CCTV Kamera Verileri Kullanılarak Atıksu Sistemlerinde Meydana Gelen Arızaların ve Etkili Faktörlerin İncelenmesi

Yıl 2020, , 2668 - 2678, 15.12.2020

Mahmut Fırat , Cansu Orhan

https://doi.org/10.21597/jist.688915

Öz

Atıksu sistemlerinde zamanla çökme, ters eğim, tıkanma, yanlış bina bağlantısı, yağlanma, çatlak vb. arızalar meydana gelmektedir. Bu arızalar, fiziksel, işletme ve çevresel gibi farklı faktörlere bağlı olarak oluşmaktadır. Özellikle eski sistemlerde sürekli meydana gelen arızalar sonucu sokakta sürekli bakım onarım çalışmalarının yapılmasına neden olmaktadır. Bu arızaların sıklığı sistemin işletme maliyetini arttırmakta ve normal işletme koşullarını bozmaktadır. Bu çalışmada, atıksu sistemlerinde kapalı devre televizyon (CCTV) kamera görüntüleri esas alınarak tespit edilen yapısal kusurlar ve bunlara sebep olan faktörlerin incelenmesi amaçlanmıştır. Bunun için Malatya ili merkez ilçeleri olan Yeşilyurt ve Battalgazi ilçelerinde hizmet veren atıksu sistemi uygulama alanı olarak seçilmiştir. Atıksu sistemlerinde bozulmalara sebep olabilecek boru uzunluğu, boru eğimi, yapısal kusur yüzdesi ve doluluk oranı aşımı gibi faktörler belirlenmiş ve bu faktörlere ait veriler saha çalışmaları, kanal görüntüleme ve proje okuma gibi yöntemlerle elde edilmiştir. Saha verileri incelendiğinde, boru eğimi düşük olduğunda hat içinde çökelmeler oluşmakta ve akış kapasitesi zamanla düşmektedir. Sonuç olarak grafik ve Çizelgede verilen sonuçlara göre, yapısal kusur oranının artmasında, işçilik kalitesi (imalat, yatak malzemesi, projeye uygun eğim verilmesi), çevresel etkiler (trafik), fiziksel ve hidrolik faktörlerin etkili olduğu görülmüştür.

Anahtar Kelimeler

Atıksu sistemleri, yapısal kusur, CCTV

Destekleyen Kurum

İnönü Üniversitesi Bilimsel Araştırma Projeleri Koordinasyon Birimi

Proje Numarası

İÜ-BAP FYL-2017-582

Teşekkür

Yazarlar, İÜBAP Birimi’ne ve veri ve teknik destekten dolayı MASKİ Genel Müdürlüğüne teşekkür etmektedir.

Investigation of Faults and Effective Factors in Wastewater Systems Using CCTV Camera Data

Öz

In wastewater systems, the different types of failures such as collapse, reverse slope, clogging, incorrect building connection, lubrication, cracks and so on occur. These failures are caused by different factors such as physical, operational and environmental factors. Failures that occur constantly in older systems, cause continuous maintenance and repair work in the street. The frequency of these failures increases the operating cost of the system and disrupts normal operating conditions. In this study, it is aimed to investigate the structural defects and the factors that cause them in wastewater systems based on closed circuit TV (CCTV) camera images. For this, the wastewater system serving in Yeşilyurt and Battalgazi districts, which are central districts of Malatya province, has been chosen as the application area. Factors such as pipe length, pipe slope, percentage of structural defect and occupancy rate exceeding that could cause deterioration in wastewater systems were determined and data of these factors were obtained by methods such as field studies, CCTV and project reading. When the field data is analyzed, if the pipe slope is low, sedimentation occurs in the line and the flow capacity decreases over time. As a result, according to the results given in the graph and the table, the quality of workmanship (manufacturing, bedding material, slope appropriate to the project), environmental effects (traffic), physical and hydraulic factors have been effective in increasing the structural defect rate.

