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[Display omitted] • A reliable blockage detection system is realized by incorporating low-power level sensors and 4G telemetry for real-time monitoring and a smart alarming system. • The blockage detection system employs a set of decision rules and time series analysis to distinguish blockage events from normal behavior. • The proposed system achieved a high success rate in detecting blockage events during a field test, with a low false alarm rate. • The system can help reduce the economic and environmental impacts of Sanitary sewer overflows (SSOs) and improve the overall performance of the wastewater network. Sewer blockages and overflows have significant economic and environmental repercussions on communities. Thus, it is crucial to detect and remove sewer blockages prior to the occurrence of overflows. With the improvement in mobile networks and the development of high-quality and low-power sensors and loggers, wastewater network operators can now adopt monitoring devices enabled by the "Internet of Things" (IoT) technology for real-time monitoring. To this end, this paper studies the current state-of-the-art in sewer blockage management and introduces a novel methodology to monitor sanitary sewer blockages to prevent sanitary sewer overflows (SSO) with the least human interaction. The proposed system incorporates low-power level sensors and 4G telemetry for real-time monitoring of the manhole's sewage level to detect sewer blockages. The blockage detection methodology encompasses a set of decision rules and time series analysis to identify blockage events. The final blockage detection model offers a versatile capability to be implemented on sanitary sewer networks of different types with minimum computational costs. [ABSTRACT FROM AUTHOR]

Sewer overflow contains several hazardous contaminants causing adverse health effects and annoyance to the public. Despite this importance, few studies has hitherto been undertaken on examining the odor nuisance and risk of diseases due to contact with untreated overflow. However, quantitative investigation of odor emission from the sanitary sewage overflows has not been addressed. As such, this study aims to scrupulously investigate the deleterious impact of such phenomenon on public health in terms of the aforesaid matters. To this end, a multi-stage methodological approach was employed. Firstly, field data was collected from the vicinity of a wastewater treatment plant for three years, and then the concentrations of H 2 S in the aqueous phase and gaseous phase were estimated based on the environmental parameters. Afterward, the Gaussian aerial dispersion model and Quantitative Microbial Risk Assessment (QMRA) were employed. In parallel, the impact of the exposure to the malodorous H 2 S emitted from overflow cases was assessed. Furthermore, the results obtained from impact assessment were validated using the developed questionnaire survey. From the results obtained, the following major conclusions are drawn: (1) levels of H 2 S (g) near the overflow were high enough to be perceived by individuals, (2) concentrations of NH 3(g) in the ambient air were estimated lower than the perception threshold, (3) the sulfide concentration in the overflow was the most influential parameter with positive linear correlation with the concentration of H 2 S (g) , (4) the concentration of odor causes high annoyance, according to the questioning from the residents near the overflow events (5) exponential dose-response indicated 89–95% infection risk and (6) the good correlation between the estimated values of annoyance and the real annoyance level perceived by the residents proved accuracy of the methodology for estimation of H 2 S concentration and annoyance level. The unique findings obtained from this study guide the environmental decision-makers to take pre-emptive actions, preventing risk of infection and complaints from the residents. • A dispersion model is developed based on the wastewater and climate data. • A predictive-based model is developed to evaluate the annoyance of the odor from the sanitary overflows. • Initial sulfide concentration is the most significant parameter affecting the odor from an overflow. • The infection risk from the exposure to the sanitary overflows is assessed. [ABSTRACT FROM AUTHOR]

Municipal decision-makers struggle to reach effective solutions in asset management. The complexity of this issue increases when trying to consider more than one asset. This article proposes an approach that can optimize integrated decision-making for road, sewer, and water municipal assets. The proposed optimization is accomplished using risk assessment and a performance rating model. The model was tested on the infrastructure network of the city of Guelph and has resulted in a 36% risk dollar reduction under full integration. The model has also shown improved budget-savings when compared to the conventional silos approach. The findings aid municipal decision-makers in maximizing the benefits of their limited budgets, which will increase the number of infrastructure projects. Additionally, the results of the proposed model unveil more opportunities. Specifically, the use of more advanced computational methods along with exploring techniques that allow the user to consider multiple corridors within one repair scenario. • Novel approach for integrated infrastructure repair optimization. • Optimization via risk and performance-based assessments. • Integrating road, water, and sewer municipal infrastructure. • Provided 25% risk reduction in a real-life case study. [ABSTRACT FROM AUTHOR]

