QScience Connect - Volume 2022, Issue 1

Background: The fourth dimension of building information modeling (BIM) plays a significant role in construction planning by linking the construction schedule to the existing building information model. However, a difficulty may arise concerning the ease with which a link can be made and modified. Pattern-based techniques that search for recurring processes can help eliminate this drawback by producing predefined process templates. Methods: This paper critically examines the applicability of generating pre-defined process templates for BIM-based schedules using process pattern recognition techniques to reduce the effort of defining the construction schedules and integrating the templates into the BIM data. The technique estimates the level of recurrence of certain tasks within a schedule by applying several metrics. A real-life construction schedule from a housing project in the State of Kuwait was decomposed into numerous sub-schedules based on a set criterion to estimate the level of recurrence, the sub-schedules were compared based on structural and contextual similarities. Results and Discussion: The generated process templates produced demonstrated that the approach is ideal for projects with repetitive processes and that by utilizing the templates, linking building elements to tasks in 4D modeling can be greatly facilitated, thereby reducing the planning time. Conclusion: This study corroborates the results of previous literature, which found that the improvement in the efficiency of construction planning could be achieved by applying reusable process templates. The generated templates should enable the idea of storing the process templates in data banks for detecting undesired regularities in previous schedules in preparation for future use.

Background: Elevated PM2.5 levels pose serious health hazards and are implicated in numerous acute and chronic conditions. Delineating the contributions of meteorological factors to PM2.5 levels is a daunting task, especially in confined or semiconfined urban spaces. This study aims to (1) characterize the influence of wind speed and direction on outdoor PM2.5 levels within a semiconfined urban environment, and (2) develop a simple and readily accessible data mining method for this purpose. The ultimate goal is to evaluate the extent to which PM2.5 correlations demonstrated in open spaces hold in semiconfined outdoor settings with irregular terrain. Methods: In this study, data mining techniques were applied to retrieve patterns pertaining to the effects of meteorological factors on PM2.5 levels. As a proof of concept, a feasible framework was developed to elucidate the associations between wind speed and direction and PM2.5 levels during May 2020 in Education City, Doha, Qatar. Results and Discussion: The results showed a modest negative correlation between wind speed and PM2.5 levels, at low to moderate, but not high, PM2.5 readings. Meanwhile, no correlation was detected between wind direction and PM2.5 levels. Conclusions: Limited by the geographical location, microenvironment, and duration of this study, it can be said with moderate statistical confidence that low PM2.5 readings are associated with high wind speeds. As a result, increasing wind speed may be beneficial at low to moderate PM2.5 levels. However, delineating a single contributing factor to high PM2.5 readings may prove infeasible. Moreover, an association with wind direction was not immediately obvious, possibly due to microenvironmental limitations. These findings underscore the applicability of data mining and the importance of microenvironmental factors in air quality research and mitigation.

Background: The COVID-19 lockdown reduced anthropogenic activities worldwide and therefore provided a unique opportunity to investigate the factors that impact air pollution. Here, we investigated the effect of the COVID-19 lockdown on particulate matter (PM2.5) levels in Education City, Doha, Qatar. Methods: Ambient air samples were collected in real time from two stations within Education City during 2019 and 2020. PM2.5 data collected from four different seasons during the lockdown were compared with their corresponding pre-lockdown levels. Results: No significant changes in PM2.5 levels were observed during the spring and fall seasons. A 10% decline in the PM2.5 level was observed post-lockdown in the summer season, whereas a 33% increase in the PM2.5 level was observed post-lockdown during the winter season. Conclusion: The decline in PM2.5 levels in Education City during the 2020 COVID-19 lockdown was transient and modest. No significant decline in PM2.5 levels was detected for most of the year. Therefore, anthropogenic activities (vehicular and industrial) could have had season-dependent effects on ambient PM2.5 levels within Education City.