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Optimizing the inspection route for a bridge network is a paramount factor in reducing inspection duration and cost, particularly for bridges located in a large geographical area. In this respect, this study presents a bridge network inspection planning model. The model aims to minimize the total inspection cost by reducing traveling distance, accommodation cost, wasted time, and inspection crew cost. The model was developed based on the main concept of the multiple traveling salesman problem. In this model, a discrete event simulation engine was built to estimate the inspection duration for each bridge in the network, whereas the genetic algorithm approach was used to optimize the inspection route. Python was used in coding the model steps. Web scraping technique was utilized to develop a geospatial information algorithm dedicated to extracting the actual driving distance between any two points in the inspection route. To tackle the limitations of previous models, the developed model considered several parameters, such as bridge inspection durations that are either shorter or longer than a day shift, variations in the accommodation fees, actual driving distance, minimum workload to assign a new inspection crew, and minimum time to allow starting inspection activities in a new bridge. Considering these parameters make the developed model comprehensive and more accurate. The model was tested against a real bridge network in the IL, USA, and it proved its effectiveness in optimizing the inspection route. The model provides strong guidance for consultants and authorities in charge of bridges in planning the upcoming inspection activities. Key words: bridge inspection routing problem, optimization, genetic algorithm, discrete event simulation, decision Support systems, 1. Introduction Bridges are vital transportation routes that connect our world and facilitate goods shipping and people movements. Unfortunately, these structures are exposed to severe environmental conditions, pollution, accidents, etc. [...]

The construction industry has long struggled with budget and time overruns, quality inconsistencies, lack of data and information, stagnant productivity, occupational health and safety risks, claims and disputes, and other [...]

Oil and gas pipelines transport millions of dollars of products every day making the optimization of their long-term maintenance strategies an essential target for practitioners. Thus, this paper aims to optimize the maintenance plan of such pipelines by maximizing their lifetime average condition with the minimum possible cost. This is achieved in three steps: (1) developing a life-cycle cost model for such pipelines to determine their net present value (NPV) based on the different rehabilitation actions applied throughout their lifetime; (2) establishing a strategy to determine the condition index of such pipelines before and after any rehabilitation action applied throughout their lifetime; and (3) formulating the optimization model and applying the elitist non-dominated sorting genetic algorithm (NSGA-II) to determine the optimum maintenance plans for such pipelines. The optimization model is applied to a real-life case study to demonstrate its applicability. Key words: oil and gas pipelines, optimization, NSGA-II, life-cycle cost, condition index. Les oleoducs et gazoducs transportent des millions de dollars en produits chaque jour, faisant de sorte que l'optimisation des strategies d'entretien a long terme de ceux-ci soit une cible essentielle pour les praticiens. Ainsi, cette etude vise a optimiser le plan d'entretien de ces pipelines en maximisant leur etat moyen a vie au cout minimum possible. Cela se fait en trois etapes : (i) elaborer un modele de cout du cycle de vie pour ces pipelines afin de determiner leur valeur actualisee nette (VAN) en fonction des differentes mesures de rehabilitation appliquees tout au long de leur duree de vie; (ii) etablir une strategie pour determiner l'indice d'etat de ces pipelines avant et apres toute mesure de rehabilitation mise en oeuvre au cours de leur duree de vie; et (iii) formuler le modele d'optimisation et appliquer l'algorithme genetique NSGA-II (<< non-dominated sorting algorithm II >>) afin de determiner les plans d'entretien optimaux de ces pipelines. Le modele d'optimisation est utilise dans le cadre d'une etude de cas reelle pour demontrer son applicabilite. Mots-cles : oleoducs et gazoducs, optimisation, NSGA-II (<< non-dominated sorting algorithm II >>), cout du cycle de vie, indice d'etat., Introduction Pipelines are considered the safest and the most efficient way to transport oil and gas products (Kennedy 1993). However, their failure can cause significant social, environmental, and financial consequences. [...]

