Risk-based Rehabilitation of Wastewater Pipes
| dc.contributor.author | Lundberg, Rikard | |
| dc.contributor.department | Chalmers tekniska högskola / Institutionen för arkitektur och samhällsbyggnadsteknik (ACE) | sv |
| dc.contributor.examiner | Lindhe, Andreas | |
| dc.contributor.supervisor | Bergion, Viktor | |
| dc.date.accessioned | 2021-06-30T14:19:11Z | |
| dc.date.available | 2021-06-30T14:19:11Z | |
| dc.date.issued | 2021 | sv |
| dc.date.submitted | 2020 | |
| dc.description.abstract | Wastewater networks are part of society's underground infrastructure, intending to safely convey wastewater from consumers to Wastewater Treatment Plants (WWTP). This modern infrastructure has been recognised as an essential factor for sustaining public health, longevity and the environment. When, or if a failure occurs in this system, it can cause severe consequences to society, related to e.g. economy, public health and the environment. However, the reinvestments in wastewater pipe networks have been procrastinated, not just in Sweden but worldwide. This thesis aims to present a risk-based model that provides decision support and facilitates the design of rehabilitation strategies for wastewater networks. The primary objectives for the thesis were set to; review state the of art risk-based strategies for wastewater rehabilitation; set up a risk-based model that can be used as decision-support and renewal planning for water utilities; and implement the model in a case study based on Kungsbacka Municipality wastewater pipe network. The reviewed literature shows that that the most common methods to evaluate the probability of failure (POF) for individual wastewater pipes are Multi-Criteria Decision Analysis (MCDA) based on expert knowledge or statistical regression models, Bayesian Networks (BNs) and Artificial Neural Networks (ANNs). Further, the consequences of failure (COF) for individual pipes have been evaluated by classifying hazardous events into the economic, social and environmental consequences. The result of this thesis is a risk-based rehabilitation model based on evaluating POF and COF for the individual pipes within the wastewater pipe network to identify pipes with a high risk of failure (ROF), which is used to set up a Closed-Circuit Television (CCTV) inspection plan. Further, the CCTV inspection is used, in combination with COF, to set up a rehabilitation and re-inspection plan. The model strive to give decision-support regarding which pipe to inspect, rehabilitate, and re-inspect to maintain a sound and good service wastewater pipe network. The risk-based model was applied in a case study on Kungsbacka municipality's wastewater network, including 15,044 unique pipe IDs, with a total length of approximately 570 kilometres. First, POF was evaluated using multinomial logistic regression and MCDA. Next, COF was evaluated using economic, social and environmental consequences based on GIS data and MCDA. Further, the ROF was evaluated using the combination of POF and COF, where ROF was to a one-to-five scale, indicating; 1 (Low), 2 (Moderate), 3 (Moderate-to-high), 4 (High) and 5 (Very high) impact. As a result, the risk-based model could successfully evaluate 97.3% of the pipes within the wastewater pipe network regarding ROF and set up an inspection plan including 11,865 pipes and a rehabilitation priority, including 2,854 previously inspected wastewater pipes. | sv |
| dc.identifier.coursecode | ACEX30 | sv |
| dc.identifier.uri | https://hdl.handle.net/20.500.12380/302871 | |
| dc.language.iso | eng | sv |
| dc.setspec.uppsok | Technology | |
| dc.subject | Wastewater management | sv |
| dc.subject | risk assessment | sv |
| dc.subject | deterioration | sv |
| dc.subject | probability of failure | sv |
| dc.subject | risk of failure | sv |
| dc.subject | consequence of failure | sv |
| dc.subject | rehabilitation | sv |
| dc.title | Risk-based Rehabilitation of Wastewater Pipes | sv |
| dc.type.degree | Examensarbete för masterexamen | sv |
| dc.type.uppsok | H | |
| local.programme | Infrastructure and environmental engineering (MPIEE), MSc |