The Comprehensive Benefit Evaluation of Urban Drainage Culverts and Pipes Based on Combination Weighting

Abstract

The urban drainage system is a significant lifeline for ensuring the safe operation of a city. In recent years, defects and diseases in drainage pipes and their ancillary facilities have occurred frequently. Aiming to provide decision-makers with comprehensive benefit evaluation support, we chose to evaluate the security, environmental, social, and economic benefits of urban drainage culverts and pipes (UDCPs). An index system of 14 first-level indicators in four dimensions was established, and the indicators contain 28 influencing factors. The index weight was obtained by combining the analytical hierarchy process and entropy weight method, and the weights assigned to the security, environmental, social, and economic benefits were 0.448, 0.222, 0.202, and 0.128, respectively. The evaluation system was developed on the basis of a geographic information system (GIS), and the topological analysis of the GIS was applied in the calculation. To process the questionnaire results, this study adopted the automatic questionnaire analysis and scoring method combining natural language processing and optical character recognition technology. The method was applied in the study area in southern China, which contains 9 catchment areas and 1356 pipes. The results show that about 5% of the pipelines need to be included in the renewal plan. For UDCP renewal, the findings provide a decision-making tool of the comprehensive analysis for the selection of engineering technologies and the evaluation of the implementation effects.

1. Introduction

An urban drainage system is an essential municipal infrastructure, ensuring the safe operation of the city [1,2,3,4]. It handles wastewater and stormwater and includes pipes and structures that collect and dispose of the water [5]. With an increase in the service time, drainage pipes and ancillary facilities have a high incidence of defects and diseases [6]. The American Society of Civil Engineers surveyed 42 wastewater utilities, and an average of 57.5% of the system assets were reported to be between 21 and 100 years old [7]. In Shanghai, the total length of the four main drainage lines in the central city is 198 km; due to some pipelines being constructed many years ago, the corrosion of steel bars, the exposure of aggregates, and the spalling of fillers have gradually appeared, which has certain security risks [8]. Therefore, accurately evaluating an urban drainage system is a critical step in developing effective prevention and management strategies.

Comprehensive benefit evaluation can be realized through multiple perspectives, such as safety, environmental, social, and economic benefits. The data in the water sector and related fields [9,10] provide the basic conditions for assessment. In recent years, the evaluation of stormwater management has been widely researched. For instance, a “cost-benefit” evaluation through an analytic hierarchy process was conducted [11]; a multi-criteria decision-making method, i.e., the Technique for Order Preference by Similarity to Ideal Solution was adopted to rank the design alternatives and identify the optimal alternative [12]; and the socioecological influences of runoff control infrastructure were innovatively included in a uniform evaluation framework with the control functions and capital investments [13]. Since an urban drainage system is a municipal infrastructure and has an important impact on normal life, its safety has been assessed in many studies. For example, a weighted scoring method to determine the criticality of sewer pipelines was presented in terms of the deteriorating condition levels of wastewater networks and financial constraints [14]; an approach of using the ‘real age’ in estimating the probability of failure was introduced based on the unique operational and environmental conditions [15]. Currently, a comprehensive evaluation has been successfully applied to the areas of urban construction and renewal. The evaluation model of urban renewal comprehensive benefits was introduced based on the entropy method and fuzzy theory [16]. For pipeline systems, a comprehensive analysis of all aspects of integrity and safety was provided, and an approach to the solution of the interdisciplinary problems of pipeline diagnostics and reliability was demonstrated [17]. Moreover, suggestions for economics, the environment, and society were put forward to improve the overall benefits of an incineration power plant [18]. Considering limited urban land resources, the effect of greenery configurations on the comprehensive benefits of the microclimate environment and carbon sequestration was investigated [19]. The coupled green-grey system was shown to generate higher environmental impacts and economic costs than the grey system in the construction stage, while the operation stage of the coupled system had significant environmental and economic benefits [20]. Therefore, it is essential to develop an indicator system and a multi-dimensional comprehensive benefit evaluation system to help water utilities assess the current situation and aid in maintenance, rehabilitation, and replacement planning.

