Search Results (10)

Road infrastructure projects are frequently affected by cost overruns driven by volatility in critical construction inputs and by the uneven association between external market shocks and material price movements. However, existing studies still provide limited evidence on how comparative forecasting, temporal price-signal diagnostics and probabilistic simulation can be integrated into a contingency-oriented decision framework. This study examines how construction input price forecasting and probabilistic simulation can inform contingency estimation in a road infrastructure case study. The empirical application is based on a Peruvian bridge project and combines benchmark-oriented forecasting using Bi-GRU and Random Walk models, descriptive temporal diagnostics based on lead–lag assessment and rolling-correlation analysis, and Monte Carlo simulation. Monthly series for structural steel, construction steel, cement, and diesel were transformed into log-returns and evaluated under a strict chronological design, while oil, the exchange rate, and the consumer price index were incorporated as exogenous variables. The Random Walk model produced lower forecasting errors for most inputs, achieving lower RMSE values in seven of the eight input-period comparisons; Bi-GRU outperformed it only for diesel in the test subset, with a 7.24% lower RMSE. From a project cost-risk perspective, the P95 contingency was estimated at 3.92% under Bi-GRU and 3.96% under Random Walk, indicating a similar upper-percentile contingency envelope under both forecasting specifications. The findings support contingency as a confidence-based budgeting decision rather than a fixed percentage. Full article

The earthquake of 23 June 2001, Mw 8.4, caused catastrophic damage in the city of Moquegua (Peru), especially in reinforced-concrete educational buildings. In this research, advanced procedures have been used and compared to assess the seismic performance of a new educational building designed under the current Peruvian construction regulations. Two nonlinear static procedures, the capacity spectrum method and an improved procedure based on the equivalent linearization method, have been applied and compared. Damage probabilities for a 475-year-return-period earthquake for the city of Moquegua evidence that the improved procedure based on the equivalent linearization method turns out to be slightly more conservative than the capacity spectrum method. Incremental dynamic analyses, based on 15 seismic events selected according to specific criteria, are taken as reference and complete the building damage assessment. Probabilistic damage matrices are proposed to assess damage using a probabilistic approach, which makes it possible to determine the levels of risk to be assumed in likely post-seismic scenarios and to carry out probabilistic estimates of the impacted population, the expected damage to structures, and the ranges of economic (social and material) costs. These tools assist stakeholders, civil protection and fire departments and the administrations involved in risk management and contingency planning in developing prevention strategies and improving preparedness for natural disasters such as earthquakes. Full article

Risk management is critical to maintain consistency between estimated and actual costs in construction investment projects, especially those that incorporate tourism and heritage components. This study aims to quantify the impact of risk factors on construction investment costs and to estimate an updated maximum project budget at a defined confidence level using an integrated expert-based and probabilistic approach. The approach combines a Frequency–Impact matrix, weighted scaling, and PERT/Monte Carlo simulation, thereby transforming expert judgments into comparable numerical parameters suitable for predictive modeling. The methodology is applied to the rehabilitation of the Esmeralda Hotel project in Cuba, a heritage asset characterized by high cultural value and technical complexity. The results quantify the effects of prioritized risk factors, compute their impact coefficients, and re-estimate the project’s upper budget limit at a 95% confidence level. The findings show that risk drivers associated with higher-complexity construction processes concentrate the main vulnerabilities and explain most of the increase in total cost. In addition, the analysis indicates that contingency margins established by regulation are insufficient to absorb the project’s observed variability. The proposed model supports proactive budget control by anticipating cost deviations, improving resource allocation, and strengthening decision-making under high uncertainty. Its flexible structure enables adaptation to different project types and serves as a practical decision-support tool for investors, designers, and project managers seeking greater financial accuracy and reduced risk of cost overruns. Full article