• A novel Exfiltration Severity Index (ESI) is proposed to assess sewer pipelines. • A two-tiered model is applied to predict the exfiltration occurrence and severity. • GridSearchCV is employed to optimize the accuracy of the prediction models. • Shapley analysis is utilized to assess the factors affecting sewer exfiltration. Effective management of aging sewer pipelines requires accurate analysis of sewer pipeline exfiltration. Previous research studies have not paid attention to proposing an index to represent the exfiltration severity. To this end, this study introduces a novel Exfiltration Severity Index (ESI) by considering the frequency and severity of defects captured from Closed-Circuit Television (CCTV) reports. To address the need for a proactive tool that automates the exfiltration estimation and reduces dependence on CCTV reports, we leverage Machine Learning (ML) and Deep Learning (DL) models to predict sewer exfiltration occurrence and severity. In this regard, our proposed methodology comprises a series of steps that start by computing the ESI of pipeline segments considering the frequency, type, and severity of defects. After that, physical, environmental, and climatic factors influencing pipeline exfiltration are gathered and aggregated to build predictive models. We compare the performance of six ML models and two DL models, developed in two tiers to predict exfiltration occurrence and severity, respectively. The hyperparameters of each model are optimized using GridSearchCV to enhance prediction accuracy. Among the eight algorithms, the light gradient-boosting machine performs best, with 71% and 85% accuracy in the first and second tiers, respectively. Furthermore, our study investigates the influence of various factors on pipeline exfiltration and reveals that pipe diameter and population have the most significant impact on exfiltration occurrence and severity. Our method provides a valuable tool for managing sewer pipeline exfiltration and can be utilized to prioritize sewer network maintenance and repair efforts. [ABSTRACT FROM AUTHOR]

One of the main components of smart and sustainable urban management is well-performed sewer networks. Therefore, maintaining and enhancing the functionality of sewer projects at an affordable cost are major challenges for decision-makers. However, the embracement of the Value Engineering (VE) concept to assess new sewer designs has not yet been fully embraced especially in developing countries. Thus, this study introduces a systematic VE approach for major sewer projects. A computer model based on six phases of VE methodology was developed, and named "Value Engineering Model" (VEM). To show its efficacy, it was applied to a real-life case study, leading to the following major contributions to the body of relevant knowledge: (1) unraveling the logical relations between the project functions, (2) a detailed life cycle cost analysis (LCCA), (3) a weighted evaluation matrix towards facilitating the decision-making procedure and (4) achieving an approximate 36% reduction in the sewer's project cost as compared to the common VE analysis technique. The developed model serves as a guide for design engineers and decision-makers in sewer projects. It will enable achieving the embracement of smart and sustainable drainage systems within cities at a greater pace. The results of this study will be a guide for decision-makers to reduce costs and improve sustainability by introducing VEs in the Egyptian sewer industry. • A new inclusive value engineering model for major sewer projects is developed. • The logical relations between the project functions are unravelled. • A detailed life cycle cost analysis (LCCA) of sewer projects is provided. • A weighted evaluation matrix towards facilitating the decision-making procedure is developed. • An approximate 36% reduction in the sewer's project cost is achieved. [ABSTRACT FROM AUTHOR]