Urban drainage culverts and pipes (UDCPs) are an important lifeline to ensure the safe operation of the city, and this study focuses on the comprehensive evaluation of UDCPs in South China. Although numerous studies have been applied to evaluate UDCPs, they mainly focus on the aspect of safe operation. Based on the attributes of a UDCP, this study adopts a combined approach to improve the comprehensiveness and objectivity of the evaluation. The objectives of this study are as follows: (1) to develop a multi-dimensional evaluation index system covering the security, environmental, social, and economic benefits; (2) to establish a matching multi-attribute decision-making model and to achieve a scientific and reasonable evaluation for UDCP rehabilitation; (3) to develop a UDCP inspection and rehabilitation comprehensive benefit evaluation system for engineering technology selection and implementation efficiency. Through these efforts, this study aims to provide evaluation and comparison tools and to offer targeted strategy recommendations for UDCP management and maintenance.

2. Materials and Methods

Following systematic, feasible, and scientific principles, a multi-dimensional comprehensive benefit evaluation system was established, which covers security, environmental, social, and economic benefits. The system consists of four criterion layers in the core field of a UDCPs inspection and rehabilitation, which are as follows:

• The security benefit is the most important part of a UDCP comprehensive benefit evaluation. The criterion layer includes 5 indexes, which are used to comprehensively measure the safe operation state of a UDCP;
• Environmental benefits reflect the impact of UDCPs on the ecological environment;
• Social benefits reflect the impact of UDCP rehabilitation on the city;
• Economic benefits evaluate the economic feasibility and long-term benefits of the rehabilitation projects.

The above 4 guidelines and 14 specific indicators were established, and each indicator contained multiple branch factors, for example, “Importance degree in the overall drainage system”, and 6 influencing factors. The purpose is to comprehensively and objectively measure the comprehensive benefits to provide scientific tools for the decision-making of UDCP rehabilitation projects. It also provides guidance for the sustainable development and optimal management of urban drainage systems.

2.1. Index System of a UDCP Comprehensive Benefit Evaluation

The index system consists of four criterion layers in the field of UDCPs (shown in Figure 1). The definition and calculation of the indicators are as follows.

2.1.1. Importance Degree in the Overall Drainage System

The importance of the pipeline in the overall drainage system (self-developed indicator) was assessed based on a geographic information system (GIS), and it mainly included 6 factors: pipeline importance, laying region importance, diameter, age, material, and soil parameters. The pipeline importance levels were divided into main, trunk, and branch and can also be classified based on a hydraulic modeling and risk assessment system. In addition, the laying regions were mainly divided into the sensitive areas such as the central urban, the area beneath elevated roads, etc.; the traffic arteries and their surrounding areas; and the other driving roads and surrounding residences. Soil parameters refer to the soil conditions around the pipe.

2.1.2. Structural Conditions

On the basis of the Shanghai regulation “Technical code of practice for inspection & evaluation of sewers with CCTV and sonar” [21], the defect parameters of the structural conditions can be calculated using the following equation:

$$F=0.25\times S,\mathrm{w}\mathrm{h}\mathrm{i}\mathrm{l}\mathrm{e}S<40$$

(1)

$$F=10,\mathrm{w}\mathrm{h}\mathrm{i}\mathrm{l}\mathrm{e}S\ge 40$$

(2)

$$S={\displaystyle \frac{100}{L}}\sum _{i=1}^{n_1}{P}_i{L}_i$$

(3)

where F is the defect parameter of the structural condition. S represents the damage condition factor. L is the total length of the pipeline being evaluated, expressed in m. L_i is the longitudinal length of the flaw at the i place (unit: m). P_i is the weight of defect i, which is obtained according to the table in the specification. n₁ indicates the total number of defects.

