Rail infrastructure costing based on multi-level full cost allocation

Due to charging issues costs of rail infrastructure use shall be determined as exactly as possible. At the same time rail infrastructure management is a very complex system characterised by a high ratio of indirect costs. There are several costing methods used in transport and logistics but none of them gives a transparent and traceable solution for allocating indirect costs. That is why the paper aims to elaborate a transport cost calculation model adopting and utilising the multi-level full cost allocation method. The developed model and the calculation process are specified for rail infrastructure management. The new cost calculation system delivers more reliable and accurate cost data of elementary rail infrastructure services by allocating indirect costs on a cause-effect basis. At the same time additional resources may be needed for the implementation of the model. Nevertheless, as rail infrastructure manager companies request more exact cost data they should consider the implementation of the proposed costing model

Development of a Statistical Theory-Based Capital Cost Estimating Methodology for Light Rail Transit Corridor Evaluation Under Varying Alignment Characteristics

The context of this research is the investigation and application of an approach to develop an effective evaluation methodology for establishing the investment worthiness of a range of potential Light Rail Transit (LRT) major system improvements (alternatives). Central to addressing mobility needs in a corridor is the ability to estimate capital costs at the planning level through a reliable and replicable methodology. This research extends the present state of practice that relies primarily on either cost averages (by review of cost data of implemented LRT projects) or cost categories in high and low cost ranges. The current methodologies often cannot produce accurate estimates due to lack of engineering data at the planning level of project development. This research strives to improve current practice by developing a prediction model for the system costs based on specific project alignment characteristics. The review of the literature reflects a wide range of estimates of capital cost within each of the contemporary mass transit modes. The primary problem addressed in this research is the challenge associated with producing capital cost estimates at the planning level for the LRT mode of public transportation in the study corridor. Furthermore, the capital cost estimates for each mode of public transportation under consideration must be sensitive to a range of independent variables, such as vertical and horizontal alignment characteristics, environmentally sensitive areas, urban design and other unique cost-controlling factors. The current available methodologies for estimating capital cost at the planning level, by transit mode for alternative alignments, have limitations. The focus of this research is the development of a statistical theory-based, capital cost-estimating methodology for use at the planning level for transit system evaluations. Model development activities include sample size selection, model framework and selection, and model development and testing. The developed model utilizes statistical theory to enhance the quality of capital cost estimation for LRT investments by varying alignment characteristics. This research has identified that alignment guideway length and station elements (by grade type) are the best predictors of LRT cost per mile at the planning level of project development. For the purpose of validating the regression model developed for this research, one LRT system was removed from the data set and run through the final multiple linear regression model equation to assess the model’s predictive accuracy. Comparing the model’s estimated cost to the projects final construction cost resulted in a 26.9% error. The percentage error seems somewhat high but acceptable at the planning level, since a 30% contingency (or higher) is typically applied to early level cost estimates. Additionally, a comparison was made for all LRT systems used in the model estimation and the percent error range is from 2.4% to 111.5% with just over 60% of the project’s predicted cost estimate within 30% or better. The model appears to be a useful tool for estimating LRT cost per mile at the planning level when only limited alignment data is available. However, further development of improved predictive models will become possible when additional LRT system data becomes available

Conceptual cost estimations using neuro-fuzzy and multi-factor evaluation methods for building projects

During the conceptual phase of a construction project, numerous uncertainties make accurate cost estimation challenging. This work develops a new model to calculate conceptual costs of building projects for effective cost control. The proposed model integrates four mathematical techniques (sub-models), namely, (1) the component ratios sub-model, (2) fuzzy adaptive learning control network (FALCON) and fast messy genetic algorithm (fmGA) based sub-model, (3) regression sub-model, and (4) multi-factor evaluation sub-model. While the FALCON- and fmGA-based sub-model trains the historical cost data, three other sub-models assess the inputs systematically to estimate the cost of a new pro­ject. This study also closely examines the behavior of the proposed model by evaluating two modified models without considering fmGA and undertaking sensitivity analysis. Evaluation results indicate that, with the ability to more thor­oughly respond to the project characteristics, the proposed model has a high probability of increasing estimation accura­cies more than the three conventional methods, i.e., average unit cost, component ratios, and linear regression methods

