nsojoutСтроительство: наука и образованиеConstruction: Science and Education2305-5502ФГБОУ ВО «Национальный исследовательский Московский государственный строительный университет»10.22227/2305-5502.2024.4.98-111nsojout-215Research ArticleИнженерные системы. Эксплуатация зданий. Проблемы ЖКК. Энергоэффективность и энергосбережение. Безопасность зданий и сооружений. ЭкологияEngineering systems. Exploitation of buildings. Problems of Housing and Communal Complex. Energy efficiency and energy saving. Safety of buildings and structures. EcologyРоль искусственного интеллекта в предотвращении утечек воды из сетей водоснабженияThe role of artificial intelligence in preventing water leakages from water supply networkshttps://orcid.org/0000-0002-6619-1212БаженовВ. И.BazhenovV. I.

Виктор Иванович Баженов — доктор технических наук, профессор, исполнительный директор

115054, г. Москва, Большой Строченовский пер., д. 7

РИНЦ AuthorID: 266644, Scopus: 57202817636

Viktor I. Bazhenov — Doctor of Technical Sciences, Professor, Executive Director

7 Bolshoy Strochenovsky pereulok, Moscow, 115054

RSCI AuthorID: 266644, Scopus: 57202817636

bazhenov@pump.ruПриминО. Г.PriminO. G.

Олег Григорьевич Примин — доктор технических наук, главный научный сотрудник; профессор

127238, г. Москва, Локомотивный проезд, д. 21;129337, г. Москва, Ярославское шоссе, д. 26

РИНЦ AuthorID: 414862

Oleg G. Primin — Doctor of Technical Sciences, chief researcher; Professor

21 Lokomotivny pr., Moscow, 127238; 26 Yaroslavskoe shosse, Moscow, 129337

RSCI AuthorID: 414862

tepper2007@yandex.ruБаженовВ. В.BazhenovV. V.

Владимир Викторович Баженов — аспирант, лаборант

105005, г. Москва, 2-я Бауманская, д. 5

РИНЦ AuthorID: 1228325

Vladimir V. Bazhenov — postgraduate student, laboratory assistant

5 2nd Baumanskaya st., Moscow, 105005

RSCI AuthorID: 1228325

BazhenovVladimirV@gmail.comАО «Водоснабжение и водоотведение» (АО «ВИВ»)РоссияJSC “Water and Wastewater”Russian FederationНаучно-исследовательский институт строительной физики Российской академии архитектуры и строительных наук (НИИСФ РААСН); Национальный исследовательский Московский государственный строительный университет (НИУ МГСУ)РоссияResearch Institute of Building Physics of the Russian Academy of Architecture and Construction Sciences; Moscow State University of Civil Engineering (National Research University) (MGSU)Russian FederationМосковский государственный технический университет имени Н.Э. Баумана (национальный исследовательский университет) (МГТУ им. Н.Э. Баумана)РоссияBauman Moscow State Technical University (BMSTU)Russian Federation20242712202414498111Copyright © Баженов В.И., Примин О.Г., Баженов В.В., 20242024Баженов В.И., Примин О.Г., Баженов В.В.Bazhenov V.I., Primin O.G., Bazhenov V.V.This work is licensed under a Creative Commons Attribution 4.0 License.https://www.nso-journal.ru/jour/article/view/215Введение

Введение. Одной из основ устойчивого развития и совершенствования централизованных систем водоснабжения (ЦСВ) и водоотведения является использование средств искусственного интеллекта (ИИ) на основе алгоритмов и моделей машинного обучения (МО): контролируемого, неконтролируемого, обучения с подкреплением. Утечки и несанкционированные подключения к ЦСВ представляют собой риски, приводя к потерям питьевой воды и снижению ценообразования в области учета водного ресурса. Актуальность связана с решением практических задач ИИ на основе новейших инноваций — прогнозированием и предотвращением аварий на ЦСВ при оптимальном планировании ремонтных работ и своевременном техническом обслуживании. Цель исследования — обосновать роль ИИ, использующего средства МО, в задачах прогнозирования отказов трубопроводов и аварийных ситуаций в ЦСВ.

Материалы и методы

Материалы и методы. Изучение информации о роли ИИ в предотвращении утечек воды из сетей водоснабжения выполнено методом литературного обзора примененных алгоритмов МО на предмет прогнозирования отказов труб в ЦСВ.

Результаты

Результаты. Выявлены и представлены модели МО, используемые для диагностического анализа с целью прогнозирования утечек воды из сетей водоснабжения. Обзор технологий свидетельствует об использовании 18 алгоритмов МО для решения задач, связанных с утечками ЦСВ. Начало применения нейросетевых алгоритмов Кохонена (KNN) в России говорит о наличии единственного переведенного на русский язык нейросетевого ПО STATISTICA Automated Neural Networks. Начинают активно развиваться акустические и ультразвуковые методы мониторинга состояния подземных трубопроводных сетей, основанные на распространении объемных и направленных волн (шума).

Выводы

Выводы. Водоканалам необходимо выполнять надежный и непрерывный сбор данных, что помогает принимать лучшие и надежные решения. Базы данных могут включать: диаметр трубы, длину участка и возраст трубы, давление, тип грунта. Собственно давление (или перепад) в сети не является признаком аварийности. Данный параметр следует рассматривать совместно с количеством отказов сети (аварий) на участках.

Introduction

Introduction. One of foundations for the sustainable development and improvement of centralized water supply networks (CWN) and sanitation is the use of artificial intelligence (AI) based on machine learning (ML) algorithms and models: supervised, unsupervised, reinforcement learning. Leaks and unauthorized connections to CWN pose risks, leading to losses of drinking water and reduced pricing in the field of water resource metering. The relevance is associated with solving practical AI problems based on the latest innovations — forecasting and preventing accidents at CWN with optimal planning of repair work and timely maintenance. The purpose of study is to substantiate the role of AI using ML tools in the tasks of predicting pipeline failures and emergency situations in CWN.

Materials and methods

Materials and methods. The study of information on the role of AI in preventing water leaks from water supply networks was carried out using the method of literature review of the used AI algorithms for predicting pipe failures in CWN.

Results

Results. The ML models used for diagnostic analysis to predict water leaks from CWN are identified and presented. The review of technologies shows the use of 18 ML algorithms to solve problems related to leaks in CWN. Start of use of Kohonen neural network algorithms (KNN) in Russia indicates the availability of the only neural network software translated into Russian, STATISTICA Automated Neural Networks. Acoustic and ultrasonic methods for monitoring the condition of underground pipeline networks, based on the propagation of volumetric and directional waves (noise), are beginning to develop rapidly.

Conclusions

Conclusions. Among the conclusions — for the Sustainable Development of CWN, water utilities need to ensure reliable and continuous data collection, this is a key practice that will help make reliable decisions based on AI predictions after the ML phase. Databases may include: pipe diameter, length of the section and age of the pipe, pressure, type of soil. The pressure itself (or difference) in the network is not a sign of an accident. This parameter should be considered together with the number of network failures (accidents) in the sections.

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The authors declare that there are no conflicts of interest present.