2.1.3. Functional Status

The calculation equations of the functional defect parameter are described as follows [21]:

$$G=0.25\times Y,\mathrm{w}\mathrm{h}\mathrm{i}\mathrm{l}\mathrm{e}Y<40$$

(4)

$$G=10,\mathrm{w}\mathrm{h}\mathrm{i}\mathrm{l}\mathrm{e}Y\ge 40$$

(5)

$$Y={\displaystyle \frac{100}{L}}\sum _{i=1}^{n_2}{P}_i{L}_i$$

(6)

where G is the defect parameter of the functional status. $Y$ represents the operation condition factor. L is the total length of the pipeline being evaluated, expressed in m. L_i is the longitudinal length of the flaw at the i place (unit: m). P_i is the weight of defect i, which is obtained according to the table in the specification. n₂ indicates the total number of defects.

2.1.4. Pipeline Network Density and the Proportion of Green Space Used for Storage

The pipeline network density refers to the total length of pipelines within an urban land area (unit: km/km²), and it is an important index to measure the laying rate and development of urban pipeline facilities. It is automatically calculated by the GIS system. Usually, the drainage network system and sponge city facilities are used jointly to collect rainwater. The proportion of green space used for storage is calculated according to the proportion of green area where the total annual runoff control rate is greater than or equal to 70% [22].

2.1.5. Waterlogging Points

The statistics of waterlogging points in the service area of the drainage system were calculated, and the unit is the number of waterlogging points per year.

2.1.6. Water Environmental Impact

Based on the comparison between the amount of water in the wastewater treatment plant and the theoretical amount of sewage in the service area, i.e., the water balance analysis, the percentage of the external water infiltration into the primary sewage volume can be calculated [23]. The sewage discharge coefficient is set at 0.85; therefore, the theoretical sewage quantity is the water supply volume multiplied by 0.85.

2.1.7. Environmental Impact of Construction Projects

The environmental impact of construction projects was carried out using a questionnaire survey on the demonstration project, and the questionnaire contents were related to the odor, dust, impact of foundation soil, noise, settlement of surrounding structures, regional water ecology, etc.

2.1.8. Accounting for Construction Carbon Emissions

Carbon emissions are calculated based on consumption during the construction phase. On the basis of the “Standard for building carbon emission calculation”, etc., the carbon emissions per unit pipe length were calculated [24,25].

$$C_{JZ}=(\sum _{i=1}^n{E}_{jz,i}{EF}_i+C_{rg})/(L\times D)$$

(7)

where $C_{JZ}$ represents the carbon emissions during construction (unit: kgCO₂/(m·m)). $E_{jz,i}$ is total consumption of Type i energy during the construction phase (unit: kWh or kg). ${EF}_i$ represents the carbon emission factors of Type i energy (unit: kgCO₂/kWh or kgCO₂/kg). $C_{rg}$ represents the carbon emissions from manual operations during the construction phase (unit: kgCO₂). L and D are the length and diameter of the pipeline, respectively, with both expressed in m.

2.1.9. Impact of Construction Traffic

The impact of construction traffic was determined by the traffic impact time and road type (or road traffic volume), and it corresponded to each pipe. The road types were divided into branch, sub-trunk, trunk and express roads.

2.1.10. Quality Level of the Construction and the Survey of Customer Satisfaction

In this study, the subjective index data were collected through questionnaires. The quality level of the construction and the survey of customer satisfaction questionnaires were used to evaluate the construction quality and the residents’ satisfaction with the restoration project, and the actual effect of the project was reflected through the residents’ subjective evaluations.

2.1.11. Per Capita Gross Domestic Product (GDP) of the Region

The per capita GDP of the administrative or drainage area was assessed.

2.1.12. Engineering Cost per Unit Pipeline Length

We calculated the cost price per unit pipe length and diameter according to the specification or budget quotas, including auxiliary work such as pretreatment which is very important in the total project.

2.1.13. Designed Service Life of the Pipeline

Based on the rehabilitation or construction quality, this indicator represents the designed life of the repaired or relaid pipeline.

