Assessment of risks of tunneling project in Iran using artificial bee colony algorithm

The soft computing techniques have been widely applied to model and analyze the complex and uncertain problems. This paper aims to develop a novel model for the risk assessment of tunneling projects using artificial bee colony algorithm. To this end, the risk of the second part of the Emamzade Hashem tunnel was assessed and analyzed in seven sections after testing geotechnical characteristics. Five geotechnical and hydrological properties of study zone are considered for the clustering of geological units in front of tunneling project including length of tunnel, uniaxial compressive strength, rock mass rating, tunneling index Q, density and underground water condition. These sections were classified in two low-risk and high-risk groups based on their geotechnical characteristics and using clustering technique. It was resulted that three sections with lithologies Durood Formation, Mobarak Formation, and Ruteh Formation are placed in the high risk group and the other sections with lithologies Baroot Formation, Elika Formation, Dacite tuff of Eocene, and Shear Tuff, and Lava Eocene are placed in the low risk group. In addition, the underground water condition and density with 0.722 and 1 Euclidean distances have the highest and lowest impacts in the high risk group, respectively. Therefore, comparing the obtained results of modelling and actual excavation data demonstrated that this technique can be applied as a powerful tool for modeling risks of tunnel and underground constructions

A Risk-Based Model for Construction Inspection in Highways

The quality and durability of highway construction projects have been a major concern to highway agencies and contractors. Quality assurance (QA) of highway construction is defined as a tool or means by which the owner and contractors ensure that the roads are constructed in accordance with approved plans and specifications by the most economical, efficient, and safe method. To ensure the quality of highway construction projects, transportation agencies typically perform a series of tests for construction materials and inspect workmanship processes through their QA programs. Transportation agencies face the critical challenge of increased demand for highway system rehabilitation and construction work with limited inspection resources. These resources play a crucial role in asserting the quality of highway projects. The shortage of experienced QA inspection staff due to retirement or migration to the private sector has significantly impacted construction inspection capabilities. The objective of this dissertation is to develop a risk-based inspection (RBI) framework. This framework optimizes inspection and testing activities of highway construction projects based on criticality. It introduces a core list of QA inspection and testing activities for the rigid pavement, flexible pavement, bridge deck, and structural concrete. This list highly contributes to the QA of design service life and long-term performance of the highway. The prioritized list of activities may help transportation agencies allocate their limited resources to the most critical construction operations. Additionally, this dissertation provides a RBI model that serves as a risk assessment tools for highway construction quality levels and identifies causes of any quality shortfall. Bayesian belief network (BBN), fuzzy set (FS) theory, and Delphi techniques have been applied to develop the RBI model. Further, this dissertation discusses different strategies to alleviate the risk of highway construction inspection

Development of Fuzzy Hybrid Approaches to Project Delivery Method Selection in Highway Construction

Selection of project delivery methods is a success factor in delivering highway construction projects because it has a substantial impact on the project performance, such as cost, time, and quality. Project delivery decision-making processes have been heavily relied on experts’ opinions and subjective judgements of professionals to evaluate quantitative and qualitative decision variables. Although current quantitative and probabilistic methods provide a robust means to analyze quantitative variables, they are not ideally suited for treating uncertainties encountered in qualitative variables. Fuzzy set theory is a mathematical approach that can accommodate a combination of quantitative and qualitative variables. This dissertation aimed at investigating the applications of fuzzy set theory and fuzzy logic to support decision-making processes in project delivery method selections. Using an empirical dataset of 254 completed highway construction projects, three fuzzy-based applications, including fuzzy cluster analysis, fuzzy pattern recognition, and fuzzy Bayesian inference system were developed, trained, and tested. As a result, fuzzy cluster analysis was used to establish seven common project clusters that share high similarities in project characteristics, project complexity, delivery risks, cost growth, and project delivery methods. Fuzzy pattern recognition was used to develop a fuzzy rule-based inference system based on the seven identified project clusters to help recognize an appropriate project delivery method associated with potential cost growth for new highway projects. Fuzzy Bayesian networks were used to develop the theoretical framework of fuzzy Bayesian inference system which is able to depict the causal relationships between project characteristics, project complexity, delivery risks, and project delivery methods. The flexibility of fuzzy membership functions in the developed applications helps leverage the evaluation of a combination of quantitative and qualitative variables in highway project delivery method selection. In addition, these data-driven fuzzy applications also allow for multiple decision scenarios based on the decision maker’s judgements of delivery risks and project complexity. This dissertation contributes to the body of knowledge by demonstrating quantitative approaches derived from fuzzy set theory and fuzzy logic to support the selection of project delivery methods in highway construction. Additionally, the results from the developed fuzzy-based applications also provide insights regarding cost performance comparisons between project delivery methods. This study may assist highway agencies in making project delivery decisions based on project attributes, historical data and their relevant experience

