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Although road surface friction is considered the most effective performance measure for maintenance operations, it is not commonly used due to the high cost of collection. As a result, most jurisdictions use subjective visual indicators that qualitatively describe the state of the road surface, even though they create measurement inconsistencies and offer less detailed maintenance tracking. For maintenance personnel to transition into using friction, the collection cost must be reduced. This paper attempts to do so by proposing a low-cost, machine learning-based method for predicting road surface friction using dash camera imagery and demonstrates its feasibility through a case study. The dataset used for this project was collected in the City of Edmonton, AB, Canada during its 2021/2022 winter season. Three models were developed using tree-based algorithms, where all three displayed high performance with an average root mean squared error of 0.0796 or 79.3% accuracy based on RMSPE. [ABSTRACT FROM AUTHOR]

Speed limits are a common traffic regulation for balancing traffic mobility and safety on roadways. Speed transition zones bear complicated driver behaviours. However, driver behaviours are even more complex when speed transition zones are dynamically created and shifted by variable speed limits (VSLs). Much existing research has estimated long-term driver compliance, evaluated effectiveness of speed enforcement, and attempted to involve compliance into VSL control algorithms. Whereas, limited research provides convincing solutions for representing speed limit effect and estimating real-time driver compliance at speed transition zones. To fill this research gap, this paper analyses field data from two speed transition zones along southbound of 97th Street, Edmonton, Canada. Temporal and spatial variations of speed and driver compliance are investigated in detail using statistical tests. A linear regression is then established to rank the contributions of the selected factors. Finally, some suggestions and guidelines for VSL algorithm design and implementation are proposed. [ABSTRACT FROM AUTHOR]

Condition assessment is a key component of successful asset management, and many diverse condition assessment protocols exist for sewer mains. These protocols are used to generate condition grades for sewer mains after inspection. Condition assessment protocols are important because renewal actions for sewers are prioritized based on condition grades. If condition grades assigned to sewers do not reflect actual pipe conditions close enough, the resulting allocation of resources will not be the best. This paper presents a comparison of three possible sewer condition assessment protocols for the City of Edmonton: the Pipeline Assessment and Certification Program quick grading method, the Manual of Sewer Condition Classification fourth edition, and the City of Edmonton's Sewer Physical Condition Classification Manual. The results show that structural sewer condition grades depend significantly on the protocol used; therefore, protocol selection has significant financial consequences for asset management programs in the city. [ABSTRACT FROM AUTHOR]

This paper reports on a laboratory investigation into the performance of a novel stacked drop manhole design where two identical rectangular manholes are stacked one beside the other but at different heights so that there is a drop in elevation from one to the other. The focus of the study was to estimate the energy dissipation that occurs in such stacked manholes during diverse inflow conditions. Flow regimes inside the structure were identified and the effectiveness of the design was assessed under variable inflow conditions. Total energy dissipation in the stacked manhole was found to range from about 50% to 90%, and the contribution of each manhole chamber to the overall energy dissipation was assessed. A relationship between water depths in the manhole chambers and the corresponding outflow conditions was established. In addition, an analysis of the flow patterns and flow regimes highlighted the relevant parameters involved in the mechanisms of energy dissipation. [ABSTRACT FROM AUTHOR]

Full-scale laboratory tests were carried out at the University of Alberta to investigate the post-wrinkling ultimate behavior of steel pipelines. The pipe specimens exhibited extreme ductile behavior and did not fail in fracture under monotonically increasing axisymmetric compressive axial loads and displacements. Fractures developed at the wrinkled region, however, when a wrinkled pipe specimen was subjected to cyclic strain reversals due to unloading and loading of primary loads. This paper presents test procedures, complete post-wrinkling behavior, fracture limit strain values, and fracture configurations obtained from full-scale tests on wrinkled pipe specimens under cyclic and monotonic axial loadings. [ABSTRACT FROM AUTHOR]