2.1.14. Return on Investment (ROI)

$$R_{OI}=\left(P/T\right)\times 100\%$$

(8)

where $R_{OI}$ is the return on investment. P represents the annual profit or average annual profit generated by the restoration (unit: ten thousand CNY). T is the total investment, expressed in ten thousand CNY. Profit calculation method: annual volume of external water infiltration multiplied by sewage treatment fee standard (taking Shanghai as an example, 2.00 CNY/m³).

2.2. Evaluation Model Building of the Comprehensive Benefits of UDCPs

2.2.1. Introduction to the Evaluation Algorithms

The comprehensive approach of AHP-entropy weight was applied to assign weights to the indicators in this study, and then the evaluation results of the comprehensive benefits for the different pipes or culverts were obtained. Currently, the analytical hierarchy process (AHP) and the entropy weight method (EWM) are widely used in determining index weights. The AHP is a mathematical model used to organize and analyze complex decision-making problems. By decomposing decision-making problems into hierarchical structures of a target layer, criterion layer, and scheme layer, the method then builds a judgment matrix and performs quantitative scoring according to the relative importance of the factors to help decision-makers deal with the problems systematically [26]. EWM is an objective weighting method, which measures the dispersion of the indicators by calculating the information entropy. If the dispersion is large, the entropy value is small, thus avoiding the bias of subjective weighting and making the determination of weights more scientific and objective [27]. The EWM is suitable for multi-index comprehensive evaluation, which helps decision-makers to conduct a comprehensive evaluation and decision analysis more accurately.

As a subjective empowerment method, the AHP is affected by human factors, while the EWM is objective, and it relies on the data’s own characteristics, which makes it easy to ignore the characteristics of the indicators. In order to solve the problems existing in the subjective and objective weight calculations, the comprehensive weight approach of AHP-entropy was applied in this study. The weight calculation equations are as follows.

2.2.2. AHP Weight Calculation Method

The AHP approach has been successfully applied to environmental management, construction programs and so on [28]. The two main AHP consistency calculations of the results are as follows:

$$C_I={\displaystyle \frac{{\lambda }_{max}-m}{m-1}}$$

(9)

$$C_R={\displaystyle \frac{C_I}{R_I}}$$

(10)

where $C_I$ is the consistency index, and $C_R$ is the consistency ratio. When $C_R$ < 0.1, the consistency of this matrix is acceptable. ${\lambda }_{max}$ is the largest eigenvalue, and m is the matrix size. R_I represents the random consistency index.

2.2.3. Entropy Weight Calculation Method

The type of criterion weight can be distinguished according to different decision methods, and objective weights are obtained through calculations of the evaluation matrix constructed from the actual information [29]. Due to the differences in the indicator units, each indicator needs to be standardized to eliminate its impact on the evaluation results. If the evaluation index is a benefit indicator, then

$$r_{ij}={\displaystyle \frac{x_{ij}-min\left(x_j\right)}{max\left(x_j\right)-min\left(x_j\right)}}$$

(11)

If the evaluation index is a cost indicator, then

$$r_{ij}={\displaystyle \frac{max\left(x_j\right)-x_{ij}}{max\left(x_j\right)-min\left(x_j\right)}}$$

(12)

where r_ij (i = 1, 2, …, m; j = 1, 2,…, n) is the value of the i-th evaluation alternative in the j-th indicator. $max\left(x_j\right)\mathrm{a}\mathrm{n}\mathrm{d}min\left(x_j\right)$ represent the maximum and minimum values of the j-th indicator, respectively. n is the number of evaluation indexes, and m is the number of evaluation objects.

$$p_{ij}={\displaystyle \frac{r_{ij}}{\sum _{i=1}^m{r}_{ij}}}$$

(13)

$$e_j=-{\displaystyle \frac{1}{ln\left(m\right)}}\sum _{i=1}^m{p}_{ij}ln\left(p_{ij}\right)$$

(14)

$$d_j=1-e_j$$

(15)

$$W_j^{\prime }={\displaystyle \frac{d_j}{\sum _{j=1}^n{d}_j}}$$

(16)

where $p_{ij}$ is the proportion of each element in the normalized matrix ${\left(r_{ij}\right)}_{m\times n}$. $e_j$ is the value of the information entropy of the j-th indicator, and when we set $p_{ij}$ = 0, $ln\left(p_{ij}\right)=0$. $d_j$ and $W_j^{\prime }$ represent the information utility and the weight for the j-th indicator, respectively. n is the number of evaluation indexes, and m is the number of evaluation objects.