Anahtar Kelimeler

CCTV, Wastewater systems, structural defect

Proje Numarası

İÜ-BAP FYL-2017-582

Kaynakça

• Ammar MA, Moselhi O, Zayed TM, 2012. Decision support model for selection of rehabilitation methods of water mains. Structure and Infrastructure Engineering, 8: 847–855.
• Ana EV, Bauwens W, 2010. Modeling the structural deterioration of urban drainage pipes: the state-of-the-art in statistical methods. Urban Water Journal, 7: 47–59.
• Barreto W, Vojinovic Z, Price R, Solomatine D, 2010. Multiobjective Evolutionary Approach to Rehabilitation of Urban Drainage Systems, Journal of Water Resources Planning and Management, 136: 547–554.
• Carriço N, Covas DIC, Céu Almeida M, Leitão JP, Alegre H, 2012. Prioritization of rehabilitation interventions for urban water assets using multiple criteria decision-aid methods. Water Science and Technology, 66: 1007–1014.
• Cherqui F, Belmeziti A, Granger D, Sourdril A, Le Gauffre P, 2015. Assessing urban potential flooding risk and identifying effective risk-reduction measures. Science of the Total Environment, 514: 418–425.
• Choi T, Koo J, 2015. A water supply risk assessment model for water distribution network. Desalination and Water Treatment, 54: 1410–1420.
• Chughtai F, Zayed T, 2008. Infrastructure Condition Prediction Models for Sustainable Sewer Pipelines. Journal of Performance of Constructed Facilities, 22: 333–341.
• Del Giudice G, Padulano R, Siciliano D, 2016. Multivariate probability distribution for sewer system vulnerability assessment under data-limited conditions. Water Science and Technology, 73: 751–760.
• Ebrahimian A, Ardeshir A, Zahedi Rad I, Ghodsypour SH, 2015. Urban stormwater construction method selection using a hybrid multi-criteria approach. Automation in Construction, 58: 118–128.
• Ennaouri I, Fuamba M, 2013. New Integrated Condition-Assessment Model for Combined Storm-Sewer Systems. Journal of Water Resources Planning and Management, 139: 53–64.
• Hosseini SM, Ghasemi A, 2012. Hydraulic performance analysis of sewer systems with uncertain parameters. Journal of Hydroinformatics, 14: 682.
• Inanloo B, Tansel B, Shams K, Jin X, Gan A, 2016. A decision aid GIS-based risk assessment and vulnerability analysis approach for transportation and pipeline networks. Safety Science, 84: 57–66.
• Kim ES, Baek CW, Kim JH, 2005. Estimate of pipe deterioration and optimal scheduling of rehabilitation. Water Science and Technology: Water Supply, 5: 39–46.
• MASKİ (2018). Malatya Büyükşehir Belediyesi Su ve Kanalizasyon İdaresi.
• Maurer M, Scheidegger A, Herlyn A, 2013. Quantifying costs and lengths of urban drainage systems with a simple static sewer infrastructure model. Urban Water Journal, 10: 268–280.
• Mounce SR, Shepherd W, Sailor G, Shucksmith J, Saul AJ, 2014. Predicting combined sewer overflows chamber depth using artificial neural networks with rainfall radar data. Water Science and Technology, 69: 1326–1333.
• Rahmati O, Haghizadeh A, Stefanidis S, 2016. Assessing the Accuracy of GIS-Based Analytical Hierarchy Process for Watershed Prioritization; Gorganrood River Basin, Iran. Water Resources Management, 30: 1131–1150.
• Orhan C, 2018. Atıksu Sistemlerinde Rehabilitasyon için Öncelikli Bölgelerin Belirlenmesi. Yüksek Lisans Tezi, İnönü Üniversitesi, Fen Bilimleri Enstitüsü.
• Rokstad M M, Ugarelli RM, 2015. Evaluating the role of deterioration models for condition assessment of sewers. Journal of Hydroinformatics, 17: 789–804.
• Shahata K, Zayed T, 2010. Integrated decision-support framework for municipal infrastructure asset. ASCE Pipelines Proceedings, 514: 1492–1502.
• Sun S, Djordjević S, Khu ST, 2011. A general framework for flood risk-based storm sewer network design. Urban Water Journal, 8: 13–27.
• Tagherouit W, Ben Bennis S, Bengassem J, 2011. A Fuzzy Expert System for Prioritizing Rehabilitation of Sewer Networks. Computer-Aided Civil and Infrastructure Engineering, 26: 146–152.
• Tscheikner-Gratl F, Sitzenfrei R, Rauch W, Kleidorfer M, 2016. Integrated rehabilitation planning of urban infrastructure systems using a street section priority model. Urban Water Journal, 13: 28-40.
• Vucijak B, Ceric A, 2011. Multicrtieria prioritization of wastewater projects on example of bihac municipality. Annals of DAAAM and Proceedings of the International DAAAM Symposium, 22: 933–935.
• Zhou Q, Panduro TE, Thorsen BJ, Arnbjerg-Nielsen K, 2013. Adaption to extreme rainfall with open urban drainage system: An integrated hydrological cost-benefit analysis. Environmental Management, 51:586–601.
• Zhu Z, Chen Z, Chen X, He P, 2016. Approach for evaluating inundation risks in urban drainage systems. Science of the Total Environment, 553: 1–12.