The proliferation of data collected by modern tunnel-boring machines (TBMs) presents a
substantial opportunity for the application of machine learning (ML) to support the decision …

Purpose Bayesian approaches have been widely applied in construction management (CM)
research due to their capacity to deal with uncertain and complicated problems. However, to …

Predicting the price of housing is of paramount importance for near-term economic
forecasting of any nation. This paper presents a novel and comprehensive model for …

The risks involved in ship pilotage operations are characterized by random, uncertain and
complex features. To reveal the spatiotemporal evolution of ship collision risks in the …

This paper presents a systemic decision-support approach to safety risk analysis for metro
construction projects under uncertainty using a fuzzy comprehensive Bayesian network …

This paper presents a systemic decision approach with step-by-step procedures based on
dynamic Bayesian network (DBN), aiming to provide guidelines for dynamic safety analysis …

This paper presents a probabilistic model for assessing risk of cascading failures in co-
located road and channel networks. The proposed Bayesian network analysis framework …

In this paper, an efficient multi-objective optimization (MOOP)-based updating framework is
established, which involves (1) the development of an enhanced multi-objective differential …

This paper proposes a novel hybrid approach that merges fuzzy matter element (FME),
Monte Carlo (MC) simulation technique, and Dempster–Shafer (D–S) evidence theory to …

Coal and gas outbursts are a major dynamic hazard in underground coal mining, and
predicting these events is challenging due to their complex mechanisms. This study …