2.2.4. Comprehensive Evaluation Method

The combined method makes full use of the advantages of the AHP and EWM methods, and the uncertainty problem caused by a single weighting method was solved. The comprehensive weight calculation formula is as follows [30].

$$W_j={\displaystyle \frac{{{(W}_j^{\prime }{W}_j^{*})}^{0.5}}{\sum _{j=1}^n{{(W}_j^{\prime }{W}_j^{*})}^{0.5}}}$$

(17)

where $W_j$ is the comprehensive weight value of the j-th indicator. $W_j^{*}$ is the index weight value calculated by the AHP method, and $W_j^{\prime }$ is calculated by the EWM, both coming from the previous step. n is the total number of indicators.

3. Study Area and Data

3.1. Data Collection and Processing

3.1.1. Data Collection Based on a GIS System

On the basis of a GIS, the evaluation data sources were assigned to the points, polylines, and polygons of the GIS to provide a large amount of spatial and attribute information for the evaluation model. Mapbox was used to develop the evaluation system. The specific data types are as follows:

• Point data, including the location information of waterlogging points, defect points, etc.
• Polyline data, which cover the detailed information of the pipeline, including the attributes of the pipe length, diameter, age, pipe importance, the importance of the district, and the designed service life, etc.
• Polygon data, including the drainage catchment and green space storage area. Data such as the pipe network density can be calculated automatically in a GIS.

3.1.2. Questionnaire Survey

In this study, the data of subjective indicators were collected using a questionnaire survey. The evaluation of the relative importance of the criteria and indicators, according to different experts, was determined based on a questionnaire to construct an AHP judgment matrix. In addition, the questionnaires also included the environmental impact of the construction projects, the quality level of the construction, and the survey of customer satisfaction. The purpose of the construction environmental impact questionnaire was to evaluate the impact of the construction process on the surrounding environment, including the investigation of noise, dust, traffic control, etc. The construction quality level and the customer satisfaction questionnaire were used to evaluate the construction quality and the satisfaction with the project and reflect the actual effects of the project through the subjective evaluation.

In total, four types of questionnaires were completed. And there were 23 valid questionnaires of AHP, 17 of the impact of the construction environment, 17 of the level of the construction quality, and 20 of the customer satisfaction. The “valid questionnaire” means one that was filled out completely and could be used for research purposes. For the AHP questionnaires, the group of experts was invited based on their expertise in urban drainage systems, including those engaged in research, design, construction, operation, and maintenance. For the construction environmental impact (containing 14 questions) and the construction quality level questionnaire (16 questions), the participants who filled out the questionnaire included technical engineers, construction personnel, managers, and designers. For the customer satisfaction questionnaire (15 questions), the staff members of hotels, restaurants, and office buildings were added additionally. The responses indicated that the construction of the drainage pipelines involves sensitive environmental areas, such as the inner ring elevated road, the urban center, water sources, and nature reserves, etc. Regarding noise pollution, approximately 70.6% of the respondents recommended that it was necessary to choose construction equipment with lower noise levels as much as possible and operate it at the lowest sound level. The impacts of construction on the environment mainly manifest in noise, odor, dust, the soil influence, the surrounding structures’ settlement, the regional water ecology, etc. Approximately 58.8% of the respondents suggested conducting a comprehensive inspection of the entire construction process based on the construction design documents, technical standards and specifications, and 47.1% of them believed that more tracking and monitoring should be carried out for the key processes. Approximately 70.1% of the people believed that it was necessary to promptly establish quality engineering information systems and properly manage the daily operations of records and forms. Approximately 82.4% of the participants placed greater importance on whether the UDCP construction caused traffic congestion. In total, 76.5% of the individuals were satisfied with the construction project duration, and 58.8% of them focused on the site condition after the project’s completion.

