Probabilistic Life-cycle Cost Analysis Research Articles

Deterioration Models for Bridge Pavement Materials for a Life Cycle Cost Analysis

As the Framework Act on Sustainable Infrastructure Management has recently been enacted in Korea, it has become mandatory to establish a medium-and long-term plan for managing social infrastructure and evaluating the feasibility of maintenance projects. However, road agencies are experiencing problems due to a lack of deterioration models which are essential to conducting a life cycle cost analysis. Thus, this study developed deterioration models for bridge pavements as the first step to secure the power of execution of the Infrastructure Management Act. The deterioration model subdivided pavement materials into asphalt, conventional concrete, and latex-modified concrete. This study analyzed the data on diagnosis for the last 12 years in Korea by applying the Bayesian Markov Hazard Model. The average life expectancy by pavement type was analyzed as follows: 12.8 years for asphalt pavement; 23.4 years for concrete pavement; and 9.8 years for latex-modified concrete pavement. For the probabilistic life cycle cost analysis and risk management, probability distributions of life expectancy, effective range by confidence level, and Markov transition probability were presented. This study lays a foundation for deterministic and probabilistic life cycle cost analysis of bridge pavement. Future studies need to develop deterioration models standardized for all components of bridges and all types of social infrastructure.

Framework for Design of Sustainable Flexible Pavement

The conventional methodologies for the design of flexible pavements are not adequate in providing solutions that meet the diverse sustainability challenges. Therefore, developing new methodologies and frameworks for the design of flexible pavement has become a priority for most highway agencies. On the other hand, there is no sound sustainable flexible pavement framework at the design phase that considers the key engineering performance, environmental impact, and economic benefits of sustainability metrics. Hence, premature failure of flexible pavements has become a common problem leading to a growing demand for sustainable pavement. Pavement engineers need to have access to tools that permit them to design flexible pavements capable of providing sustainable solutions under various complex scenarios and uncertainties. Hence, the objective of this study was to develop a resilience analysis framework, probabilistic life cycle assessment (PLCA) framework, and probabilistic life cycle cost analysis (LCCA) framework as the pillars of sustainability. These frameworks were used to develop a single sustainable flexible pavement design framework. The developed framework enables highway agencies to effectively quantify the lifetime sustainability performance of flexible pavements during the design phase in terms of resilience, environmental sustainability, and economic sustainability; and it allows to select the optimum design by comparing alternative design options. The framework will enhance the durability of flexible pavement projects by minimizing the cost, operational disturbance, environmental impact, and supporting the design. Many countries, especially those that fully dependent on the road network as the primary transportation route, may benefit from the sustainability-based road network design, which could ensure dependable market accessibility. The resilience of such a road network may reduce the cost of business activities by minimizing the interruption in surface transportation due to the functional and structural failures resulting from extreme events.

A Markovian approach to infrastructure life‐cycle analysis: Modeling the interplay of hazard effects and recovery

AbstractProbabilistic life‐cycle cost (LCC) analysis facilitates optimal management of infrastructure systems under uncertainty. Despite recent advancements, accurate and efficient estimation of the LCC of systems facing risk of exposure to multiple stochastic occurrences of hazards has remained a significant challenge. This paper proposes a Markovian LCC framework that substantially reduces the often very high computational cost of probabilistic LCC analysis especially for large systems. The presented methodology also enhances the accuracy of LCC estimates in multiple fronts. It captures damage accumulations due to incomplete or no repair actions over multiple stochastic hazard exposures in a system's lifetime. Moreover, it tracks the state of infrastructure vulnerability during repair processes and incorporates uncertainties in downtimes associated with damage states of the system. Finally, it estimates the entirity of loss induced by a hazard and avoids the truncation error that exists in annual loss estimates for scenarios where repair downtimes are greater than one year. With these new capabilities, the proposed LCC framework is applied to a bridge in a seismic zone to estimate the LCC and analyze complex interactions of stochastic hazards and their effects with recovery efforts. Results indicate that neglecting effects of damage accumulation and partial improvements in the course of repair can lead to 20% underestimation and 14% overestimation of the expected hazard‐induced LCC, respectively.

