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The purpose of this paper is to discuss the characteristics of several stochastic simulation methods applied in computation issue of structure health monitoring (SHM).

On the basis of the previous studies, this research focusses on four promising methods: transitional Markov chain Monte Carlo (TMCMC), slice sampling, slice-Metropolis-Hasting (M-H), and TMCMC-slice algorithm. The slice-M-H is the improved slice sampling algorithm, and the TMCMC-slice is the improved TMCMC algorithm. The performances of the parameters samples generated by these four algorithms are evaluated using two examples: one is the numerical example of a cantilever plate; another is the plate experiment simulating one part of the mechanical structure.

Both the numerical example and experiment show that, identification accuracy of slice-M-H is higher than that of slice sampling; and the identification accuracy of TMCMC-slice is higher than that of TMCMC. In general, the identification accuracy of the methods based on slice (slice sampling and slice-M-H) is higher than that of the methods based on TMCMC (TMCMC and TMCMC-slice).

The stochastic simulation methods evaluated in this paper are mainly two categories of representative methods: one introduces the intermediate probability density functions, and another one is the auxiliary variable approach. This paper provides important references about the stochastic simulation methods to solve the ill-conditioned computation issue, which is commonly encountered in SHM.

Bayesian approaches have been widely applied in construction management (CM) research due to their capacity to deal with uncertain and complicated problems. However, to date, there has been no systematic review of applications of Bayesian approaches in existing CM studies. This paper systematically reviews applications of Bayesian approaches in CM research and provides insights into potential benefits of this technique for driving innovation and productivity in the construction industry.

A total of 148 articles were retrieved for systematic review through two literature selection rounds.

Bayesian approaches have been widely applied to safety management and risk management. The Bayesian network (BN) was the most frequently employed Bayesian method. Elicitation from expert knowledge and case studies were the primary methods for BN development and validation, respectively. Prediction was the most popular type of reasoning with BNs. Research limitations in existing studies mainly related to not fully realizing the potential of Bayesian approaches in CM functional areas, over-reliance on expert knowledge for BN model development and lacking guides on BN model validation, together with pertinent recommendations for future research.

This systematic review contributes to providing a comprehensive understanding of the application of Bayesian approaches in CM research and highlights implications for future research and practice.

Due to product diversity, traditional quality signals in the hotel industry such as star ratings and brand affiliation do not work well in the accommodation booking process on the sharing economy platform. From a suppliers’ perspective, this study aims to apply the signaling theory to the booking of Airbnb listings and explore the influence of quality signals on the odds of an Airbnb listing being booked.

A binomial logistic model is used to describe the influences of different attributes on the market demand. Because of the large sample size, sequential Bayesian updating method is utilized in hospitality and tourism field for the first attempt.

Results show that, in addition to host-specific information such as “Superhost” and identity verification, attributes including price, extra charges, region competitiveness and house rules are all effective signals in Airbnb. The signaling impact is more effective for the listings without any review comments.

This study contributes to the literature by incorporating the signaling theory in the analysis of booking probability of Airbnb accommodation. The research findings are valuable to hosts in improving their booking rates and revenue. In addition, government and industrial management organizations can have more efficient strategy and policy planning.

Products that have a short selling season face high uncertainty in demand. Retailers who sell such products therefore find the task of pricing and inventory challenging. Many retailers consider making these decisions as an art form and do not use quantitative models that are developed by researchers. Describes how retailers typically make pricing and inventory decisions and also reviews quantitative models that have been developed by researchers to improve on one or more of these decisions. A classification of these models is developed and how they can assist the retailer is explained. A simple explanation of two mathematical tools, Bayesian updating of information and dynamic programming, which are commonly mentioned in the literature are also given.

The purpose of this paper is to value an innovation project derived from a product diversification strategy in the agricultural auto parts sector, considering uncertainties and flexibility as sources of value for the project.