3.2. Study Area Overview

We present a drainage system in Nantong as an example, which has low-lying terrain with relatively gentle surface undulations. It has four distinct seasons and a mild climate. The urban geology is mainly composed of sedimentary rocks, clay, sandy soil, etc., and the strata are relatively soft. The study area contained 9 catchment areas and 1356 pipes. According to statistics, the land area is approximately 3,443,000 square meters (see Figure 2). The water flooding points were counted in points but not in frequency, therefore the values were set by referring to the data of rainfall weather. In addition, the attribute values of the study area were referenced from journal articles and officially published documents [25,31].

3.3. Questionnaire Analysis and Scoring Method

Aiming to analyze the questionnaires related to the environmental impact, the construction quality, and the customer satisfaction of the project, the research adopted the automatic questionnaire analysis and scoring method combining natural language processing and image recognition technology (seen in Figure 3). The specific implementation was as follows:

(1)

Building scoring criteria and conversion rules

• We input the structured questionnaires and scoring criteria. We organized the structured questionnaire scoring criteria into a standardized document format and used it as the input for the pre-trained language model. It mainly contained the scoring details of each question in the questionnaire, the setting of scores, and the calculation method of the total score, etc.
• The next step was semantic understanding and knowledge graph building. We leveraged the powerful semantic understanding capabilities of LLM to conduct an in-depth analysis of the scoring criterion documents. We analyzed the text content in the document and extracted the key scoring concepts and logical relationships to construct a knowledge graph. We organized each scoring item into a structured knowledge network to clearly understand and process the scoring logic.
• We automatically extracted scores for each item. We identified the logical relationship between each survey question and the selected score item by item to understand the scoring logic of single-choice, multiple-choice, and other self-filled items.
• We established conversion rules for detailed items and the total score. Based on the score settings and logical relationships in the scoring criteria document, the model automatically established conversion rules for the detailed item scores and the total score.

(2)

Batch image recognition of questionnaires and automatic scoring

• The first step was optical character recognition (OCR) text recognition and checkbox selection determination [32]. The images of the collected questionnaires were scanned and retrieved. Then, OCR text recognition was conducted using a LLM. The LLM recognized the text information in the image and accurately identified the positions of the checked items in the questionnaire to determine the selection results for each question.
• Based on the deep learning model of the LLM, we checked whether the recognition results of the previous step conformed to the logical structure of the questionnaire and the common answering patterns.
• We scored each question and calculated the total score. On the basis of the trained scoring rules, we scored each question in the questionnaires that were logically verified. We matched the selection results of each question with the scoring rules to determine the score. Finally, the total score of each questionnaire was calculated according to the conversion rules between the detailed item scores and the total score established previously.

4. Results

4.1. Index Weight Calculation

4.1.1. AHP Weight Calculation

The processing of the AHP judgment matrix involved the criterion layer and the index layers. There was a total of 23 valid questionnaires that adopted the nine-level scaling method. The steps were as follows. For the calculation results, refer to

Table 1. The calculation results of the AHP weight.

Criterion Layer	Weight Value	Index Layer	Weight Value
Security benefits	0.5612	Importance degree in the overall drainage system	0.2440
		Structural condition	0.2311
		Functional status	0.1988
		Pipeline network density and the proportion of green space used for storage	0.1407
		Waterlogging points	0.1854
Environmental benefits	0.1772	Water environmental impact	0.2790
		Environmental impact of construction projects	0.4496
		Accounting for construction carbon emission	0.2714
Social benefits	0.1821	Impact of construction traffic	0.3315
		Quality level of the construction and the survey of customer satisfaction	0.3954
		Per capita GDP of the region	0.2731
Economic benefits	0.0795	Engineering cost per unit pipeline length	0.3597
		Designed service life of pipeline	0.4089
		Return on investment	0.2314

.