Probabilistic performance assessment of power distribution infrastructure under wind events

The electric power delivery infrastructure directly contributes to essential societal functions, the economy, and the general quality of life. Recent extreme wind events, such as Hurricanes Irma and Maria, and more recently Hurricane Michael resulted in power outages that affected millions of customers and led to major social and economic disruptions throughout communities. The longer the outage duration, the greater the incurred losses. Power distribution systems have proven to be highly vulnerable to such events and responsible for 90% of the outages. Distribution structures are built according to safety standards to ensure the safety of assets in operational and extreme conditions, but the dynamic nature of the wind loading is often overlooked or only considered empirically. Therefore, in this paper, such effects are included in a comprehensive risk-informed framework to assess the performance of electric power distribution components. This methodology explicitly accounts for the inevitable uncertainty in predictions of the component response. The focus is on characterizing wind events and the component-level risk analysis of physical components of the electric power system. For this purpose, a power pole–conductor system is modeled in which wind events are simulated as one-dimensional multivariable stochastic processes along the height of the poles and the conductors. Three-second peak gust wind speeds are used as a modeling reference. A probabilistic framework is developed to estimate the capacity of the distribution poles under different aging mechanisms. The response of the power distribution poles due to the simulated wind speeds is then computed using a finite element analysis. Fragility functions are generated to estimate the probability of exceedance for different damage states. A set of hazard mitigation strategies, the associated costs, and estimated benefits are implemented, and the improved fragility functions for elements are generated. A probabilistic life-cycle cost analysis is used to assess the long-term benefits of investing in different components of the power distribution system.

Field evaluation of porous asphalt course for life-cycle cost analysis

Porous asphalt courses (PAC) provide environmental and safety benefits by reducing traffic noise and improving drainability. As pressure increases on the cost and availability of natural resources, a greater understanding is needed of field performance to allow maintenance and preservation of resilient, sustainable and economic networks. The main purpose of this paper is to utilize life-cycle cost analysis (LCCA) as a decision-support tool for three pavement rehabilitation strategies. These alternatives included open-graded friction course using natural aggregate (OGFC), porous asphalt course using natural aggregate (PAC), and porous asphalt course using steel slag (SS-PAC). The LCCA followed the Federal Highway Administration’s RealCost procedure and incorporated information collected from highway agencies. Based on the results of the performance analysis, pavement life-cycle models were developed for each alternative. Using the life-cycle models and historical unit costs, deterministic and probabilistic LCCAs were conducted. The results demonstrated the cost saving of SS-PAC in both low and medium price ranges. In order to have economic benefits, the service life of SS-PAC needed to be more than eight years for a high price range. SS-PAC had a 78 and 92 percent chance of being more cost-effective than OGFC and PAC according statistical analysis, respectively. Conclusions from the LCCA supported use of the SS-PAC alternative since SS-PAC was shown to be relatively cost-effective over the 40-year analysis period. Utilization of the SS-PAC alternative would combine cost efficiency and sustainability for highway projects, especially rehabilitation and reconstruction.

Probabilistic Life-Cycle Cost Analysis of Pavements Based on Simulation Optimization

Life-cycle cost analysis (LCCA) is a way to evaluate the long-term cost effectiveness of different pavement designs or treatment actions. Owing to the existence of uncertainties, many probabilistic LCCA models have been proposed. They mainly use a prescribed treatment schedule or determine schedules by mechanistic-empirical analysis, potentially leading to the overestimation of life-cycle cost (LCC). In this paper, a new probabilistic simulation-optimization LCCA model is proposed. This new model determines treatment schedules by minimizing total LCC, including agency and user cost, which is different from current probabilistic models. In addition, it also incorporates uncertainties of treatment costs and deterioration processes. Two case studies are presented. The first one shows the influence of treatment schedule uncertainties on LCC distributions. After considering treatment schedule uncertainties, a tighter LCC distribution is estimated. The second case study compares the new model and a conventional prescribed-schedule model from the perspective of pavement design selection. The results show that the simulation-optimization model could lead to different preferred pavement designs than the prescribed-schedule model.