Leakages in water distribution networks (WDNs) cause economic losses and environmental hazards. It is, therefore, unsurprising that water-leak management has been a focus of research over the last couple of decades, but leaks in WDNs still occur frequently. Thus, this domain is experiencing a transformation from traditional signal processing and statistical-based models to artificial intelligence (AI) based models for recognizing complex leak patterns, handling large datasets, and establishing accurate leak-management models, especially in leak detection and localization. However, a comprehensive review of the application of AI in water-leak management is largely missing from the literature. To bridge this gap, this review presents a criteria-based critical review to systematically investigate the existing literature on the application of AI in four sub-domains of leak management including leak detection, localization, prediction, and sizing. The first criterion (research attributes) established the (1) research trends, (2) links between influential countries and sources, and (3) popular keywords using scientometric analysis. The systematic analysis of the second criterion (research technicality) and the third criterion (research focus) revealed the (1) AI-techniques adopted, (2) equipment used for collecting data, (3) data features used in the models, (4) objectives of different models adopted, (5) type of experiments conducted to collect the data, and (6) types of pipes for which models were developed. The study highlighted research gaps, future research directions, and proposed a leak management framework for upcoming AI studies in this domain. This review is intended to serve early researchers by enhancing their understanding of existing research in AI-based leak management as well as seasoned researchers by providing a platform for future research. Key words: artificial intelligence, water-leak management, criteria-based review. Lesfuitesdans les reseaux de distribution d'eau entrainent des pertes economiques et des risques environnemen-taux. Il n'est donc pas surprenant que la gestion des fuites d'eau ait suscite l'interet les chercheurs au cours des deux der-nieres decennies. Toutefois, des fuites surviennent encore frequemment et par consequent, pour reconnaitre les patrons complexes de fuite, traiter de grands ensembles de donnees et etablir des modeles precis de gestion des fuites, ce domaine est en train de passer de modeles traditionnels bases sur le traitement des signaux et les statistiques a des modeles bases sur l'intelligence artificielle (IA) au cours des dernieres annees, notamment pour la detection et la localisation des fuites. Cependant, un examen complet de l'application de l'IA dans la gestion des fuites d'eau fait largement defaut dans la littera-ture. Pour combler cette lacune, cet article presente un examen critique fonde sur des criteres afind'examiner systemati-quement la litterature existante sur l'application de l'IA dans quatre sous-domaines de la gestion des fuites, notamment la detection, la localisation, la prediction et le dimensionnement des fuites. Le premier critere <> a permis d'etablir (1) les tendances de la recherche, (2) les liens entre les pays et les sources d'influence et (3) lesmotscles populaires selon l'analyse scientometrique. L'analyse systematique du deuxieme critere (technicite de la recherche) et du troisieme critere (orientation de la recherche) a revele (1) les techniques d'IA adoptees, (2) l'equipement utilise pour recueil-lir les donnees, (3) les caracteristiques des donnees utilisees dans les modeles, (4) les objectifs des differents modeles adoptes, (5) le type d'experiences menees pour recueillir les donnees et (6) les types de tuyaux pour lesquels les modeles ont ete developpes. L'etude a mis en evidence les lacunes de la recherche, les orientations futures de la recherche et a propose un cadre de gestion desfuitespourles etudes d'IA a venir dans ce domaine. Cette synthese vise a servir les chercheurs en debut de carriere en ameliorant leur comprehension des recherches existantes dans le domaine de la gestion des fuites par l'IA, ainsi que les chercheurs experimentes en leur fournissant une plate-forme pour les recherches futures. [Traduit par la Redaction] Mots-cles: intelligence artificielle, gestion des fuites d'eau, examen base sur des criteres., Introduction Access to freshwater is indispensable for economic and social prosperity. Changing consumption patterns due to rapid urbanization and industrialization as well as increasing population levels are putting stress on [...]