• We performed consistency tests on all the collected judgment matrices. Of the 115 from 23 questionnaires, those with $C_R<0.1$ passed the test and were retained; conversely, they were excluded. Values below 0.1 indicate that the inconsistency of the judgment matrix is within a tolerable range, ensuring that the derived weights can reasonably reflect the relative importance of the evaluated factors [33]. This standard has been widely adopted in AHP applications to balance the practicality of human judgment. Through the screening process, 73 matrices met the consistency requirement and were retained for subsequent analysis, whereas 42 matrices were eliminated due to failure to reach the acceptable consistency level.
• We normalized and calculated the arithmetic mean of the retained judgment matrices to convert them into weight vectors, thereby obtaining a set of values that could accurately reflect the relative weights of each index.
• We took the arithmetic average of multiple weight vectors of different experts under the same object to calculate the comprehensive weight vector of the criterion layer and the index layer. This was used as the final subjective weight of the AHP based on expert experience.

4.1.2. EWM and Comprehensive Weight Calculation

According to Section 2.2.2, Section 2.2.3 and Section 2.2.4, the obtained entropy weight results of each index layer and comprehensive weight were as shown in Figure 4, and the weights assigned to the security, environmental, social, and economic benefits were 0.448, 0.222, 0.202, and 0.128, respectively.

4.2. Comprehensive Evaluation Results

The comprehensive evaluation results are shown in

Table 2. The scoring situation of the study area.

Evaluation Grade	Suggested Measures	Comprehensive Evaluation Range	Number of Pipes
Very poor	Rehabilitate immediately	E ≥ 0.25	17
Poor	Conduct regular inspections and formulate rehabilitation plans	0.21 ≤ E < 0.25	47
General	Regular inspection	0.18 ≤ E < 0.21	537
Good	Conduct evaluations regularly	0.15 ≤ E < 0.18	271
Very good	Conduct evaluations regularly	E < 0.15	484

and Figure 5. The Evaluation (E) value was obtained by multiplying the calculation results of each indicator by the corresponding comprehensive weight values and then summing them cumulatively. It corresponds to the final comprehensive benefit evaluation results of the study area. The results were consistent with actual pipeline operation and renovation plans. For instance, due to the waterlogging, the pipes near the river were included in the renewal plan. It was concluded that this comprehensive evaluation model is feasible to evaluate the UDCP rehabilitation.

5. Discussion

The study offers valuable insights into the challenges in UDCP rehabilitation, and it utilizes a combined-methods approach, incorporating GIS, literature reviews, questionnaires, AHP, and EWM to evaluate the comprehensive benefit. Compared to other studies, different focuses were completed. For instance, the comprehensive benefit evaluation of low-impact development was built to support cold cities to prevent and control waterlogging [11]; the multi-objective intelligent optimization was applied to a sponge city pilot region, and green–grey couple infrastructures in a uniform framework were suggested [13]; and the contents concerning the diagnostics, monitoring, maintenance, reliability, and safety of critical infrastructures were supplied to decision-makers responsible for various types of pipeline operations [17].

5.1. Index Sensitivity Analysis

The comprehensive benefit evaluation system of UDCPs includes four criteria: security, environment, society, and economy. The weight calculation shows the following:

• The security benefit is the core criterion layer, and its weight value is 0.448, reflecting the basic status of safe operation. The importance degree in the overall drainage system, condition, status, etc. of the pipelines are directly related to the drainage system stability. At present, most of the related research has focused on the operation safety of UDCPs, such as failure models and criticality assessment [14,15].
• The comprehensive weights of the environmental benefits and social benefits are relatively close, at 0.222 and 0.202 respectively, reflecting the comprehensive consideration of ecological protection and public impact in a UDCP project. Among them, the weight of the impact of the construction environment is relatively high, reflecting that the multi-faceted impacts of the engineering construction process on the environment need to be a focus. Accounting for construction carbon emissions holds a certain weight, which highlights the need to adopt environmentally friendly materials and equipment in UDCP projects to decrease the impact on the environment. For the social benefits, the quality level of the construction and the survey of customer satisfaction are the highest, reflecting the public’s great concern for the quality and service of UDCP projects. This indicates that during the implementation of UDCP projects, the needs and interests of the public should be fully considered. Communication and coordination with the surrounding residents and enterprises should be strengthened, and the impact of construction on social life aspects such as traffic should be minimized as much as possible. User satisfaction should be improved to enhance the social recognition of and support for UDCP projects, ensure the completion of the project, and achieve good social benefits.
• The weight of economic benefits is the lowest; however, the indicators such as the return on investment and designed service life still provide an economic feasibility basis for the ranking of rehabilitation priorities.