Deterministic and probabilistic life-cycle cost analysis of pavement overlays with different pre-overlay conditions

This study aims to evaluate the effect of pre-overlay condition on pavement performance and develop deterministic and probabilistic approaches to analyse the cost-effectiveness of pavement overlay alternatives. Pavement-condition data collected from Pavement Management System were used to analyse the service life of pavement overlay with different pre-overlay conditions and overlay thicknesses. Deterministic and probabilistic life-cycle cost analysis (LCCA) was first conducted to compare life-cycle cost (LCC) of different overlay treatments. Statistical test proves that the estimated overlay life for major rehabilitation is significantly higher than the overlay life for minor rehabilitation. Deterministic LCCA results show that there is a threshold of pre-overlay surface distress index (SDI) where the LCC is equal between major and minor rehabilitation treatments. Sensitivity analyses show that the cost ratio between major and minor rehabilitation treatments has significant influence on the threshold of SDI. Probabilistic LCCA results demonstrate the possible variations of LCC for different rehabilitation treatments. A probability index is proposed to quantify the probability that the LCC of minor rehabilitation is greater than the LCC of major rehabilitation. The analysis results show that probability index decreases as the pre-overlay SDI value increases or the cost ratio between major and minor rehabilitation treatments increases. The cost-effectiveness of rehabilitation treatments can be analysed using the LCCA approach developed in this study considering different pavement performance indicators and analysis parameters.

Construction cost estimation: A parametric approach for better estimates of expected cost and variation

As project planners continue to move towards frameworks such as probabilistic life-cycle cost analysis to evaluate competing transportation investments, there is a need to enhance the current cost-estimation approaches that underlie these models to enable improved project selection. This paper presents an approach for cost estimation that combines a maximum likelihood estimator for data transformations with least angle regression for dimensionality reduction. The authors apply the proposed method for 15 different pavement bid items across five states in the United States. The results from the study demonstrate that the proposed approach frequently leads to consistent parametric estimates that address the structural bias and heteroscedasticity that plague the current cost-estimation procedures. Both of these aspects are particularly important for large-scale construction projects, where traditional methods tend to systematically underestimate expected construction costs and overestimate the associated variance.

Probabilistic Life Cycle Cost Analysis of Building Energy Efficiency Measures: Selection and Characterization of the Stochastic Inputs through a Case Study

In recent years, the boost to the realization of “nearly Zero Energy Buildings” (nZEB) could require so high investment costs which may be not justifiable with the reduced consumptions (and costs) during the use phase. So, even if the estimated potential saving of energy efficiency projects seems to be very high, investors are often discouraged by a high-risk perception, linked to difficulty in knowing the real costs of advanced and innovative technologies, assessing unforeseen costs, or taking into account the significant fluctuations in energy costs. Life Cycle Cost Analysis (LCCA) in buildings could be a useful estimation method, but a large set of input parameters and accurate predictions is required to achieve an effective assessment. Aim of this study is the selection and characterization of the stochastic inputs to be exploited in a probabilistic LCCA to find the cost-optimal energy efficiency measures. We developed a Monte-Carlo based methodology for uncertainty quantification and sensitivity analysis, which combines Global Costs calculation with Building Energy Simulation and we applied it to a building case study, representative of the typical Italian stock. We characterized the stochastic inputs typically involved in the Global Cost method (related to the initial Investment Costs, Annual Costs, Residual Values, and Discount Rates) and analyzed the impact of these parameters on the final results in different renovation scenarios. Results showed that the financial factors (inflation and discount rate) and the energy trend uncertainty are the most influential parameters. Nevertheless, pushing towards nZEB makes it increasingly important the accuracy of investment costs data.