The present study proposes a novel hybrid methodological approach for meticulously investigating the factors causing Sewer Overflow (SO). The proposed framework is based on a systematic literature review, experts' interviews, PLS-SEM statistical technique, and system dynamic modeling. Based on a large number of data collected from experts having rich experience in developed countries, three major findings are obtained: (1) three main factors and eighteen sub-factors are the main culprit of SO occurrence, (2) under design pipe diameter, blockages, and infiltration and inflow are the most significant sub-factors within the respective clusters, and (3) physical-related sub-factors are the most influential causes of SO occurrence under a dynamic environment. The findings attained in this study offer an insightful account for the concerned environmental decision-makers on coming up with further fecund measures towards reducing the magnitude of SO, preserving our environment from the occurrence of such harmful incidents. [Display omitted] • Through extensive literature review and experts' interviews, the contributors to sewer overflow are identified. • Through the PLS-SEM, the significance of identified contributors is quantified. • Using the System Dynamic Modeling, complex interrelationships among the contributors are uncovered. • Several theoretical and managerial implications are offered for the concerned decision-makers. [ABSTRACT FROM AUTHOR]

A smart and sustainable drainage system is strongly dependent on appropriate functioning of sewer pipelines existing within the infrastructure of cities' networks. Although numerous studies have been conducted on the sewer networks, the existing body of literature lacks a comprehensive review paper that contains scientometric analysis, critical review, and meta-analysis in one holistic frame. To fill this gap, this paper provides a review of sewer deterioration models and factors causing sewer pipeline deterioration through a hybrid review approach. Having meticulously reviewed the studies undertaken hitherto using the adopted hybrid review approach, the following conclusions are noted: (1) The meta-analysis results indicate that the most important factors impacting sewer pipeline deterioration include operational defect, pipe shape, pipe material, waste type, structural defect, and hydraulic conditions, (2) Factors and defects that cause sewer deterioration have a varying degree of impact on the deterioration process; a good understanding of their individual impact will improve the sewer infrastructure performance and save cost, and (3) Countries from Africa, South America, the Middle East, and Asia are lagging in this research area, although there might be some justification for this, such as availability of funds and research priority. Sewer infrastructure is an important part of the environment, and keen attention needs to be given to it; therefore, there is a need for more international research collaborations. The findings obtained from this review paper are expected to facilitate the adoption of sagacious policies by the concerned decision-makers, paving the way for realizing smarter and more sustainable cities. • An inclusive scientometric analysis on the papers published on sewer pipeline deterioration is undertaken. • An in-depth discussion on the methods used in modeling sewer pipeline deterioration is provided. • Critical factors playing role in the deterioration of sewers networks are identified. • The significance levels of identified critical factors are unraveled using meta-analysis approach. • Current research gaps together with the potential research streams to be undertaken are put forward. [ABSTRACT FROM AUTHOR]

In order to achieve an efficient and a successful operation and maintenance plan for assets, management personnel should have detailed information on the condition of the assets to make informed strategic decisions and properly plan expenditure of capital investments. Condition assessment models for sewage pipelines can be considered as a helpful tool to achieve such objective and from which a decision regarding the required and appropriate intervention can be made. This paper presents a review for the different physical, Artificial Intelligence and statistical models that have been developed to assess the condition of sewage pipelines over a period from 1998 through 2019. The description of different techniques used in building the condition assessment models, and the data required to construct these models are presented. In addition, the major disadvantages and limitations of using these techniques in developing the models have also been discussed. The conducted literature review indicates that various condition assessment models were capable of precisely forecasting the future condition of sewer pipelines. Most of the developed assessment models have been validated with various identified techniques to ensure the adequacy of the predictions. The main problem in model development arises from data availability and liability as several factors were identified by researchers to impact the deterioration of sewer pipelines. In order to overcome this problem, municipalities must utilize the new emerging technologies to facilitate gathering the required dataset in a complete and precise manner. Also, certain techniques such as evidential reasoning or Bayesian Belief Network can be used due to their capabilities in dealing with missing data. Furthermore, the influence of the factors on the pipe condition were identified by some researchers. Although there were discrepancies in the findings, but the majority concluded that both age and material factors have high influence and pipe slope has low influence. • This paper reviews the models for assessing the condition of sewage pipeline. • Sewer pipelines deteriorate by physical, operational and environmental factors. • For assessment, physical, statistical and artificial intelligence models are used. • A combination of assessment models and regular inspection is essential. [ABSTRACT FROM AUTHOR]