This systematic review examined how the Travel Cost Method (TCM) works together with other valuation methods, such as stated and declared preferences, to improve estimates of total economic value (TEV). Despite the widespread use of TCM, no systematic synthesis has examined how its integration with complementary methods enhances TEV estimation across different ecosystems and geographical contexts. Following PRISMA guidelines, we conducted searches in Scopus and Web of Science, identifying 245 records. After the screening process, 57 studies remained for analysis. Results show that 74% of the studies combined TCM with Contingent Valuation Method (CVM), and 12.3% with Choice Experiment (CEM). Three chronological phases were identified: early domination by the United States (1985–2000), international expansion and diversification (2001–2015), and recent methodological innovation led by China (2016–2024). Forest and recreational ecosystems accounted for 25% of applications, followed by marine-coastal (21%). Within cultural ecosystem services, the subcategory of physical and experiential interactions predominates with 63.1%. Comparative analysis indicates that TCM systematically produces higher and more variable monetary estimates than CVM, reflecting its sensitivity to travel behavior and spatial scale, while stated preference methods provide more stable estimates of non-use values. Persistent methodological limitations include non-probabilistic sampling and uneven ecosystem coverage. This review advances the literature by providing the first comprehensive synthesis of integrated TCM applications, demonstrating how methodological combinations strengthen TEV estimation beyond single-method approaches. The findings offer practical guidance for policymakers designing environmental impact assessments, environmental managers selecting valuation tools tailored to ecosystem and management objectives, and researchers seeking standardized and robust frameworks for integrated ecosystem service valuation. Full article

Inaccurate cost and schedule estimations in road infrastructure projects continue to be a critical source of contractual disputes and financial inefficiencies, particularly in developing countries. While quantitative risk analysis (QRA) methods such as Monte Carlo simulation (MCS) and schedule risk analysis (SRA) are well-established in the literature, their practical adoption remains limited in contexts with low technical capacity and limited access to advanced modeling tools. This study addresses this gap by proposing a practical and accessible quantitative risk analysis framework tailored to the needs of professionals with limited expertise in probabilistic techniques. The framework combines MCS and SRA using probability distributions (PERT, triangular, and normal) and was empirically validated through three road projects in Peru. Results indicated substantial reductions in uncertainty, achieving cost contingency estimates between 1.34% and 11% which were significantly lower than documented overruns of up to 32.29%. Schedule contingencies ranged from 28.71% to 91.67%, markedly improving accuracy. The novelty of this research lies in its context-adapted implementation strategy, offering a robust and easily replicable approach for similar infrastructure environments in Latin America and beyond. This contribution bridges the gap between theoretical risk modeling and its practical adoption, thus enhancing the reliability of infrastructure planning under resource-constrained conditions. Full article

There is little consensus about the underlying parameters of human reasoning. Two major theories have been proposed that suppose very different mechanisms. The mental model theory proposes that people use working memory intensive processes in order to construct limited models of problem parameters. Probabilistic theories propose that reasoning is a process by which people use the sum of their existing knowledge in order to generate an estimate of the probability of a conclusion given problem parameters. Following an initial proposition by Verschueren et al., the dual-strategy model supposes that these different approaches to reasoning are in fact an important individual difference. Specifically, a recently developed diagnostic questionnaire has identified two major categories of reasoners: Counterexample reasoners use a mental model form of processing, while Statistical reasoners use a probabilistic form of processing. In the following, I describe results that show that the Counterexample/Statistical distinction affects information processing across a variety of reasoning and judgment tasks. In addition, strategy use correlates with performance on very different kinds of thinking, such as contingency judgments, processing of negative emotions, or susceptibility to social biases. Although this distinction is related to differences in cognitive ability, it has been found to predict performance over and above these differences. More recent results have shown that it is possible to experimentally modify strategy use. These results suggest that strategy use is an important individual difference that can affect performance in a wide variety of contexts. Full article

In this study, Bayesian Belief Networks (BBN) are proposed to model the relationships between factors contributing to pavement deterioration, where their values are probabilistically estimated based on their interdependencies. Such probabilistic inferences are deemed to provide a reasonable alternative over costly data collection campaigns and assist in road condition diagnoses and assessment efforts in cases where data are only partially available. The BBN models examined in this study are based on a vast database of pavement deterioration factors including road distress data, namely cracking, deflection, the International Roughness Index (IRI) and rutting, from major road sections in the United Arab Emirates (UAE) along with the corresponding traffic and climatic factors. The dataset for the analysis consisted of 3272 road sections, each of 10 m length. The test results showed that the most critical parameter representing the whole process of road deterioration is the IRI with the highest nodal force. Additionally, IRI is strongly correlated with rutting and deflection, with mutual information of 0.147 and 0.143, respectively. Furthermore, a Bayesian network structure with a contingency table fit of over 90% illustrates how the road distress parameters change in the presence of external factors, such as traffic and climatic conditions. Full article