Probabilistic life-cycle cost analysis for renewable and non-renewable power plants

Two probabilistic models are presented to assess the costs of power plants. One of them uses requirement trees, value functions and the analytic hierarchy process. It is also based on Monte Carlo simulation. The second one is a mathematical model for calculating the levelised cost of electricity (LCOE) based on discounted cash flow techniques, and combined with Monte Carlo simulation. The results obtained with both models are compared and discussed. On the one hand, the LCOE model provides the most reliable results. These results reinforce the idea that conventional or coal, lignite, oil, natural gas and nuclear power plants are still the most competitive options, with the LCOE falling in a range of around 25 to 200 €/MWh and mean values approaching 70 €/MWh. Generally, renewable power plants obtained the worst results, with a LCOE varying from around 30 to more than 450 €/MWh. Nevertheless, this study demonstrates that renewable alternatives can compete with their conventional counterparts under certain conditions.

Enhancing life cycle cost analysis with a novel cost classification framework for pavement rehabilitation projects

Life cycle cost analysis (LCCA) procedures have been used over the past decades to justify the choice of one pavement design alternative over the others. However, many ambiguities associated with the life cycle cost input values, such as the discount rate and future cost estimates have questioned the credibility of the analysis results. Another unrecognized source of errors in pavement LCCA is the misunderstanding of pavement treatment costs when historical costs are typically used for estimating those costs. The historical costs of pavement rehabilitation projects typically include a significant amount of non-pavement-related costs, which may result in a wrong LCCA if not treated appropriately. This paper addresses this specific point of error and proposes a solution to eliminating this error by using a novel cost classification framework that successfully differentiates mainline roadway costs from non-pavement cost items. A case study using Monte Carlo simulation is conducted to evaluate the probabilistic LCCA results. The results of the case study indicate that the conventional approach of using total rehabilitation project costs in LCCA may even lead to a wrong investment decision. The findings of this study will help practitioners and researchers better understand the nature of pavement rehabilitation project cost distributions.

A risk-based optimisation for pavement preventative maintenance with probabilistic LCCA: a Chinese case

Compared to major structural repair or even replacement, preventative preservation of in-service pavements has been more popular in engineering practices, but recently, pavement preventative maintenance (PPM) has become more complex in China as the competition for pavement preservation funds has grown and the need to justify decisions has increased. Therefore, the life cycle cost analysis (LCCA) has increasingly attracted attention from transportation agencies. However, most of previous studies were conducted deterministically or only focused on a single factor, while PPM is apparently affected by many potential sources of uncertainty. The risk-based analysis to investigate potential risks and combined effects of multiple factors is a necessary component of PPM. This paper aims at presenting a risk-based PPM with the probabilistic LCCA for a Chinese highway case. Major analysis variables of different range are examined to probe risks of different scenarios, investigate combined effects of multiple variables and identify an optimal preservation strategy.

생애 위험도기반 건축물의 설계단계 생애주기비용 분석 방법

LCC 분석은 건축물의 설계단계 뿐만 아니라 유지관리 단계에서의 보수 보강 또는 교체에 대한 최적 의사결정의 도구로서 이론적으로나 실무적으로 각광을 받고 있는 분석 방법이다. 이는 초기 투자비용의 효율성을 극대화 하고 유지관리를 통한 구조물 사용성의 효율적 증대를 극대화하는 노력의 일환으로 최근 건축물의 설계 및 유지관리 시 LCC 분석효과의 실질적인 적용이 요구되어 지고 있다. 따라서 본 논문은 일반적인 분석기법에 그치고 있는 기존 LCC 연구를 생애위험도를 고려한 LCC분석을 통해 설계단계 최적 의사결정을 위한 새로운 분석 방법론을 도출하였다. Recently, the demand on the practical application of life-cycle cost effectiveness for design and rehabilitation of structure is rapidly growing unprecedently in engineering practice. Accordingly, in the 21st century, it is almost obvious that life-cycle cost together with value engineering will become a new paradigm for all engineering decision problems in practice. However, in spite of impressive progress in the researches on the LCC, the most researches have only focused on the Deterministic or Probabilistic LCC analysis approach (Level-1 LCC Model) at design stage. Thus, the goal of this study is to develop a practical and realistic methodology for the Lifetime risk based Life-Cycle Cost (LCC)-effective optimum decision-making at design stage.

