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Regular inspection, assessment, and rehabilitation are necessary to maintain the performance levels of sewer pipelines. The primary objective of this research is to develop a causality-based model by incorporating twenty-two sewer defects, including erosion voids. The Decision Making Trial Evaluation Laboratory (DEMATEL) was used to study the causality relationship between the defects, based on a designed questionnaire, to conclude the influencing and influenced distress. The results suggested that 59% of the sewer structural and operational defects were influencing ones. Besides, the existing state of the sewer pipelines was evaluated on a scale from 1 (Excellent) to 5 (Critical) by integrating the DEMATEL technique with the Quality Function Deployment (QFD) method. The QFD-DEMATEL model was implemented on a case study to compare the model’s classifications of the pipelines’ states with the actual ones. Based on the comparison, the average accuracy and true negative rate (TNR) of the classifications were 74% and 79% respectively. Also, the average invalidity percentage (AIP) was 41.32%, causing the validity percentage to exceed 50%. The integrated approach keeps decision-makers informed about their sewer pipelines to prioritize pipelines rehabilitation. Thus, defective sewers are reduced and so are the exfiltration cases. [ABSTRACT FROM AUTHOR]

The growing problem of bridge deterioration globally has imposed prominent challenges on transportation agencies, mainly in terms of ensuring safety and serviceability of the bridge infrastructure. The large number of bridges built during the 20th century has aged and produced a complex decision-making problem that departments of transportation need to deal with. Bridge management, as a particular domain of infrastructure asset management, has focused on developing methods for condition rating and deterioration modeling. The current research reviews bridge inspection practices and identifies the main defects and deterioration signs of concrete bridge decks that are typically captured by Visual Inspection (VI) and Non-Destructive Evaluation (NDE) techniques. The research introduces the Quality Function Deployment (QFD) theory and Weibull Distribution Function (WDF) as an integrated novel method to the area of bridge condition assessment and deterioration modeling. The proposed QFD condition assessment model is developed based on integrating VI and Ground Penetrating Radar (GPR) evaluation results to provide consistent condition ratings and performance predictions. The QFD model is demonstrated with a real case study and compared to other condition assessment models. Moreover, the QFD method is validated with data extracted from twenty bridge inspection reports completed by bridge inspectors and assessed by bridge experts. The developed deterioration curves using the reliability function for the Weibull distribution show absolute matching in these results through predicting the structure future performance and defining its useful service life. Accordingly, these models can enhance bridge Maintenance, Repair and Replacement (MRR) decisions since they produce reliable condition ratings and predictions that can link to proper rehabilitation action, and eventually assist in the decision making and planning for the selected MRR action. All these processes are integrated within one framework. [ABSTRACT FROM AUTHOR]

Abstract Ground-penetrating radar (GPR) is one of the most commonly used technologies for condition assessment of concrete bridge decks. However, there have been no fully automated algorithms to visualize the data collected with this technique. In such context, the current paper presents a method for a full automation of GPR data visualization and analysis. Based on the background removal, depth correction, synthetic aperture focusing technique (SAFT), and interpolation algorithms, this automated method produces a plan view map of amplitude of GPR signals. In the obtained map, two types of information are observed at the same time. First, as the strongest reflectors of electromagnetic energy, rebars will appear as the most visible. Second, the areas of corrosive environment and, thus, likely corrosion, will be detected as having low amplitude rebar reflections. As a proof of concept, the proposed method was implemented for two bare concrete bridge decks and two concrete bridge decks with asphalt overlays. In all cases, the results obtained were excellent where the maps pinpointed the areas affected by corrosion. These areas were confirmed by other methods of evaluation, such as electrical resistivity (ER), half-cell potential (HCP), chloride analysis of core samples, or visual inspection. With the demonstrated performance, the proposed method is expected to be an excellent alternative to the available methods of GPR data evaluation and visualization. In the future, it should be improved to provide an indication of corrosion severity/probability at each deck location. [ABSTRACT FROM AUTHOR]