Background: Recent clinical trial results reported that stereotactic radiotherapy (SABR) may improve survival for patients with oligometastatic (OM) cancer. Given that these results come from a phase II trial, there remains considerable uncertainty about this finding, and about the cost-effectiveness of SABR for patients with OM cancer. In this analysis, we estimate the cost-effectiveness of SABR for oligometastatic cancer patients. Methods: A probabilistic time-dependent Markov model was constructed to simulate treatment of oligometastatic cancer patients over five- and ten-year time horizons. The primary data source was the phase II, Stereotactic Ablative Radiotherapy for the Comprehensive Treatment of Oligometastases (SABR-COMET )trial and supplemented with data from the literature. We estimated the effect of SABR and the standard of care (SoC) using quality-adjusted life-years (QALYs). Costs were measured from a provincial payer perspective (2018 Canadian dollars). Results: In the reference case analysis (five-year time horizon), SABR was associated with additional incremental costs of CAD 38,487 and an incremental QALY gain of 0.84. This resulted in an incremental cost-effectiveness ratio (ICER) of CAD 45,726 per QALY gained. Over a ten-year time horizon, the increased uncertainty in the long-term effectiveness of SABR resulted in an ICER of CAD 291,544 per QALY gained. Estimates from the probabilistic analysis indicated that at a willingness-to-pay (WTP) threshold of CAD 50,000 and CAD 100,000 per QALY gained, there is 54% and 78% probability (respectively) that SABR would be cost-effective using the five-year time horizon. Conclusions: The adoption of SABR therapy requires a considerable upfront capital investment. Our results suggest that the cost-effectiveness of SABR is contingent on the uncertainty in the evidence base. Further clinical trials to confirm the effectiveness of SABR and research into the real-world costs associated with this treatment could reduce the uncertainty around implementation of the technology. Full article

Probabilistic sea level projections are frequently used to characterise the uncertainty in future sea level rise. Here, it is investigated how different modelling assumptions and process estimates affect such projections using two process-based models that add up the sea level contributions from different processes such as thermosteric expansion and ice sheet melt. A method is applied to estimate the direct contributions from the different processes as well as that of nonlinear interactions between the processes to the projections. In general, the nonlinear interaction terms are found to be small compared to the direct contributions from the processes, and only a few interaction terms give significant contributions to the projections. Apart from the process estimates, probabilistic models often also incorporate some expert judgements that inflate the uncertainty compared with that derived from climate and ice-sheet models, and the effects of some such judgements are also evaluated and found to have a considerable influence on the projections. Lastly, sea level projections are most often given contingent on representative concentration pathways for atmospheric greenhouse gases. Here, we generalize this approach by also providing projections for a probabilistic baseline scenario. Full article

An increasing share of renewable energy sources in power systems requires ad-hoc tools to guarantee the closeness of the system’s frequency to its rated value. At present, the use of new technologies, such as battery energy storage systems, is widely debated for its participation in the service of frequency containment. Since battery installation costs are still high, the estimation of their lifetime appears crucial in both the planning and operations of power systems’ regulation service. As the frequency response of batteries is strongly dependent on the stochastic nature of the various contingencies which can occur on power systems, the estimation of the battery lifetime is a very complex issue. In the present paper, the stochastic process which better represents the power system frequency is analyzed first; then the battery lifetime is properly estimated on the basis of realistic dynamic modeling including the state of the charge control strategy. The dynamic evolution of the state of charge is then used in combination with the celebrated rain-flow procedure with the aim of evaluating the number of charging/discharging cycles whose knowledge allows estimating the battery damage. Numerical simulations are carried out in the last part of the paper, highlighting the resulting lifetime probabilistic expectation and the impact of the state of the charge control strategy on the battery lifetime. The main findings of the present work are the proposed autoregressive model, which allows creating accurate pseudo-samples of frequency patterns and the analysis of the incidence of the control law on the battery lifetime. The numerical applications clearly show the prominent importance of this last aspect since it has an opposing impact on the economic issue by influencing the battery lifetime and technical effects by modifying the availability of the frequency regulation service. Full article