Engineering Cost–Benefit Analysis of Thin, Durable Asphalt Overlays

Use of sustainable and cost-effective pavement overlays as a rehabilitation technique has been emphasized for several years as oil prices continue their instability in the United States. Since the 1980s, significant advances have been introduced in asphalt pavements regarding material selection, mix design, and construction technology that improve pavement performance. Some of these improvements require the use of a highly modified asphalt binder and expensive high-quality aggregates to achieve the desired performance. This study developed four potentially cost-effective asphalt mixtures and efficient cross sections for asphalt wearing surfaces. The newly developed asphalt mixes incorporated locally available aggregates, special binder additives such as fibers, innovative surfacing technologies, or all of these techniques. The cost-effectiveness of the newly developed asphalt mixtures is presented, and the improved performance is considered. Deterministic and probabilistic life-cycle cost analysis was performed on the considered mixes. The performance of the considered mixes was investigated in the laboratory and on site under actual traffic. The engineering cost–benefit analysis showed that the new mixtures performed better and were more cost-effective than the control mixes considered.

Probabilistic Characterization of Uncertain Inputs in the Life-Cycle Cost Analysis of Pavements

Life-cycle cost analysis (LCCA) is an important tool for evaluating the merits of alternative investments. Inevitably, inputs for an LCCA are subject to a high level of uncertainty in both the short and long term. However, the way LCCA is currently implemented in the field treats LCCA inputs as static, deterministic values. Conducting an analysis in this way, though computationally simpler, hides the underlying uncertainty of the inputs by only considering a few possible permutations. The results of such an analysis could potentially lead a decision maker to the incorrect pavement selection. One methodology that has gained traction in the past decade is describing uncertain parameters probabilistically and allowing the analysis to consider a range of possible outcomes. Although this methodology is recommended by FHWA, practitioners still generally conduct deterministic LCCAs. One of the major reasons is that further work must be conducted to characterize uncertainty statistically for input parameters. This research attempted to build on previous work by probabilistically characterizing several input parameters for which empirical data were and were not readily available. This paper characterizes uncertainty and variability in the LCCA of pavements and then applies the methodology presented to two case studies to understand the implications of a probabilistic LCCA for the pavement selection process.

A comparison of deterministic and probabilistic life cycle cost analyses of ground source heat pump (GSHP) applications in hot and humid climate

Geothermal energy is widely considered as an ideal alternative energy for building operations. Despite the fact that ground source heat pumps (GSHPs) have been studied for decades, including economic analyses such as life cycle costing, their technical and economic applicability in hot and humid climate such as FL, USA is yet to be determined. In the past, many life cycle cost analyses on GSHPs were conducted using a deterministic method to derive point estimates. In many cases, data were assumed, but data uncertainties were not accounted for. In this study, a comparison of the deterministic and the probabilistic method was performed in order to understand the impact of data uncertainties on results of analyses. The probabilistic life cycle cost analysis was based on Monte Carlo simulation. A GSHP application case in Pensacola, FL was selected for the comparison. Some data were collected through site visits or interviews, while others were gathered from the literature or published data. Probability distribution functions used by Monte Carlo simulation were derived based on historical data or assumptions. Results of the case study from both deterministic and probabilistic methods confirmed that the GSHP option was economically more favorable than a conventional single zone split system using heat pumps, but with a long payback time if incentives were not considered. The probabilistic method was found to deliver a more reliable conclusion with more critical information than the deterministic method. However, further studies were needed to verify those initial observations obtained from this case study.