Spalling is a significant surface defect that can compromise the integrity and durability of concrete structures. The detection and evaluation of spalling are predominantly conducted on the basis of visual inspection (VI) techniques. Although these methods can provide substantial inspection data, they are known to be time-consuming, costly, and qualitative in nature. The objective of this paper is to develop an integrated model based on image processing techniques and machine learning to automate consistent spalling detection and numerical representation of distress in subway networks. The integrated model consists of a hybrid algorithm, interactive 3D presentation, and supported by regression analysis to predict spalling depth. First, RGB images are preprocessed by means of a hybrid algorithm to de-noise the image and enhance the crucial clues associated with spalling. Second, a spalling processor is designed to detect distress attributes, thereby providing 3D visualization model of the defect. And third, the depth and severity of spalling distress are measured using a novel regression analysis model in conjunction with image processing techniques in intensity curve projection. The integrated model was validated through 75 images. Regarding the hybrid algorithm, the recall, precision, and accuracy attained, were 91.7%, 94.8%, and 89.3% respectively. The mean, standard deviation of error percentage in spalling region extraction were 11% and 7.1% respectively, while the variance was 25. Also, the regression model was able to satisfactory quantify the spalling depth with an average validity of 93%. The integrated model is a decision support tool, expected to assist infrastructure managers and civil engineers in their future plans and decision making. [ABSTRACT FROM AUTHOR]

• Data of multiple machine intelligent systems were fused to assess the subway condition. • Image processing and machine learning detected and quantified various surface defects. • Monte Carlo simulation was leveraged to estimate the defects' condition indices. • Artificial neural networks and regression analysis attained 0.928 and 0.957 validation performance. • Monte Carlo simulation forecast precision was recorded as 95% percentile. Water intrusion through soil is considered the most significant structural issue and the major cause of concrete degradation in subway networks. An enormous amount of water infiltration may expedite the deterioration mechanisms, such as moisture marks, spalling, scaling, and cracks. These mechanisms can compromise the structural durability and jeopardize people's safety. The condition assessment of concrete infrastructure is predominantly conducted based of visual inspection techniques, which are costly, time-consuming, and error prone. In this research, two main models for the condition assessment of subway networks are proposed. First, image processing techniques and machine intelligent systems are streamlined through successive operations to detect and quantify multiple surface defects automatically. Spatial and frequency domain filters are used to enhance the image clues, in tandem with artificial neural networks (ANNs) and regression analysis (RA) for defect recognition. The Monte Carlo simulation (MCS) is then leveraged to deliver advanced optimization and accurate estimation for each defect's condition index in the subway element. The developed method was implemented on four stations in Montréal subway systems, whereby the performance of ANNs and RA was validated through R2 as 0.928 and 0.957, respectively. Moreover, the MCS forecast precision was recorded as 95% percentile, which proves the efficacy of the developed models. This research provides insights for infrastructure managers about maintenance and intervention plans in order to prioritize their spending policies. [ABSTRACT FROM AUTHOR]

The condition assessment of civil engineering concrete-made structures has been a prevalent research area over the last thirty years as a result of concrete deterioration mechanisms that may compromise the integrity and durability of civil infrastructure. Ground Penetrating Radar (GPR) has been extensively used for the inspection and probing of concrete structures. Nevertheless, a comprehensive state-of-the-art review on the developments of condition assessment of concrete-made structures using GPR in a civil engineering perspective is missing in the literature. This paper aims at addressing the abovementioned limitations and contributes to the body of knowledge by leveraging a mixed methodology approach that incorporates the scientometric and systematic analyses on research work related to the condition assessment of civil constructions. Scopus database is used in this study where the state-of-the-art of GPR-based condition assessment models for civil engineering systems is described in conjunction with models' contributions, shortcomings, methodologies, and critiques. These systems include roads, bridges, structures, concrete elements, tunnels, and other civil engineering disciplines such as underground pipes and treatment plants. Finally, future research directions are recommended to guide the civil engineering community in identifying the crucial topics to be explored in the upcoming years. • Developments of condition assessment of concrete-made structures using GPR were reviewed. • Mixed review method was adopted to provide an in-depth understanding of 105 studies in a civil engineering context. • Civil engineering systems such as roads, bridges, structures, concrete elements, and tunnels are included in this study. • GPR-based condition assessment models' contributions, shortcomings, methodologies, and critiques were described. • Future directions to guide the civil engineering community in identifying crucial topics to be explored are recommended. [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]