Impact of noise from urban railway operations

PhD ThesisThis thesis concerns the noise nuisance that results from the operation of urban railways and reports on a case-study of the impact of the Tyneside Metro on residents living in close proximity to the railway tracks. The study was based upon parallel related surveys in the vicinity of Wallsend and Walkergate, during the period August to November 1983: one, a subjective questionnaire survey of perceived noise-nuisance and the other, an objective set of measurements of the actual noise conditions prevailing there. A review of the methods of current practice in the control or urban railway noise demonstrates that regular maintenance of the rails and train wheels is still the most effective way of keeping noise under control at source. Nevertheless, with high speeds of operation, considerable noise nuisance is likely to be experienced by residents nearby. The Metro is the biggest source of noise and noise-nuisance for people exposed to noise levels of over 60 18H Leq dB(A), although the noise annoyance model constructed from the data showed that half of the annoyance felt by respondents could not be explained. Other factors which affect annoyance include vibration, perception of other transport noises, the subjects , ages and whether or not they own the property they occupy. Metro is generally perceived to be quieter and to cause less vibration than the diesel trains (DMUs) which preceded it. The equivalent continuous noise level (Led appears to be the most practical of all the various noise indexes for measuring railway noise annoyance. Finally, informal conversation with respondents in the course of a social survey can provide valuable insight into the mental and psychological processes of perception.The Rees Jeffreys Road Fund: The Ridley Fellowship

Development of a semantic knowledge modelling approach for evaluating offsite manufacturing production processes

The housing sector in the UK and across the globe is constantly under pressure to deliver enough affordable houses to meet the increasing demand. Offsite Manufacturing (OSM), a modern method of construction, is considered to be a key aspect in meeting these demands given its potential to increase efficiency and boost productivity. Although the use of OSM to increase the supply of affordable and efficient homes is getting popular, the focus has been on ‘what’ methods of construction are used (i.e. whether implementing OSM or traditional approach) rather than ‘how’ the alternative construction approach shall be done (i.e. choice of OSM method to meet set objectives). There have been criticisms of the approaches used by professionals implementing OSM methods as some of these approaches are non-structured and these methods have been criticised for being similar to the conventional onsite methods with little process gains. There are previous studies that have compared the performance of OSM and other modern methods of construction with conventional methods of construction. However, there is hardly any attempt nor quantitative evidence comparing the performance of various competing OSM approaches (i.e. methods with standardised and non-standardised processes) in order to support stakeholders in making an informed decision on choices of methods. In pursuit of the research gap identified, this research aims to develop a proof-of-concept knowledge-based process analysis tool that would enable OSM practitioners to efficiently evaluate the performances of their choice of OSM methods to support informed decision-making and continuous improvement. To achieve this aim, an ontology knowledge modelling approach was adopted for leveraging data and information sources with semantics, and an offsite production workflow (OPW) ontology was developed to enable a detailed analysis of OSM production methods. The research firstly undertook an extensive critical review of the OSM domain to identify the existing OSM knowledge and how this knowledge can be formalised to aid communication in the OSM domain. In addition, a separate review of process analysis methods and knowledge-based modelling methods was done concurrently to identify the suitable approach for analysing and systemising OSM knowledge respectively. The lean manufacturing value system analysis (VSA) approach was used for the analysis in this study using two units of analysis consisting of an example of atypical non-standardised (i.e. static method of production) and standardised (i.e. semi-automated method of production) OSM methods. The knowledge systematisation was done using an ontology knowledge modelling approach to develop the process analysis tool – OPW ontology. The OPW ontology was further evaluated by mapping a case of lightweight steel frame modular house production to model a real-life context. A two-staged validation approach was then implemented to test the ontology which consists of firstly an internal validation of logic and consistency of the results and then an expert validation process using an industry-approved set of criteria. The result from the study revealed that the non-standardised ad-hoc OSM production method, involving a significant amount of manual tasks, contributes little process improvement from the conventional onsite method when using the metrics of process time and cost. In comparison with the structured method e.g. semi-automated OSM production method, it is discovered that the process cost and time are 82% and 77% more in the static method respectively based on a like-to-like production schedule. The study also evaluates the root causes of process wastes, accounting for non-value-added time and cost consumed. The results contribute to supporting informed decision-making on the choices of OSM production methods for continuous improvement. The main contributions to knowledge and practice are as follows: i. The output of this research contributes to the body of literature on offsite concepts, definition and classification, through the generic classification framework developed for the OSM domain. This provides a means of supporting clear communication and knowledge sharing in the domain and supports knowledge systematisation. ii. The approach used in this research, integrating the value system analysis (VSA) and activity-based costing (ABC) methods for process analysis is a novel approach that bridges that gaps with the use of the ABC method for generating detailed process-related data to support cost/time-based analysis of OSM processes. iii. The developed generic process map which represents the OSM production process captures activity sequences, resources and information flow within the process will help in disseminating knowledge on OSM and improve best practices in the industry. iv. The developed process analysis tool (the OPW ontology) has been tested with a real-life OSM project and validated by domain experts to be a competent tool. The knowledge structure and rules integrated into the OPW ontology have been published on the web for knowledge sharing and re-use. This tool can be adapted by OSM practitioners to develop a company-specific tool that captures their specific business processes, which can then support the evaluation of their processes to enable continuous improvement

