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Artificial Intelligence (AI) techniques, such as Artificial Neural Networks (ANN) and Adaptive Neuro-Fuzzy Inference Systems (ANFIS), and dimensional analysis-based empirical equations (DAEEs), can estimate scour depth around bridge piers. AI's accuracy depends on various architectures, while DAEEs' performance depends on experimental data. This study evaluated the performance of AI and DAEEs for scour depth estimation using flow velocity, depth, size of bed sediment, critical approach velocity, and pier width. The data from a smooth rectangular (20 m × 1 m) flume and a high-precision particle image velocimetry to study the flow structure around the pier - width: 1.5 – 91.5 cm evaluated DAEEs. Various ANNs (5, 10, and 15 neurons), double layer (DL) and triple layers (TL), and different ANFIS settings were trained, tested, and verified. The Generalized Reduced Gradient optimization identified the parameters of DAEEs, and Nash–Sutcliffe efficiency (NSE) and Mean Square Error (MSE) evaluated the performance of different models. The study revealed that DL ANN-3 with 10 neurons (NSE = 0.986) outperformed ANFIS, other ANN (ANN1, ANN2, ANN4 & ANN5) models, and empirical equations with NSE values between 0.76 and 0.983. The study found pier dimensions to be the most influential parameter for pier scour. [ABSTRACT FROM AUTHOR]

Fractal dimension represents the geometric irregularity of an object with respect to the underlying space and is used for several characterizations. The divider method and the box counting method are two classical methods to compute the fractal dimension of fractals, coastlines, natural objects and other complex systems. In this work, we present a novel, extremely efficient algorithm based on the fractal interpolation function (FIF) method for estimating the fractal dimension of coastlines and for reconstructing the coastline geometry. The algorithm is implemented for the coastline of the Kingdom of Saudi Arabia (KSA) as a case study. For validating the accuracy of the proposed algorithm in estimating the fractal dimension we compare our results with those obtained using the divider and the box-counting method. We also reconstruct the coastline geometry of KSA using our algorithm which generates functions (interpolants) that matches the coastline geometry very accurately. Numerical simulations are obtained using a robust, parallel multi-processing library, an R - program, Python codes, a dynamic programming algorithm, binary search algorithm and the QGIS software. KSA coastline geometry, methodology and flowchart of the proposed FIF algorithm [ABSTRACT FROM AUTHOR]

Conventional interview surveys for assessing customer satisfaction require supplementary resources and yield a low response rate. Covering fewer customers who generally face service interruptions is another limitation that hinders the identification of the root causes of failure. The survey findings might mislead the utilities by showing unrealistic satisfaction levels. Customer dissatisfaction risk subjects to the time between the complaint registration and the utility response pending full resolution of the issue. In the present research, bowtie analysis (BTA) reveals an innovative synergetic correlation between system reliability and customer satisfaction with water utilities. Using complaints reported due to service failures, the fault tree analysis (FTA) assesses the reliability of system components (pressure, water quality, and structure). On the opposite side of the bowtie, event tree analysis (ETA) evaluates the efficiency of the utility response and translates it into customer dissatisfaction risk. Exploring over 1500 registered work orders in a utility operating in British Columbia (Canada) reveals that 80% of complaints originated from structural (primarily service connections) issues. Simulating the BTA for five improvements showed that economical proactive maintenance and an efficient complaint response mechanism improve the system reliability from 58 to 86% and reduce the cumulative risk by 62%. The remaining risk reduction requires expensive improvements, e.g., source change and conventional treatment installation, which require customer approval based on willingness to pay. The study reveals that a complete bowtie analysis effectively integrates both the FTA and ETA and provides a novel customer-driven decision-support tool for water supply systems and other elsewhere applicable. [ABSTRACT FROM AUTHOR]

Water utilities in arid regions deal with multifaceted issues of natural groundwater contamination, high treatment costs, and low water rates. These utilities rely on intermittent supplies resulting in numerous water quality failures at source, treatment, distribution, and in-house plumbing systems. The present research presents an inclusive risk assessment methodology for managing water quality from source to tap. Three-year monitoring data for turbidity, TDS, pH, iron, ammonia, nitrates, residual chlorine, Coliform group, E. coli, and Fecal Streptococci identified the root causes of failures. The cause-effect relationships in the form of a fault tree were solved using multiple failure modes and effect analysis (FMEA) to handle both the Boolean operations. The fuzzy sets addressed the uncertainties associated with data limitations in calculating exceedance probabilities (Pe) and vagueness in expert opinion for subjective evaluation of severity and detectability. The methodology was applied on a smaller system serving 18,000 consumers in Qassim, Saudi Arabia. Potable supplied water underwent reoccurrence of TDS (Pe = 20%), turbidity (Pe = 10%), and Fe (Pe = 2%) failures in distribution that further increased up to 44%, 33%, and 11% at the consumer end. The Pe for residual chlorine failure soared up to 89%. Economic controls reduced the cumulative risk to 50%, while the shift to continuous supply can limit the remaining failures under the acceptable risk. The framework will help utilities manage water quality in intermittent systems from source to tap in Saudi Arabia, the Gulf, and elsewhere. [ABSTRACT FROM AUTHOR]