Nicolas Caradot

Bio: Nicolas Caradot is an academic researcher from Institut national des sciences Appliquées de Lyon. The author has contributed to research in topics: Combined sewer & Asset management. The author has an hindex of 10, co-authored 26 publications receiving 262 citations.

Sewer asset management – state of the art and research needs

Abstract: Sewer asset management gained momentum and importance in recent years due to economic considerations, since infrastructure maintenance and rehabilitation directly represent major investments. Becau...

Combined sewer overflows: A critical review on best practice and innovative solutions to mitigate impacts on environment and human health

Abstract: Combined sewer overflows (CSOs) are of major environmental concern for impacted surface waterbodies. In the last decades, major storm events have become increasingly regular in some areas, and mete...

Practical benchmarking of statistical and machine learning models for predicting the condition of sewer pipes in Berlin, Germany

Abstract: Deterioration models can be successfully deployed only if decision-makers trust the modelling outcomes and are aware of model uncertainties. Our study aims to address this issue by developing a set of clearly understandable metrics to assess the performance of sewer deterioration models from an end-user perspective. The developed metrics are used to benchmark the performance of a statistical model, namely, GompitZ based on survival analysis and Markov-chains, and a machine learning model, namely, Random Forest, an ensemble learning method based on decision trees. The models have been trained with the extensive CCTV dataset of the sewer network of Berlin, Germany (115,258 inspections). At network level, both models give satisfactory outcomes with deviations between predicted and inspected condition distributions below 5%. At pipe level, the statistical model does not perform better than a simple random model, which attributes randomly a condition class to each inspected pipe whereas the machine learning model provides satisfying performance. 66.7% of the pipes inspected in bad condition have been predicted correctly. The machine learning approach shows a strong potential for supporting operators in the identification of pipes in critical condition for inspection programs whereas the statistical approach is more adapted to support strategic rehabilitation planning.

Analytical Approaches for Determining Chemical Oxygen Demand in Water Bodies: A Review.

Abstract: Chemical oxygen demand (COD) is a critical analytical parameter for water quality assessment. COD represents the degree of organic pollution in water bodies. However, the standard analytical methods for COD are time-consuming and possess low oxidation efficiency, chloride interference, and severe secondary pollution. Works performed during the last two decades have resulted in several technologies, including modified standard methods (e.g., microwave-assisted method) and new technologies or methods (e.g., electro- and photo-oxidative methods based on advanced oxidation processes) that are less time-consuming, environment friendly, and more reliable. This review is devoted in analyzing the technical features of the principal methods described in the literature to compare their performances (i.e., measuring window, reliability, and robustness) and identify the advantages and disadvantages of each method.

Deep Learning–Based Automated Detection of Sewer Defects in CCTV Videos

Abstract: Automated interpretation of closed-circuit television (CCTV) inspection videos could improve the speed and consistency of sewer condition assessment. Previous approaches focus on defect cla...