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This paper seeks to propose a general framework to be used in developing multi‐use simulation modules for estimating project durations at the planning phase.

The research method incorporates two main stages. First, conceptualisation of the general framework, and second, implementing the framework in modelling and experimenting simulation modules, which involves data collection, statistical analysis, templates building through the ARENA software, and modules verification and validation.

The framework was found to be effective in providing an approach for building multi‐use simulation modules. The validation and verification processes of the developed simulation module reflect the soundness of the proposed framework.

Useful insights have been presented in this research regarding building multi‐use simulation modules in infrastructure construction projects. In addition, the paper demonstrates examples about how simulation interaction interface can contribute to the efficiency of using the simulation technique.

Given the lack of general approaches for building multi‐use simulation modules, this research suggests a simplified approach for developing multi‐use modules. Both academics and practitioners can benefit from this new approach by understanding the mechanism behind the multi‐use model concept as explained in this paper.

In this paper we are presenting our research findings on how effective neural networks are at forecasting and estimating preliminary project costs. We have shown that neural networks completely outperform traditional techniques in such tasks. In exploring nonlinear techniques almost all of the current research involves neural network techniques, especially multilayer perceptron (MLP) models and other statistical techniques and few authors have considered radial basis function neural network (RBF NN) models in their research. For this purpose we have developed RBF NN models to represent nonlinear static and dynamic processes and compared their performance with traditional methods. The traditional methods applied in this paper are multiple linear regression (MLR) and autoregressive moving average models with eXogenous input (ARMAX). The performance of these and RBF neural network and traditional models is tested on common data sets and their results are presented.

This paper describes the application of heuristic techniques for designing gravity wastewater collection systems. Designing sewer networks can be a time‐consuming task that is largely based on trial and error where suitable pipe diameters and slopes combinations for all pipelines between manholes must be identified. Since there is a large range of possible slopes, diameters and roughness coefficients of pipes, only a small number of combinations of these parameters are usually analyzed in traditional design processes. Identifying a minimum cost design is an important issue when constructing sewer networks. In this paper, genetic algorithms and tabu search techniques are implemented to solve this difficult optimization problem. An adaptive rule and a dynamic search strategy were developed to assist the search procedures find better solutions.

Finding the optimal solution to address problems in sewer management systems has always challenged asset managers. An understanding of deterioration mechanisms in sewers can help asset managers in developing prediction models for estimating whether or not sewer collapse is likely. The effective use of deterioration prediction models along with the development and use of life cycle cost analysis (LCCA) can contribute to the goals of reducing construction, operation and maintenance costs in sewer systems. When sewer system maintenance/rehabilitation options are viewed as investment alternatives, it is important, and in some cases, imperative, to make decisions based on life cycle costs instead of relying totally on initial construction costs. The objective of this paper is to discuss the application of deterioration modelling and life cycle cost principles in sewer system management, and to explore the role of the Markov chain model in decision making regarding sewer rehabilitation. A test case is used to demonstrate the application of the Markov chain decision model for sewer system management. The analysis includes evaluation of this concept using dynamic programming and the policy improvement algorithm.

This paper proposes a decision‐making framework to assist asset managers in decision making regarding sewer maintenance/rehabilitation (M&R) plans under constraints of limited access to sewer condition data. It discusses the application of probabilistic dynamic programming in conjunction with a Markov chain model to analyze the life cycle cost of combined sewer systems. M&R issues have traditionally been addressed with a crisis‐based approach, but this study contributes to sewer infrastructure management efforts in developing a management system based on life cycle cost analysis. The framework includes the optimal M&R techniques for sewer projects and the optimal times of application. The role of simulation is also explored to obtain the variability of the total cost. By knowing the expected costs and their variabilities, a deeper understanding of life cycle costs of sewer infrastructure can be obtained. The model’s capability is enhanced further by testing its sensivitity to varying discount and inflation rates.

This paper aims to investigate the condition and select a repair procedure for a damaged sewer in Kharkiv, Ukraine.

Inspection shafts are critical objects in the sewerage networks and are exposed to the strong influence of destructive processes, which are mainly corrosion of concrete structures, penetration of surface water inside structures, static and dynamic loads generated by truck transport, deviations from the codes and errors in construction, poor quality of the shaft wall surface, aggressive biological environment and ground subsidence. Therefore, selecting the optimum repair technology is of current interest.