Safety and Reliability - Safe Societies in a Changing World

The contributions cover a wide range of methodologies and application areas for safety and reliability that contribute to safe societies in a changing world. These methodologies and applications include: - foundations of risk and reliability assessment and management - mathematical methods in reliability and safety - risk assessment - risk management - system reliability - uncertainty analysis - digitalization and big data - prognostics and system health management - occupational safety - accident and incident modeling - maintenance modeling and applications - simulation for safety and reliability analysis - dynamic risk and barrier management - organizational factors and safety culture - human factors and human reliability - resilience engineering - structural reliability - natural hazards - security - economic analysis in risk managemen

Systems Engineering: Availability and Reliability

Current trends in Industry 4.0 are largely related to issues of reliability and availability. As a result of these trends and the complexity of engineering systems, research and development in this area needs to focus on new solutions in the integration of intelligent machines or systems, with an emphasis on changes in production processes aimed at increasing production efficiency or equipment reliability. The emergence of innovative technologies and new business models based on innovation, cooperation networks, and the enhancement of endogenous resources is assumed to be a strong contribution to the development of competitive economies all around the world. Innovation and engineering, focused on sustainability, reliability, and availability of resources, have a key role in this context. The scope of this Special Issue is closely associated to that of the ICIE’2020 conference. This conference and journal’s Special Issue is to present current innovations and engineering achievements of top world scientists and industrial practitioners in the thematic areas related to reliability and risk assessment, innovations in maintenance strategies, production process scheduling, management and maintenance or systems analysis, simulation, design and modelling

Advances in Condition Monitoring, Optimization and Control for Complex Industrial Processes

The book documents 25 papers collected from the Special Issue “Advances in Condition Monitoring, Optimization and Control for Complex Industrial Processes”, highlighting recent research trends in complex industrial processes. The book aims to stimulate the research field and be of benefit to readers from both academic institutes and industrial sectors

A Statistical Approach to the Alignment of fMRI Data

Multi-subject functional Magnetic Resonance Image studies are critical. The anatomical and functional structure varies across subjects, so the image alignment is necessary. We define a probabilistic model to describe functional alignment. Imposing a prior distribution, as the matrix Fisher Von Mises distribution, of the orthogonal transformation parameter, the anatomical information is embedded in the estimation of the parameters, i.e., penalizing the combination of spatially distant voxels. Real applications show an improvement in the classification and interpretability of the results compared to various functional alignment methods

A comparison of the CAR and DAGAR spatial random effects models with an application to diabetics rate estimation in Belgium

When hierarchically modelling an epidemiological phenomenon on a finite collection of sites in space, one must always take a latent spatial effect into account in order to capture the correlation structure that links the phenomenon to the territory. In this work, we compare two autoregressive spatial models that can be used for this purpose: the classical CAR model and the more recent DAGAR model. Differently from the former, the latter has a desirable property: its ρ parameter can be naturally interpreted as the average neighbor pair correlation and, in addition, this parameter can be directly estimated when the effect is modelled using a DAGAR rather than a CAR structure. As an application, we model the diabetics rate in Belgium in 2014 and show the adequacy of these models in predicting the response variable when no covariates are available

IENE 2020 International Conference LIFE LINES – Linear Infrastructure Networks with Ecological Solutions. Abstract Book

In the past 12 to 14 of January 2021, the University of Évora, in the framework of the LIFE LINES project, and the Infrastructure and Ecology Network Europe held the online event IENE International Conference, under the theme “LIFE LINES – Linear Infrastructure Networks with Ecological Solutions”. We had 293 attendees from 40 different countries representing the five continents. During three days, participants were able to assist to 115 full oral presentations, 36 lightning talks, 13 workshops and chat with 40 posters authors, representing studies and projects worldwide. The event counted with 50 thematic sessions, running in five parallel sessions mixing live and pre-recorded interventions