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Water Loss (WL) is a global issue. In Italy, for instance, WL reached 36.2% of the total fresh water conveyed in 2020. The maintenance of a water supply system is a strategic task that requires a huge amount of investment every year. In this work, we focused on the use of Distributed Fiber Optic Sensors (DFOS) based on Stimulated Brillouin Scattering (SBS) technology for monitoring water pipeline networks. We worked on High-Density Polyethylene (HDPE) pipes, today the most widely used for creating water pipelines. By winding and fixing the optic fiber cable on the pipe’s external surface, we verified the ability to detect strain related to pressure anomalies along a pipeline, e.g., those caused by water leakage. We performed two experimental phases. In the first one, we assessed the sensibility of sensor layout on an HDPE pipeline solicited with static pressure. We investigated the viscoelastic rheology of the material by calibrating and validating the parameters of a Burger model, in which Maxwell and Kelvin-Voigt models are connected in series. In the second experimental phase, instead, we focused on the detection of the pressure anomaly produced by leakage in a pipeline circuit set up with running water moved by a pump. The theoretical and experimental studies performed returned overall positive feedback on the use of DFOS for the monitoring of HDPE water pipelines. Future developments will be focused on more detailed studies of this monitoring solution and on the industrial production of “natively smart” HDPE pipes in which DFOS cables are integrated into the pipeline surface during the extrusion process.

A smart, safe, and efficient management of water is fundamental for both developed and developing countries. Several wireless sensor networks have been proposed for real-time monitoring of drinking water quantity and quality, both in the environment and in pipelines. However, surface fouling significantly affects the long-term reliability of pipes and sensors installed in-line. To address this relevant issue, we presented a multi-parameter sensing node embedding a miniaturized slime monitor able to estimate the micrometric thickness and type of slime. The measurement of thin deposits in pipes is descriptive of water biological and chemical stability and enables early warning functions, predictive maintenance, and more efficient management processes. After the description of the sensing node, the related electronics, and the data processing strategies, we presented the results of a two-month validation in the field of a three-node pilot network. Furthermore, self-powering by means of direct energy harvesting from the water flowing through the sensing node was also demonstrated. The robustness and low cost of this solution enable its upscaling to larger monitoring networks, paving the way to water monitoring with unprecedented spatio-temporal resolution.

As a result of the progressive implantation of the Industry 4.0 paradigm, many industries are experimenting a revolution that shipyards cannot ignore. Therefore, the application of the principles of Industry 4.0 to shipyards are leading to the creation of Shipyards 4.0. Due to this, Navantia, one of the 10 largest shipbuilders in the world, is updating its whole inner workings to keep up with the near-future challenges that a Shipyard 4.0 will have to face. Such challenges can be divided into three groups: the vertical integration of production systems, the horizontal integration of a new generation of value creation networks, and the re-engineering of the entire production chain, making changes that affect the entire life cycle of each piece of a ship. Pipes, which exist in a huge number and varied typology on a ship, are one of the key pieces, and its monitoring constitutes a prospective cyber-physical system. Their improved identification, traceability, and indoor location, from production and through their life, can enhance shipyard productivity and safety. In order to perform such tasks, this article first conducts a thorough analysis of the shipyard environment. From this analysis, the essential hardware and software technical requirements are determined. Next, the concept of smart pipe is presented and defined as an object able to transmit signals periodically that allows for providing enhanced services in a shipyard. In order to build a smart pipe system, different technologies are selected and evaluated, concluding that passive and active RFID (Radio Frequency Identification) are currently the most appropriate technologies to create it. Furthermore, some promising indoor positioning results obtained in a pipe workshop are presented, showing that multi-antenna algorithms and Kalman filtering can help to stabilize Received Signal Strength (RSS) and improve the overall accuracy of the system.

Several millions of kilometres of pipes and cables are buried beneath our streets in the UK. As they are not visible and easily accessible, the monitoring of their integrity as well as the quality of their contents is a challenge. Any information of these properties aids the utility owners in their planning and management of their maintenance regime. Traditionally, expensive and very localised sensors are used to provide irregular measurements of these properties. In order to have a complete picture of the utility network, cheaper sensors need to be investigated which would allow large numbers of small sensors to be incorporated into (or near to) the pipe leading to so-called smart pipes. This paper focuses on a novel trial where a short section of a prototype smart pipe was buried using mainly off-the-shelf sensors and communication elements. The challenges of such a burial are presented together with the limitations of the sensor system. Results from the sensors were obtained during and after burial indicating that off-the-shelf sensors can be used in a smart pipes system although further refinements are necessary in order to miniaturise these sensors. The key challenges identified were the powering of these sensors and the communication of the data to the operator using a range of different methods.

This chapter examines the economic impacts of service‐based discrimination on the market for telecommunication services. It analyzes a market with discrete consumer types, who may or may not be uniformly ordered with regards to their preferences for various services. The chapter examines two models, each with two types of consumers. In the first model, consumer preferences are uniformly ordered across services. In the second one, this ordering is not uniform. For these two models, the chapter compares all three surpluses‐producer, consumer, and social‐under smart pipes with that under dumb pipes. The chapter identifies circumstances in which smart pipes surprisingly lead to a higher consumer surplus. It sheds light on the issue of neutrality with respect to different services that use the same communication network. Finally, the chapter discusses why the wisdom that smart pipes increase profits may not hold when consumer types are ordered nonuniformly.