5.2. Algorithm Applicability Analysis

By building a hierarchical model of criterion and index layers (such as subdividing five indicators for the security benefit) and systematically integrating expert experiences, the AHP is suitable for the comprehensive and complex evaluation in this study. Meanwhile, it allows the relative importance of the indicators to be adjusted through the judgment matrix to adapt to the demand of different priorities. However, the judgment matrix depends on the decision-maker’s subjective judgment, which may be biased. At the same time, because this study involved 14 specific indicators, there was a certain failed consistency test.

The EWM is based on data dispersion to calculate the weight, which guarantees the objectivity of the evaluation. At the same time, this method can automatically adjust the weight with the updated data and support long-term monitoring and dynamic evaluation. EWM relies entirely on data distribution, which may weaken the key indicators identified by the experts’ experiences. Through the combination of subjective and objective empowerment, balance is achieved: the AHP strengthens expert experience (such as security benefit-oriented), and the EWM is data-driven, which improves the objectivity. In addition, the evaluation’s robustness is improved.

5.3. Future Research Recommendations

Hydrologic and hydrodynamic models are combined in a safety assessment, and simulation results have been applied to assess the performance of the sponge city infrastructure [34]. The hydraulic modeling of the urban drainage system is applied in the operation and maintenance, and the pipelines’ flow velocity, filling degree, and depth of water flood on the ground are calculated on the basis of the modeling. The simulation results are suggested in the indicator “Importance degree in the overall drainage system” to assess the operating conditions of the pipeline. Future research should further explore the coupled evaluation algorithm based on updated data, aiming to provide scientific support for decision-making. Real-time monitoring sensors of UDCPs can be combined to obtain dynamic data, and machine learning and hydraulic models can be utilized to explore the dynamic optimization of the weight distribution, further enhancing the intelligence of comprehensive evaluation.

During urban renewal, the comprehensive evaluation results can provide a basis; otherwise, it is necessary for the rehabilitation program to conduct a comprehensive evaluation in combination with the feasibility of construction and formulate a renewal plan for UDCPs.

6. Conclusions

(1)

We analyzed and evaluated the comprehensive benefits of UDCP renewal, based on the AHP and EWM, aiming at building a method that combines the subjective and objective. The indicators concern the security, environmental, social, and economic benefits. The system was developed on the basis of a GIS, and the topological analysis of the GIS was applied in the calculation of the indicators.

(2)

In this study, the evaluation system takes into account both the priority of security and comprehensive benefits on the basis of multi-dimensional indicators and a mixed weight algorithm. According to the approach of the AHP-entropy weight, security benefits were the core criterion layer with a comprehensive weight of 0.448, reflecting the fundamental position for the safe operation of UDCPs. The comprehensive weights of the environmental benefits and social benefits were 0.222 and 0.202, respectively, representing the comprehensive consideration of ecological protection and public impact in the rehabilitation of UDCPs. The weight of economic benefits was the lowest, at 0.128.

(3)

In applying the evaluation model to the study area, the results show that 5% of the pipelines need to be included in the renewal plan of these UDCPs.

(4)

The system provides a decision-making tool for UDCP projects, and a comprehensive analysis should be conducted, rather than focusing on a single benefit. For the questionnaire used in this study, an automatic questionnaire analysis and scoring method was proposed, which combined natural language processing and optical character recognition.