A case study project is presented and valued using real options, with the possibility of including new information modelled by Bayes’ theorem, which enables the fitting of the probabilities of the project for the purpose of valuation through the decision tree method.

The results indicate the effect of new information and provide implications for value creation, demonstrating the need for a more profound and systematic approach to the use of investment strategies considering factors endogenous and exogenous to a firm that can and should be considered in the decision-making process.

This study contributes to an understanding of the relationship of strategy and innovation through product diversification with value creation and serves as a guide for controlling the possibility of investments in diversified companies and agribusiness using the methodology discussed in this paper.

Valuing the inclusion of new information through Bayesian inference in the analysis of investments is a methodological feature that has received minimal attention in the literature. Therefore, the use of Bayesian theory for the real options model applied to agribusiness is the main innovation of this study to demonstrate new ways of modelling uncertainty in addition to stochastic processes.

Unlike other computer‐based information systems, expert systems (ES) are characterized by the satisficing and conservative behavior of their users. Shows that the learning curve may be used to model user dependency on ES technology. Even though user dependency relates to ES quality control parameters (for example, Raggad’s 13 ES quality attributes) only dynamic or late binding features really affect ES dependency: ES learning capability and ES recommendation anticipation. There is hence a learning race between the system and the user. If user learning prevails, then there will be user defection. If system learning prevails, then there will be system perfection. Proposes failure analysis based on user defection due to the absence or underutilization of machine learning. ES owners can adopt this model to design a subsystem capable of transforming user defection analysis into a strategic plan for ES management.

This paper proposes a decision‐making framework to assist asset managers in decision making regarding sewer maintenance/rehabilitation (M&R) plans under constraints of limited access to sewer condition data. It discusses the application of probabilistic dynamic programming in conjunction with a Markov chain model to analyze the life cycle cost of combined sewer systems. M&R issues have traditionally been addressed with a crisis‐based approach, but this study contributes to sewer infrastructure management efforts in developing a management system based on life cycle cost analysis. The framework includes the optimal M&R techniques for sewer projects and the optimal times of application. The role of simulation is also explored to obtain the variability of the total cost. By knowing the expected costs and their variabilities, a deeper understanding of life cycle costs of sewer infrastructure can be obtained. The model’s capability is enhanced further by testing its sensivitity to varying discount and inflation rates.

Modern service robots are designed to work in a complex indoor environment, in which the robot has to interact with the objects in different ambient light intensities (day light, tube light, halogen light and dark ambiance). The variations in sudden ambient light intensities often cause an error in the sensory information of optical sensors like laser scanner, which reduce the reliability of the sensor in applications such as mapping, path planning and object detection of a mobile robot. Laser scanner is an optical sensor, so sensory information depends upon parameters like surface reflectivity, ambient light condition, texture of the targets, etc. The purposes of this research are to investigate and remove the effect of variation in ambient light conditions on the laser scanner to achieve robust autonomous mobile robot navigation.

The objective of this study is to analyze the effect of ambient light condition (dark ambiance, tube light and halogen bulb) on the accuracy of the laser scanner for the robust autonomous navigation of mobile robot in diverse illumination environments. A proposed AIFA (Adaptive Intensity Filter Algorithm) approach is designed in robot operating system (ROS) and implemented on a mobile robot fitted with laser scanner to reduce the effect of high-intensity ambiance illumination of the environment.

It has been experimentally found that the variation in the measured distance in dark is more consistent and accurate as compared to the sensory information taken in high-intensity tube light/halogen bulbs and in sunlight. The proposed AIFA approach is implement on a laser scanner fitted on a mobile robot which navigates in the high-intensity ambiance-illuminating complex environment. During autonomous navigation of mobile robot, while implementing the AIFA filter, the proportion of cession with the obstacles is reduce to 23 per cent lesser as compared to conventional approaches.

The proposed AIFA approach reduced the effect of the varying ambient light conditions in the sensory information of laser scanner for the applications such as autonomous navigation, path planning, mapping, etc. in diverse ambiance environment.