The transportation of oil and gas through pipelines is an integral aspect of the global energy infrastructure. It is crucial to ensure the safety and integrity of these pipelines, and one way to do so is by utilizing an inspection tool called a smart pig. This paper reviews various smart pigs used in steel oil and gas pipelines and classifies them according to pipeline structure, anomaly-detection capability, working principles, and application areas. The advantages and limitations of each sensor technology that can be used with the smart pig for in-line inspection (ILI) are discussed. In this context, ultrasonic testing (UT), electromagnetic acoustic transducer (EMAT), eddy current (EC), magnetic flux leakage (MFL), and mechanical contact (MC) sensors are investigated. This paper also provides a comprehensive analysis of the development chronology of these sensors in the literature. Additionally, combinations of relevant sensor technologies are compared for their accuracy in sizing anomaly depth, length, and width. In addition to their importance in maintaining the safety and reliability of pipelines, the use of ILI can also have environmental benefits. This study aims to further our understanding of the relationship between ILI and the environment.

Smart water networks, created using Internet of Things (IoT) technologies, have been increasingly adopted by water utilities across the world. This research focuses on the use of smart water technologies for detecting newly formed through-wall pipe cracks and leaks in water distribution systems for the purpose of pipe break early warning and prevention. This research develops easy-to-implement algorithms for analysing the hydro-acoustic vibration wave files regularly collected by permanently installed accelerometers in a water network, and for generating alarms when small leaks from developing pipe cracks are detected. Descriptive features of the historical wave files are investigated for five sites where newly-formed pipe cracks were detected nearby. It is found that the median frequency (MF) and the root-mean-square (RMS) values derived from the wave files are useful indicators for detecting new cracks and leaks. The confidence of pipe crack detection in noisy city environments can be significantly increased by windowing each already short-duration wave file into a number of frames with even shorter durations, and analysing the behaviour of the MF and RMS values of all the frames to look for the persistence of the leak feature even when part of the signal is contaminated with interference. The results show that the developed technique can robustly detect new through-wall cracks and leaks in a timely manner, and is tolerant of interference from customer water use and transient environmental noise.

In this paper, an event-driven wireless sensor node is proposed and demonstrated. The primary design objective is to devise a wireless sensor node with miniaturization, integration, and high-accuracy recognition ability. The proposed wireless sensor node integrates two vibration-threshold-triggered energy harvesters that sense and power a threshold voltage control circuit for power management, a microcontroller unit (MCU) for system control, a one-dimensional convolutional neural network (1D-CNN) environment data analysis and vibration events distribution, and a radio frequency (RF) digital baseband transmitter with IEEE 802.15.4-/.6 protocols. The dimensions of the wireless sensor node are 4 × 2 × 1 cm3. Finally, the proposed wireless sensor node was fabricated and tested. The alarming time for detecting the vibration event is less than 6 s. The measured recognition accuracy of three events (knock, shake, and heat) is over 97.5%. The experimental results showed that the proposed integrated wireless sensor node is very suitable for wireless environmental monitoring systems.

A field experiment was conducted to study the effect of pipes depth and the distance between them in the smart artificial subsurface irrigation system on the efficiency of the system performance and the yield of sunflower ( Helianths annuus L. ), in one of the fields of Al-Raed Research Station/Ministry of Water Resources located 25 km west Baghdad for the growing season 2022. Two factors were used in the experiment, the first factor included the depths of the subsurface irrigation pipes at three levels 10, 15, and 20 cm. The second factor included the between subsurface irrigation pipes at three levels 50, 60, and 70 cm. The characters studied were soil moisture content %., irrigation water amount, cm 3 h -1 . depth of irrigation water added cm, plant height cm, and sunflower grain yield ton ha -1 . Nested system was used according to the randomized complete block design (RCBD) with three replicates. The pipes depth was allocated to the main plot and the distance in the sub-plots. Least significant difference was used under the probability (LSD 0.05) to compare the averages of treatments. The results showed that the highest and lowest values of moisture content were 23.21 and 16.69 % for treatment depth 20 and distance 60 cm and treatment depth 10 and distance 70 cm, respectively. The highest and lowest values for irrigation water depth were 7.99 and 5.31 cm for the treatment depth 15 and distance 50 cm, and treatment depth 10 and distance 60 cm, respectively. The highest and lowest values for the amount of water consumed were 234.59 and 96.7 cm 3 h -1 for treatments depth 10 and distance 50 cm, and treatment depth 20 and distance 60 cm, respectively. The highest and lowest values for plant height were 150.6 and 117.0 cm for the treatment depth 20 and distance 50 cm and treatment depth 10 and distance 70 cm, respectively. The highest and lowest values of grain yield were 4.97- and 2.62-ton ha -1 for treatments depth 20 and distance 50 cm and treatment depth 10 and distance 70 cm, respectively.

This paper presents a novel algorithm for compensating the changes in conductance signatures of a piezo sensor due to the temperature variation employed in condition monitoring using the electro-mechanical impedance (EMI) approach. It is crucial to consider the changes in an EMI signature due to temperature before using it for comparison with the baseline signature. The shifts in the signature due to temperature can be misinterpreted as damages to the structure, which might also result in a false alarm. In the present study, the compensation values are calculated based on experiments on piezo sensors both in a free boundary condition and in a bonded condition on a metallic host structure. The values were further validated experimentally for damage detection on a large 2D steel plate structure. The variation in first natural frequency values for the unbonded piezo sensor at different temperatures has been used to develop the compensation algorithms. Whereas, in the case of the bonded sensor, the shift in structural peaks has been used. The developed compensation relations showed promising results in damage detection. Lastly, a finite element-based study has also been performed, supporting the experimental findings. The outcome of this study will aid in the compensation of the signatures in the structure due to temperature variation in the conductance signature.