Life-Cycle Cost Analysis of Alternative Reinforcement Materials for Bridge Superstructures Considering Cost and Maintenance Uncertainties

A life-cycle cost analysis (LCCA) was conducted on prestressed concrete bridge superstructures using carbon fiber reinforced polymer (CFRP) bars and strands. Traditional reinforcement materials of uncoated steel with cathodic protection and epoxy-coated steel were also considered for comparison. A series of deterministic LCCAs were first conducted to identify a range of expected cost outcomes for different bridge spans and traffic volumes. Then, a probabilistic LCCA was conducted on selected structures that included activity timing and cost random variables. It was found that although more expensive initially, the use of CFRP reinforcement has the potential to achieve significant reductions in life-cycle cost, having a 95% probability to be the least expensive alternative beginning at year 23–77 after initial construction, depending on the bridge case considered. In terms of life-cycle cost, the most effective use of CFRP reinforcement was found to be for an AASHTO beam bridge in a high traffic volume area.

Pavement Rehabilitation Strategy Selection for Steel Suspension Bridges Based on Probabilistic Life-Cycle Cost Analysis

Field inspections on long-span steel suspension bridges in China reveal that the pavements undergo a fast deterioration process attributed to the flexibility of suspension bridges and the heavy traffic loads. Such extensive damage and enormous repair costs call for a cost-effective rehabilitation strategy for these bridges. In this paper, the pavement rehabilitation strategy is investigated for the Runyang Suspension Bridge in China with the aid of the life-cycle cost analysis (LCCA). Three alternatives are compared for their economic efficiency, and both agency costs and user costs are taken into account. To consider the uncertainties that abound in the LCCA, probabilistic analyses are made in which the Latin hypercube method is used to provide samples of random variables. According to the LCCA results, the user costs are a considerable proportion of the total costs so that the selection of rehabilitation strategies is significantly influenced by the user costs. In this regard, the second alternative, the double-layer pavement (guss asphalt plus stone matrix asphalt) is the most cost-effective rehabilitation strategy for the bridge. Additionally, sensitivity analyses show that the traffic growth rates, the discount rate, the work zone duration, and the delay cost rate for passenger cars are the most sensitive variables in the user costs. The methodologies and approaches introduced herein can be easily implemented by practitioners and would be of benefit to pavement rehabilitations for steel suspension bridges.

Fuzzy Logic Pavement Maintenance and Rehabilitation Triggering Approach for Probabilistic Life-Cycle Cost Analysis

Life-cycle cost analysis (LCCA) is an important tool in transportation asset management. Transportation agencies have used both deterministic and probabilistic LCCA approaches. Probabilistic approaches allow decision makers to evaluate the risk of an investment by using uncertain input variables, assumptions, or estimates. The incorporation of fuzzy logic–based models into the risk analysis process is explored to enhance further the traditional probabilistic LCCA method. It proposes a fuzzy logic approach for determining the timing of pavement maintenance, rehabilitation, and reconstruction (MR&R) treatments in a probabilistic LCCA model for selecting pavement MR&R strategies. Instead of using predefined service life for initial construction and future rehabilitations, the proposed approach uses performance curves and fuzzy logic triggering models to determine the most effective timing of MR&R activities. The new approach is compared with the deterministic and traditional probabilistic approaches in a simple case study. The case study demonstrates that the fuzzy logic–based risk analysis model for LCCA can effectively produce results that are at least comparable to those of the benchmark methods while effectively considering some of the uncertainty inherent to the process.

Risk-Based Life-Cycle Cost Analysis of Privatized Infrastructure

One main shortcoming in the use of life-cycle cost analysis (LCCA) for analyzing long-term infrastructure projects is the uncertainty in the value of the LCCA parameters. Probabilistic LCCA incorporates these elements of uncertainty by assigning probabilistic values to cost and performance parameters. Studies that have performed probabilistic LCCA in the infrastructure domain propose a probability-based framework for alternative comparison. Although such frameworks convey a wealth of probabilistic information, they are not well suited to decision making. This study proposes a risk-based framework that is similar to techniques used in portfolio risk management. To illustrate the use of such a framework, a Monte Carlo simulation is used to perform probabilistic LCCA for a highway project. Two highway investment opportunities with varying risks and returns are analyzed. The decision framework is used to compare the simulation results with some common investment opportunities in the market. This framework ena...