This paper reviews the factors and criteria for the design of new and the retrofitting of existing dry detention ponds to enhance removal of total suspended solids (TSS) from stormwater. Detention time is discussed as the most important factor affecting TSS removal. Two-stage facilities and multi-level outlet design are important means of enhancing TSS removal in dry ponds. Two dry ponds within the city of Edmonton were selected to evaluate their TSS removal. The level of expected TSS removal is low owing to the relatively short detention times for both ponds. Methods for retrofitting the dry ponds to enhance TSS removal are discussed.Key words: dry pond, stormwater, TSS removal, detention time, retrofitting.Cet article passe en revue les facteurs et critères de conception de nouveaux étangs de rétention, et de modification de ceux déjà existants, avec pour but d'améliorer la capacité d'enlèvement des substances solides totales en suspension (SST) contenues dans les eaux de ruissellement. Le temps de rétention est examiné en tant que facteur principal affectant l'enlèvement des SST. Les installations à deux étages et la conception de sorties multi-niveaux sont des procédés importants qui améliorent l'enlèvement des SST dans les étangs. Deux étangs de la Ville d'Edmonton ont été sélectionnés et leur capacité de rétention a été évaluée. Le niveau d'enlèvement des SST escompté est bas compte-tenu des temps de rétention relativement courts de ces deux étangs. Les méthodes de modification des étangs visant à améliorer l'enlèvement des SST sont examinées.Mots clés : étang, eaux de ruissellement, enlèvement des SST, temps de rétention, réajustement.[Traduit par la Rédaction] [ABSTRACT FROM AUTHOR]

In the past few years, artificial neural networks (ANNs) have been used in describing and modelling wastewater treatment processes. Artificial neural network models can be identified without a detailed knowledge of the kinetics of the system to be modelled. Also, ANN models can potentially contain a great deal of information about the system itself, including the same type of information contained in conventional deterministic models. The fact that these models can be continuously updated with minimal resource requirements makes them very attractive for application in a real-time control scenario. In the current paper, applications of ANNs in the field of wastewater treatment performance prediction are reviewed. In addition, this paper presents a case study that reports some comprehensive modelling work to develop nonlinear neural network prediction models for the Gold Bar Wastewater Treatment Plant (GBWWTP), the largest sewage treatment facility in Edmonton, Alberta. [ABSTRACT FROM AUTHOR]

This paper examines the application of the artificial neural network (ANN) modeling technique to model a lime softening process at a full-scale drinking water treatment facility. The modeling was done for the Rossdale Water Treatment Plant (WTP) operated by EPCOR Water Services Inc. in Edmonton, Alberta. It was determined that ANN can model a lime clarifier accurately and with superior performance to other modeling methods. During the development stage, a prediction of alum clarifier pH also becomes necessary, and a very accurate inferential (virtual) sensor for pH was developed using ANN. The ANN models were also integrated with the Supervisory Control and Data Acquisition (SCADA) system of the plant so that real-time predictions of lime doses and effluent total hardness could be monitored. It was shown that the performance of the ANN models that were developed using average daily values for the parameters also work well when they are executed in real time. [ABSTRACT FROM AUTHOR]

Artificial neural network (ANN) technology has evolved from the experimental stage into actual industrial applications. To achieve this significant transition, careful planning and adjustment are required. This paper illustrates such an example in the water treatment industry. The project objective is to upgrade the ANN models from a previous research project and install the system on-line in the Rossdale Water Treatment Plant in Edmonton, Alberta, Canada, to forecast raw water colour one day ahead. The article discusses the important issues and techniques to upgrade the neural network model to the actual application. Furthermore, sufficient communication is also required between the designers and the users to address the applicability and user friendly issues in model implementation. Failure in communication can render the whole process ineffective. Possible improvements are also recommended for the future on-line applications. [ABSTRACT FROM AUTHOR]

This paper reports on the application of artificial neural network (ANN) techniques for predicting the concentration of trihalomethanes (THMs) in finished water at the E.L. Smith Water Treatment Plant (WTP) in Edmonton, Alberta, Canada. The formation of THMs in finished water involves many complex chemical reactions and interactions that are difficult to model using conventional methods. The formation of THMs has been found to be correlated to raw and treated water quality characteristics such as colour, pH, and temperature and chemical addition such as chlorine, alum, and powder activated carbon (PAC). Three models were derived using raw water, post clarification water, and a combination of raw and post clarification water parameter inputs. The model that most successfully predicted the concentration of THMs in finished water is the model that uses clarifier effluent parameter inputs. This model can be used at the E.L. Smith WTP for early detection of potentially high THM concentrations in finished water and gives plant operators enough advanced warning to reduce THM precursors. With an adequate understanding of water treatment plant processes and THM formation potential it will be fairly easy for any water treatment facility, which has a few years of historical plant data, to develop its own ANN model for predicting the formation of THM in finished water. [ABSTRACT FROM AUTHOR]