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Wall thickness loss in water pipes has been found to be positively correlated with water pipe failure. The reliability of water pipes reduces as their wall thickness loss increases. Although previous studies have investigated pipe failure modeling using historical failure data, however, indirect failure modeling via wall thickness loss is yet to be explored. Hence, this study develops machine learning (ML) models to predict wall thickness loss in water pipes. Random Forest (RF) and Gradient Boosting Machine (GBM) are chosen as the base models and are integrated with Bayesian Optimization (BO) algorithm for hyperparameters selection. The predictive models are evaluated using root mean square error (RMSE), mean absolute error (MEA), mean absolute percentage error (MAPE), and coefficient of determination (R2). Based on the evaluation metrics, the hybrid models (i.e., RF+ BO and GBM+BO) outperformed the base models (RF and GBM), showing the importance of the systematic selection of hyperparameters. The best model (RF + BO) achieved an RMSE, MAE, MAPE, and R2 value of 3.212, 2.494, 11.506, and 0.910, respectively. These metrics show the high predictive capability of the model, which can be used by water infrastructure management to predict wall thickness loss in water pipes. [ABSTRACT FROM AUTHOR]

The rise in percentage of deteriorated bridges coupled with the escalation of maintenance costs call for systematic bridge management systems. Therefore, this research paper introduces an exponential chaotic differential evolution (ECDE) model for optimizing bridge maintenance plans at both project and network levels. The developed optimization model is envisioned on addressing the performance, economic, social, and environmental aspects of bridge maintenance management. In addition, it exploits the use of chaotic sequences to circumvent the critical shortcomings of local minima entrapment and premature convergence encountered by classical optimization metaheuristics. The validity of the developed model is experimented using a comparative analysis of a wide set bridge elements and over a varying multi‐year maintenance plan. Comparison results reveal that ECDE‐based Sinusoidal algorithm improve the performance diagnostics of classical meta‐heuristics by values ranging from 49.2% to 73.1% over the multi‐year maintenance plans. It can be argued that the developed model could benefit transportation agencies in formulating flexible, sustainable, and cost‐effective maintenance strategies in addition to preserving the structural condition of designated bridges in the network. [ABSTRACT FROM AUTHOR]

Bridges all over the world are vulnerable to severe deterioration agents meanwhile their maintenance budgets are being tightened. This state of affairs necessitates the establishment of an autonomous deterioration model to predict the performance condition of bridges. This research paper explores the implementation of a set of intelligent data driven models to analyze the future condition ratings of bridge decks. These models comprise support vector machines, Gaussian process regression, regression tree, back propagation artificial neural network, Elman recurrent neural network, cascade forward neural network, long short‐term memory network and deep convolutional neural network. The performance comparison is carried out using five evaluation metrics of root mean squared percentage error, mean absolute percentage error, root mean squared error mean absolute error and relative absolute error. The models herein are developed and validated using the structural deterioration information retrieved from the National Bridge Inventory (NBI). It can be argued that the developed deterioration model could be implemented by departments of transportation to analyze and monitor the performance condition behavior of bridge components over their useful lifetime. [ABSTRACT FROM AUTHOR]

Effective water pipeline leak detection and localization can help water practitioners save water losses and reduce pipe rehabilitation budgets. Different sensing technologies can provide essential data aiding model development for predicting future leaks in the water distribution networks. Solutions for applying different sensing technologies to real water distribution networks are being developed for real‐time data collection, precise leak localization, reducing false alarms, and continuous pipeline monitoring. Experiments of different scales and specifications have been conducted to test the application and effectiveness of sensing technologies for leak diagnosis in water pipelines. Quality data collection is especially crucial for the successful implementation of these experiments. However, practitioners need help designing effective leak detection and localization experiments, wasting time, and resources. There needs to be a greater understanding of the design parameters for more accurate testing, such as sensor selection, sensor placement, ambient noise sources, and system operations. The current study, therefore, delineates design parameters for design leak diagnosis experiments for lab, testbed, and real networks. For application in real water networks, expert opinion is used to help identify the benefits and limitations and the implementation issues of available sensing technologies. An example experiment design using multi‐sensing technologies for smart leak diagnostics in Hong Kong is also presented. The study can guide water practitioners and researchers to design a multi‐sensing leak diagnostic experiments enabling a smart and sustainable utility management. This article is categorized under:Engineering Water > MethodsEngineering Water > Sustainable Engineering of WaterScience of Water > Water Extremes [ABSTRACT FROM AUTHOR]

Failures of water distribution networks (WDNs) are rising at an exponential rate, necessitating immediate attention. An effective way to reduce the failure rate is to develop accurate predictive models for the failure probability of water pipes, which are the most critical assets of WDNs. Despite the fact that researchers have invested efforts to develop various predictive models, the extant literature lacks a complete state‐of‐the‐art review. To fill this gap, this study employs a mixed approach (i.e., quantitative and qualitative) by providing (a) a bibliometric analysis of existing scholarly literature, (b) a systematic review of the techniques used in modeling the failure probability of water pipes, including physical, statistical, and machine‐learning (ML)‐based models, and (c) identified gaps and future research directions. The bibliometric analysis shows that ML‐based models are emerging and, hence understudied as compared to the physical and statistical‐based models. Regarding the systematic review, a proper understanding of the development of each model has been provided in addition to their advantages and critiques. Furthermore, failure probability integration methods are discussed. Findings reveal that the social and operation‐related predictors have been understudied, thereby suggesting their further exploration. This study adds to the existing body of knowledge by providing water utilities and academics with a comprehensive understanding of the probability of water pipe failure, which will be useful in the decision‐making process and network management. Plain Language Summary: This study is motivated by the lack of a comprehensive review of existing models in predicting the failure probability of water pipes. In order to fill this gap, this study conducts a bibliometric and systematic review by critically analyzing the physical, statistical, and machine learning‐based models in the literature. A frequency analysis of the factors used in the development of the models was conducted, to determine the most influential factors. This research provides a complete reference for water utilities and academics on the prediction of water pipe failure, which would be useful in the decision‐making process. Key Points: The state‐of‐the‐art of failure modeling of water pipes is demonstratedA conceptual framework for decision‐making process in water utility management is presentedKey future directions for achieving more sustainable water distribution networks are discussed [ABSTRACT FROM AUTHOR]

Background: Management of facial skin cancer and its complications is important research topics needing continuous update to improve the outcome. Objective: The study is to share our findings with surgeons and healthcare providers. The authors provide their efforts by pooling data from multiple institutions; as reporting surgical outcomes is significantly lacking and much needed in the Middle East and North Africa region in order to meaningfully improve quality of care. This study proposes an algorithm for management that could aid a surgical decision‐making for reconstruction of defects after excision of facial skin cancer. Methods: Retrograde simple descriptive analysis study is conducted for multicenter data about management of facial skin cancer and its cosmetic outcome. The analysis involves 159 male patients and 95 females. Results: Nonmelanoma skin cancer was reported in 250 (98.4%) of 254 cases. Reconstructive procedures were complicated in 16 cases (~6.3% of the study). Skin cancer recurrence in head and neck has happened in five cases (~1.9% of the study). Flaps used survived without major complications; however, V‐Y advancement flaps showed the best aesthetic outcome. Conclusion: This study reports data in order to meaningfully improve the quality of care. Disease incidence, reconstructive complications, recurrences, and aesthetic outcome of facial skin cancer are included in the study. Based on the data pooling, the study proposes a simple treatment algorithm that could aid in surgical decision‐making. V‐Y advancement flaps showed the best aesthetic outcome. [ABSTRACT FROM AUTHOR]