology (IJIMT) is an international academic open access journal which gains a foothold in Singapore, Asia and opens to the world. It aims to promote the integration of innovation management and techn
To accurately direct investments towards sustainable transit, current transport status and factors driving passengers towards private cars instead of public transport (PT) should be identified first. Past research advocated improvements in PT to shift mode-usage but has yet to model the different causal effects that direct bus users to cars in rapidly developing yet congested areas. On-board questionnaire survey data from intra-city Abu Dhabi bus passengers (n = 1520, variables = 31) over a month were gathered in this study during both weekends and weekdays. The study modelled existing bias of travellers and quality attributes as antecedents of bus service's perceived value for money (VfM) and satisfaction from level of service (LoS) and mode choice (car vs. bus) as the ultimate consequence. Findings revealed that any previous biased opinions of travellers adversely affected satisfaction and perceived value, while quality attributes had a positive effect. Mode use was influenced by satisfaction from LoS (frequency of buses and network coverage), which was a positive consequence of perceived VfM (quality of ride and level of fare trade-off). Journey time and bus-stop waiting area quality also positively influenced satisfaction from fare level while passenger socio-demographic distribution showed that most respondents travelled more than five times a week by bus and were full-time workers and transport agencies may target service improvements around office-hours.
This paper proposes a customized convolutional neural network for crack detection in concrete structures. The proposed method is compared to four existing deep learning methods based on training data size, data heterogeneity, network complexity, and the number of epochs. The performance of the proposed convolutional neural network (CNN) model is evaluated and compared to pretrained networks, i.e., the VGG-16, VGG-19, ResNet-50, and Inception V3 models, on eight datasets of different sizes, created from two public datasets. For each model, the evaluation considered computational time, crack localization results, and classification measures, e.g., accuracy, precision, recall, and F1-score. Experimental results demonstrated that training data size and heterogeneity among data samples significantly affect model performance. All models demonstrated promising performance on a limited number of diverse training data; however, increasing the training data size and reducing diversity reduced generalization performance, and led to overfitting. The proposed customized CNN and VGG-16 models outperformed the other methods in terms of classification, localization, and computational time on a small amount of data, and the results indicate that these two models demonstrate superior crack detection and localization for concrete structures.
The outcomes-based learning (OBL) approach focuses on academic outputs rather than educational input for quality assurance (QA) and quality enhancement (QE). The practicality and feasibility of adopting OBL in engineering education are questionable. Little is known about how students in different academic levels perceive OBL in engineering education. This paper aims to fill this gap through a study designed to obtain and evaluate perceptions of students of the effectiveness of adopting OBL in civil engineering education. Data was collected from 79 students enrolled in four different undergraduate and graduate civil engineering courses. Students' perceptions were collected through a questionnaire distributed on students at the end of the offering semester. Findings demonstrated the feasibility and effectiveness of adopting OBL in civil engineering courses to support student learning and improve their understanding of course topics. Students of the senior- and graduate-level courses held more positive attitude towards the value-added learning offered by OBL-based courses rather than students of the junior-level courses.
Life-cycle management of road network projects traditionally emphasise material production and construction stages, with less attention given to usage stage and functionality improvement. Increasingly there is a need to address: inconsistencies in cost attribute selection; adjusting for uncertainties and costs; clarifying system boundaries; data sources; functional units and regional or temporal applicability of life-cycle frameworks. The current study focuses on a critical literature review of life-cycle cost analysis (LCCA) and life-cycle assessment (LCA) research published in the last decade (post 2008) towards identification of research gaps. Accurately analysing all life-cycle stages, feedback loops, future cash and resource flows, and interlinking performance with overall sustainability can aid the decision-making process towards sustainable alternatives for constructing new, or rehabilitating existing roads. This review finds that the use of recycled materials, base/sub-base stabilisers and asphalt binder replacement has the potential of energy saving (≥34% or 3.1 TJ), mitigating landfill disposal issues, and greenhouse gas load reduction (≥34.5% CDE). Lack of real world LCCA-LCA application and stakeholder prejudice against recycled material usage are addressable by better stakeholder (decision-makers and road users) engagement via a social component. The proposed enhancements identified in this study can increase LCA/LCCA attraction to policy-makers, planners and users and ultimately ensure a more sustainable asset.
