Urban development and infrastructure maintenance
Modular Construction Research Group
University of Alberta, Department of Civil & Environmental Engineering
Sustainable urban development, with ready access to amenities and infrastructure, results in a higher quality of life for all citizens. We conduct research investigating infrastructure planning, maintenance, policies, and community design in order to enhance quality of life and sustainability. This involves exploring potential efficiencies in the design, delivery, and maintenance of infrastructure, and researching policy changes that could reduce costs while enhancing safety. Complementary research initiatives include evidence-based elderly-friendly architectural design, exploring innovative designs that allow elderly persons to age at home with reduced safety hazards.
A deep learning-based framework for an automated defect detection system for sewer pipes
This research proposes a framework for automated defect detection in sewer pipes. The municipal drainage system is a key component of every modern city’s infrastructure. However, as the drainage system ages its pipes gradually deteriorate at rates that vary based on the conditions of utilization (i.e., intrinsic conditions) and extrinsic factors such as the presence of trees with deep roots or the traffic load above the sewer lines, which collectively can affect the structural integrity of the pipes. As a result, regular monitoring of the drainage system is extremely important since replacement is not only costly, but, more importantly, can disturb the daily routines of citizens. In this respect, closed-circuit television (CCTV) inspection has been widely accepted as an effective inspection technology for buried infrastructure. Since sewer pipes can run for thousands of kilometres underground, cities collect massive amounts of CCTV video footage, the assessment of which is time-consuming and may require a large team of trained technologists. In our research a framework is proposed to realize the development of a real-time automated defect detection system that takes advantage of a deep-learning algorithm. The framework focuses on streamlining the information and data flow, proposing patterns of input and output data processing. A state-of-the-art convolutional neural network (CNN)-based object detector, the YOLOv3 network, is employed in this research, as this deep-learning algorithm is known to be very efficient in the field of object detection from the perspective of processing speed and accuracy. The model used in this research is trained with a dataset of 4,056 samples that contains six types of defects (i.e., broken, hole, deposits, crack, fracture, and root) and one type of construction feature (tap). The performance of the model is validated with a mean average precision (mAP) of 85.37%. The proposed output of the system includes labelling of the CCTV video footage, identification of the frames that contain defects, and associated defect information. The labelled video can serve as a benchmark for assessment technologists, while the multiple output frames provide an overview of the condition of the sewer pipe.
Asset levels of service (ALOS)-based decision support system for municipal infrastructure investment
In this research we develop a web-based decision support system for municipal infrastructure investment based on optimum Asset Levels of Service (ALOS). The research framework is premised on the notion that ALOS should be one of the main criteria for municipal infrastructure investment, while secondary parameters include physical deterioration of assets, future growth and the impact on the dependent infrastructure network.
A framework to quantify the economic impact of changing the road paving standard and related municipal bylaws on housing affordability
Jurisdictions in cold-climate regions impose restrictions on asphalt paving of neighbourhood roadways based on weather conditions. These limitations are imposed mainly to avoid the inadequate compaction of asphalt that results in poor performance of roads. Paving restrictions during the weather fluctuations of late-autumn cause project delays. As a result, a significant increase in cost is observed due to long-term project overhead and idle equipment costs, and this increased cost ultimately reduces housing affordability for residents of such jurisdictions. In the construction industry, there are a few examples of successful paving work in low temperatures using innovative technologies and materials.
Construction planners typically make decisions about work schedule based on the respective cost-benefit performance of two options (e.g., selecting an innovative technology to avoid schedule extension or waiting for suitable weather). This research studies the impact of weather on asphalt paving works in Edmonton, Alberta, Canada, considering the case of a neighbourhood road construction project. We propose a framework to quantify the economic impact of these limitations using historical weather data, and develop a simulation model of paving work by which to analyze the change in construction progress associated with a relaxation of weather limitations while verifying the asphalt performance in these conditions. This research is motivated by the steady growth of the residential construction sector in North America, the high construction cost, the short duration of the outdoor construction season in cold-climate regions, and the imperative to re-examine the weather-related limitations imposed by different jurisdictions. It is found that lowering the temperature standard can potentially improve profitability of neighbourhood development work and improve housing affordability, while using an alternative material (such as warm-mix asphalt) under the existing weather limitations does not improve housing affordability.
Spatial analysis framework for age-restricted communities integrating spatial distribution and accessibility evaluation
The rapid growth of the older-adult segment of the population is having a profound effect on urban development and the fulfillment of housing needs. The location of age-restricted communities that serve older adults aged 65 or older can influence accessibility to goods and services and further affect the health and quality of life of seniors. However, few quantitative indicators are available to judge whether the accessibility between these communities and neighbouring facilities is appropriate. Therefore, this research develops a spatial analysis framework for age-restricted communities that takes a regional/local perspective. The regional analysis explores the spatial distribution of age-restricted communities, while the local analysis involves the accessibility measure of these communities to necessary neighbouring facilities by type, age-friendly community identification, and a comprehensive accessibility evaluation. This framework is then applied to investigate three types of age-restricted communities in Edmonton, Canada. The results indicate that age-restricted communities are predominantly located centrally in the city, while population ageing is not prevalent in any of these neighbourhoods. Some communities are regarded as friendly to older adults, but, excluding nearby bus stops and emergency medical services stations, the options for different amenities among these communities are not diverse. The elderly in independent living communities tend to have better access to necessary neighbouring facilities. The methodology developed in this research can be used for accessibility analysis of age-restricted communities for different stakeholders, and can also lead to improvements to enhance age-friendliness in neighbourhood design.