The problems of repair and refurbishment of inspection shafts in deep-level sewer tunnels were considered. The technical and technological solutions using profiled polyethylene, reinforced slag cast panels, ceramic bricks and protective coatings are given. The advantages and disadvantages of the proposed technologies and examples of their practical application are shown.

This study performs reconstruction and strengthening work for a damaged section of the inspection shaft without shutdown.

In less than ten years the television camera has changed from being a “fascinating gadget” to establishing an important role in the pipe inspection and renovation business. Its other modern equivalent is possibly the laser beam which started life as an interesting straight beam of light which only took off in the construction industry when engineers exposed vast markets to the instrument designers by telling them just where, when and how they could use the pencil beam as a constant datum to replace the spasmodic sitings taken through optical instruments. The laser also added a vital component whereby the light beam had mechanical properties which could be effectively slaved to actuate recording and operational equipments. Today, the laser beam provides a constant datum on a building site, accurate alignment for a pipeline, automatic steering mechanisms for tunnel driving and automatic dredging depth in tidalwaters. Ere long, we will wonder how on earth we managed without it.

Introduction Sewer rehabilitation encompasses many different aspects of civil engineering. This single term defines everything from the detection of a lost sewer to its final reconnection to an existing property after the renovation of the sewer is complete. In its broadest definition, rehabilitation can be split into two sections — the inspection and the renovation of the sewer. In the first part of this paper, the extent of the problem, the survey techniques employed and the initial remedial work needed will be discussed.

The purpose of this paper was exploring and comparing different deterioration models based on statistical and machine learning approaches. These models were chosen from their successful results in other case studies. The deterioration models were developing considering two scenarios: (i) only the age as covariate (Scenario 1); and (ii) the age together with other available sewer characteristics as covariates (Scenario 2). Both were evaluated to achieve two different management objectives related to the prediction of the critical condition of sewers: at the network and the sewer levels.

Six statistical and machine learning methods [logistic regression (LR), random forest (RF), multinomial logistic regression, ordinal logistic regression, linear discriminant analysis and support vector machine] were explored considering two kinds of predictor variables (independent variables in the model). The main propose of these models was predicting the structural condition at network and pipe level evaluated from deviation analysis and performance curve techniques. Further, the deterioration models were exploring for two case studies: the sewer systems of Bogota and Medellin. These case studies were considered because of both counts with their own assessment standards and low inspection rate.

The results indicate that LR models for both case studies show higher prediction capacity under Scenario 1 (considering only the age) for the management objective related to the network, such as annual budget plans; and RF shows the highest success percentage of sewers in critical condition (sewer level) considering Scenario 2 for both case studies.

There is not a deterioration method whose predictions are adaptable for achieving different management objectives; it is important to explore different approaches to find which one could support a sewer asset management objective for a specific case study.

The originality of this paper consists of there is not a paper in which the prediction of several statistical and machine learning-based deterioration models has been compared for case studies with different local assessment standard. The above to find which is adaptable for each one and which model is adaptable for each management objective.

This paper is about best practice in managing legacy drainage assets to support sustainable urban regeneration. The purpose of this paper is to describe best practice sewer asset management (AM) and to adjust the current reactive maintenance approach for sewers, to one that accommodates long-term operational and town planning needs. The development of an improved sewer deterioration model (DM) provided an important tool for this.

This research adopts a mixture of qualitative and quantitative approaches to analyse a total network length of 24,252 km which represents 703,156 records of historic sewer structural condition inspection data. This was used to build an improved DM. These models were used as inputs into a proactive AM approach that improves upon recommendations in the Sewerage Rehabilitation Manual developed by Water Research Centre.

This is a paradigm shift and goes beyond the current culture of OFWAT (Water Services Regulation Authority) supervision, five-year asset management period and occasional environmental penalties. A new legislative model may be needed; especially because a report by UKWIR (Water Industry Research) in 2015 identified that nationally the rate of sewer network deterioration is outpacing available investment and significant health problems may arise in addition to those from developmental pressures.

The authors have researched and managed old sewer networks and present a review of the new issues raised by intensive development, particularly for the London region, but applicable elsewhere, and how these must lead to a modified risk, and novel incentive-based approach to AM, if the system is not to fail.

Large, legacy databases of several decades of sewer network performance records have been combined and analysed as stratified, heterogeneous sets with Gaussian distributions; thereby improving on previous assumptions of homogeneous data. The resulting rigorous DMs are the foundation of new approaches to sustainable risk management of large urban networks.