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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. [...]

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. [...]

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 [...]

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 [...]

Tower crane layout planning (TCLP) is key to ensure the safety and productivity of construction projects. Previous studies on TCLP mainly focused on the typical in situ construction method, however, overlooking modular integrated construction (MiC). This study aims to develop an integrated planning approach for optimizing the locations of tower cranes and trucks for MiC across the entire construction site. This approach consists of four objective components. First, identifying potential locations for cranes and trucks by adopting a gridding method and an intersection examination algorithm. Second, estimating the crane operation duration. Third, optimizing the crane and truck locations by developing an integer linear programming (ILP) model while considering various constraints, such as the demand fulfillment, the crane's lifting capacity, and the multiple truck parking areas. Lastly, optimizing the distance between cranes to avoid crane blocking on site. The proposed approach is validated via three high-rise residential MiC projects. The results achieve 19.4%–37.9% reductions in cranes' operation durations, as well as carbon emissions from the original plan. Compared with another existing TCLP method made from limited predefined layout plans, a 29.4% improvement is observed. Moreover, three aspects to extend the application of the approach are discussed: (1) the multiple tower crane layout scenario, (2) the impact of the number of truck points, and (3) the trade-offs between computing effectiveness and performance. The approach realizes the objective of TCLP decision-making for multi-crane multitruck MiC throughout the whole construction site. Construction practitioners can use this approach for decision-making which helps enhance the effectiveness of their tower crane layout designs. [ABSTRACT FROM AUTHOR]

Modular integrated construction (MiC) is now widely adopted by industry and governments. However, its fragile and delicate logistics are still a concern for impeding project performance. MiC logistic operations involve rigorous multimode transportation, loading-unloading, and stacking during storage. Such processes may induce latent and intrinsic damage to the module. This damage causes safety hazards during assembly and deteriorates the module's structural health during the building use phase. Also, additional inspection and repairs before assembly cause uncertainties and can delay the whole supply chain. Therefore, continuous monitoring of the module's structural response during MiC logistics and the building use phase is vital. An IoT-based multi-sensing system is developed, integrating an accelerometer, gyroscope, and strain sensors to measure the module's structural response. The compact, portable, wireless sensing devices are designed to be easily installed on modules during the logistics and building use phases. The system is tested and calibrated to ensure its accuracy and efficiency. Then, a detailed field experiment is demonstrated to assess the damage, safety, and structural health during MiC logistic operations. The demonstrated damage assessment methods highlight the application for decision-makers to identify the module's structural condition before it arrives on site and proactively avoid any supply chain disruption. The developed sensing system is directly helpful for the industry in monitoring MiC logistics and module structural health during the use phase. The system enables the researchers to investigate and improve logistic strategies and module design by accessing detailed insights into the dynamics of MiC logistic operations. [ABSTRACT FROM AUTHOR]

Surface cracks are alluded to as one of the early signs of potential damage to infrastructures. In the same vein, their detection is an imperative task to preserve the structural health and safety of bridges. Human-based visual inspection is acknowledged as the most prevalent means of assessing infrastructures' performance conditions. Nonetheless, it is unreliable, tedious, hazardous, and labor-intensive. This state of affairs calls for the development of a novel YOLOv8-AFPN-MPD-IoU model for instance segmentation and quantification of bridge surface cracks. Firstly, YOLOv8s-Seg is selected as the backbone network to carry out instance segmentation. In addition, an asymptotic feature pyramid network (AFPN) is incorporated to ameliorate feature fusion and overall performance. Thirdly, the minimum point distance (MPD) is introduced as a loss function as a way to better explore the geometric features of surface cracks. Finally, the middle aisle transformation is amalgamated with Euclidean distance to compute the length and width of segmented cracks. Analytical comparisons reveal that this developed deep learning network surpasses several contemporary models, including YOLOv8n, YOLOv8s, YOLOv8m, YOLOv8l, and Mask-RCNN. The YOLOv8s + AFPN + MPDIoU model attains a precision rate of 90.7%, a recall of 70.4%, an F1-score of 79.27%, mAP50 of 75.3%, and mAP75 of 74.80%. In contrast to alternative models, our proposed approach exhibits enhancements across performance metrics, with the F1-score, mAP50, and mAP75 increasing by a minimum of 0.46%, 1.3%, and 1.4%, respectively. The margin of error in the measurement model calculations is maintained at or below 5%. Therefore, the developed model can serve as a useful tool for the accurate characterization and quantification of different types of bridge surface cracks. [ABSTRACT FROM AUTHOR]

The construction industry faces significant challenges in measuring and assessing performance effectively. Conventional methods of measuring construction performance have become less effective, prompting a need to adopt non-financial performance measurements. This shift acknowledges the shortcomings of relying solely on financial performance measurement systems. As a result, there has been a substantial increase in research and focus on non-financial performance measurement systems in recent decades. This study focuses on analyzing performance measurement practices and key performance indicators (KPIs) in the construction industry, specifically at the project level. By examining 146 relevant articles, the study offers a thorough overview of various aspects of project performance. In addition to the traditional dimensions of the project management triangle (cost, time, and quality performance), the study emphasizes the importance of considering other dimensions. These include stakeholder performance, safety performance, technology utilization performance, value performance, environmental impact performance, and the application of maturity models. By incorporating these additional KPIs, a more comprehensive and holistic evaluation of project performance can be achieved. This study's findings make notable contributions to the methodological framework of performance measurement in construction projects. By consolidating diverse research sources, the study offers valuable guidance for future research in the field of project performance. Moreover, it provides insights into selecting suitable performance measurement methods, empowering practitioners to effectively assess and manage project performance. [ABSTRACT FROM AUTHOR]