Rethinking construction cost overruns: an artificial neural network approach to construction cost estimation

The main concern of a construction client is to procure a facility that is able to meet its functional requirements, of the required quality, and delivered within an acceptable budget and timeframe. The cost aspect of these key performance indicators usually ranks highest. In spite of the importance of cost estimation, it is undeniably neither simple nor straightforward because of the lack of information in the early stages of the project. Construction projects therefore have routinely overrun their estimates. Cost overrun has been attributed to a number of sources including technical error in design, managerial incompetence, risk and uncertainty, suspicions of foul play and even corruption. Furthermore, even though it is accepted that factors such as tendering method, location of project, procurement method or size of project have an effect on likely final cost of a project, it is difficult to establish their measured financial impact. Estimators thus have to rely largely on experience and intuition when preparing initial estimates, often neglecting most of these factors in the final cost build-up. The decision-to-build for most projects is therefore largely based on unrealistic estimates that would inevitably be exceeded. The main aim of this research is to re-examine the sources of cost overrun on construction projects and to develop final cost estimation models that could help in reaching more reliable final cost estimates at the tendering stage of the project. The research identified two predominant schools of thought on the sources of overruns – referred to here as the PsychoStrategists and Evolution Theorists. Another finding was that there is no unanimity on the reference point from which cost performance could be assessed, leading to a large disparity in the size of overruns reported. Another misunderstanding relates to the term “cost overrun” itself. The experimental part of the research, conducted in collaboration with two industry partners, used a combination of non-parametric bootstrapping and ensemble modelling with artificial neural networks to develop final project cost models based on about 1,600 water infrastructure projects. 92% of the validation predictions were within ±10% of the actual final cost of the project. The models will be particularly useful at the pre-contract stage as they will provide a benchmark for evaluating submitted tenders and also allow the quick generation of various alternative solutions for a construction project using what-if scenarios. The original contribution of the study is a fresh thinking of construction “cost overruns”, now proposed to be more appropriately known as “cost growth” based on a synthesises of the two schools of thought into a conceptual model. The second contribution is the development of novel models of construction cost estimation utilising artificial neural networks coupled with bootstrapping and ensemble modelling

Parametric cost estimation system for light rail transit and metro trackworks

WOS: 000284863200170The main objective of this work is to develop early cost estimation models for light rail transit and metro trackworks using the multivariable regression and artificial neural network approaches. These two approaches were applied to a data set of 16 projects by using 17 parameters available at the early design phase. The regression analysis estimated the cost of testing samples with an error of 2.32%. On the other hand, artificial neural network estimated the cost with 5.76% error, which was slightly higher than the regression error. As a result, two successful cost estimation models have been developed depending on the findings of this paper. These models can effectively be utilized in the tender decision-making phase of projects with trackworks. (C) 2010 Elsevier Ltd. All rights reserved