Jianlei Yi, Kunjian Jin, Haiying Qin and Yuhong Cui
An ideal method for predicting the fatigue life of spherical thrust elastomeric bearings has not been reported, thus far. This paper aims to present a method for predicting the…
Abstract
Purpose
An ideal method for predicting the fatigue life of spherical thrust elastomeric bearings has not been reported, thus far. This paper aims to present a method for predicting the fatigue life of laminated rubber spherical thrust elastomeric bearings.
Design/methodology/approach
First, the mechanical properties of standard rubber samples were tested; the axial stiffness, cocking stiffness, torsional stiffness and fatigue life of several full-size spherical thrust elastomeric bearings were tested. Then, the stiffness results were calculated using the neo-Hookean, Mooney–Rivlin and Yoeh models. Using a modified Mooney–Rivlin constitutive model, this paper proposes an improved method for fatigue life prediction, which considers the laminated characteristics of a spherical thrust elastomeric bearing and loads of multiple multi-axle conditions.
Findings
The Mooney–Rivlin model could accurately describe the stiffness characteristics of the spherical thrust elastomeric bearings. A comparative analysis of experimental results shows that the model can effectively predict the life of a spherical thrust elastomeric bearing within its range of use and the prediction error is within 20%.
Originality/value
The fatigue parameters of elastomeric bearings under multiaxial loads were fitted and corrected using experimental data and an accurate and effective multiaxial fatigue-life prediction expression was obtained. Finally, the software was redeveloped to improve the flexibility and efficiency of modeling and calculation.
Details
Keywords
Long Li, Haiying Luan, Mengqi Yuan and Ruiyan Zheng
As the scale of mega transportation infrastructure projects (MTIs) continues to expand, the complexity of engineering construction sharply increases and decision-making…
Abstract
Purpose
As the scale of mega transportation infrastructure projects (MTIs) continues to expand, the complexity of engineering construction sharply increases and decision-making sustainability faces severe challenges. Decision-making for mega transportation infrastructure projects unveils the knowledge-intensive characteristic, requiring collaborative decisions by cross-domain decision-makers. However, the exploration of heterogeneous knowledge fusion-driven decision-making problems is limited. This study aims to improve the deficiencies of existing decision-making by constructing a knowledge fusion-driven multi-attribute group decision model under fuzzy context to improve the sustainability of MTIs decision-making.
Design/methodology/approach
This study utilizes intuitionistic fuzzy information to handle uncertain information; calculates decision-makers and indicators weights by hesitation, fuzziness and intuitionistic fuzzy entropy; applies the intuitionistic fuzzy weighted averaging (IFWA) operator to fuse knowledge and uses consensus to measure the level of knowledge fusion. Finally, a calculation example is given to verify the rationality and effectiveness of the model.
Findings
This research finally constructs a two-level decision model driven by knowledge fusion, which alleviates the uncertainty and fuzziness of decision knowledge, promotes knowledge fusion among cross-domain decision-makers and can be effectively applied in practical applications.
Originality/value
This study provides an effective decision-making model for mega transportation infrastructure projects and guides policymakers.
Details
Keywords
Chao Yu, Haiying Li, Xinyue Xu and Qi Sun
During rush hours, many passengers find it difficult to board the first train due to the insufficient capacity of metro vehicles, namely, left behind phenomenon. In this paper, a…
Abstract
Purpose
During rush hours, many passengers find it difficult to board the first train due to the insufficient capacity of metro vehicles, namely, left behind phenomenon. In this paper, a data-driven approach is presented to estimate left-behind patterns using automatic fare collection (AFC) data and train timetable data.
Design/methodology/approach
First, a data preprocessing method is introduced to obtain the waiting time of passengers at the target station. Second, a hierarchical Bayesian (HB) model is proposed to describe the left behind phenomenon, in which the waiting time is expressed as a Gaussian mixture model. Then a sampling algorithm based on Markov Chain Monte Carlo (MCMC) is developed to estimate the parameters in the model. Third, a case of Beijing metro system is taken as an application of the proposed method.
Findings
The comparison result shows that the proposed method performs better in estimating left behind patterns than the existing Maximum Likelihood Estimation. Finally, three main reasons for left behind phenomenon are summarized to make relevant strategies for metro managers.
Originality/value
First, an HB model is constructed to describe the left behind phenomenon in a target station and in the target direction on the basis of AFC data and train timetable data. Second, a MCMC-based sampling method Metropolis–Hasting algorithm is proposed to estimate the model parameters and obtain the quantitative results of left behind patterns. Third, a case of Beijing metro is presented as an application to test the applicability and accuracy of the proposed method.
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Farman Afzal, Ayesha Shehzad, Hafiz Muhammad Rehman, Fahim Afzal and Mohammad Mushfiqul Haque Mushfiqul Haque Mukit
Cost estimation is a major concern while planning projects on public–private partnership (PPP) terms in developing countries. To bridge the gap of the right approximation of cost…
Abstract
Purpose
Cost estimation is a major concern while planning projects on public–private partnership (PPP) terms in developing countries. To bridge the gap of the right approximation of cost of capital, this study aims to sermon a significant role of investor’s risk perception as unsystematic risk in PPP-based energy projects.
Design/methodology/approach
To investigate the effective mechanism of determining cost of capital and valuing the capital budgeting, a pure-play method has been acquired to measure systematic risk. In addition, dynamic conditional correlation (DCC) and generalized autoregressive conditional heteroscedasticity (GARCH) models have been applied to calculate weighted average cost of capital.
Findings
Initially, a joint cost of capital of energy-related projects has been calculated using DCC-GARCH and pure-play method. Investors risk perception has been discussed through market point of view using country risk premium modeling. Latter yearly betas have been calculated using DCC signifying the final outcomes that applying a dynamic model can provide a better cost estimation in emerging economies.
Practical implications
The findings are implicating that due to the involvement of international investors, domestic risk is linked with country risk. In such situations, market-related information is a key factor to find out the market performance, helping in the estimation of cost of capital through capital asset pricing model (CAPM). High dynamic nature of emerging economies causes an impediment in the estimation of cost of capital. Consequently, to calculate risk in dynamic markets, this study has acquired DCC model that can predict the value of beta factor.
Originality/value
Study contributes to the body of knowledge by addressing an important issue of investor’s risk perception and effective implication of CAPM using pure-play and DCC-GARCH when data is not promptly available in dynamic situations.