A bayesian belief networks approach to risk control in construction projects

Although risk control is a key step in risk management of construction projects, very often risk measures used are based merely on personal experience and engineering judgement rather than analysis of comprehensive information relating to a specific risk. This paper deals with an approach to provide better information to derive relevant and effective risk measures for specific risks. The approach relies on developing risk models to represent interactions between risk factors and carrying out analysis to identify critical factors on which risk measures must focus. To ameliorate the problem related to the scarcity of risks information often encountered in construction projects, Bayesian Belief Networks are used and expert knowledge is elicited to augment available information. The paper describes proposed modifications to the standard methods used to develop Bayesian Belief Networks in order to deal with divergent information originated from epistemic uncertainty of risks. The\ud capacity of the proposed approach to provide better information to support risk related decision making is verified by means of an illustrative application to risk factors involved in the construction of cross passages between tunnels tubes in soft soils

A bayesian nework based risk model for volume loss in soft soils in mechanized bored tunnels

Volume loss is one of the most important risks when boring a tunnel. This is particularly true when a tunnel is being constructed in soft soils. The risk of excessive volume loss, if materialised can lead to large consequences such as damage in buildings on the surface. This paper describes the development and use of a Bayesian based risk model containing more than forty relevant risk factors associated with the occurrence of volume loss in soft soils in mechanized bored tunnels. The developed risk model takes into account additional factors other than those normally used in analytical methods to estimate volume loss. The considered risk factors involve issues related to the excavation process, design, monitoring, human factors as well as variables associated with ground conditions. By means of data elicited from tunnel experts and the analysis of the importance of the various factors using sensitivity analysis, the model is evaluated and its ability to provide information to derive specific risk measures is verified

Industrial time series modelling by means of the neo-fuzzy neuron

Abstract—Industrial process monitoring and modelling represents a critical step in order to achieve the paradigm of Zero Defect Manufacturing. The aim of this paper is to introduce the Neo-Fuzzy Neuron method to be applied in industrial time series modelling. Its open structure and input independency provides fast learning and convergence capabilities, while assuring a proper accuracy and generalization in the modelled output. First, the auxiliary signals in the database are analyzed in order to find correlations with the target signal. Second, the Neo-Fuzzy Neuron is configured and trained according by means of the auxiliary signal, past instants and dynamics information of the target signal. The proposed method is validated by means of real data from a Spanish copper rod industrial plant, in which a critical signal regarding copper refrigeration process is modelled. The obtained results indicate the suitability of the Neo-Fuzzy Neuron method for industrial process modelling.Postprint (published version

Contributions to industrial process condition forecasting applied to copper rod manufacturing process

Ensuring reliability and robustness of operation is one of the main concerns in industrial anufacturing processes , dueto the ever-increasing demand for improvements over the cost and quality ofthe processes outcome. In this regard , a deviation from the nominal operating behaviours implies a divergence from the optimal condition specification, anda misalignment from the nominal product quality, causing a critica! loss of potential earnings . lndeed, since a decade ago, the industrial sector has been carried out a significant effortAsegurar la fiabilidad y la robustez es uno de los principales objetivos en la monitorización de los procesos industriales, ya que estos cada vez se encuentran sometidos a demandas de producción más elevadas a la vez que se deben bajar costes de fabricación manteniendo la calidad del producto final. En este sentido, una desviación de la operación del proceso implica una divergencia de los parámetros óptimos preestablecidos, lo que conlleva a una desviación respecto la calidad nominal del producto final, causando así un rechazo de dicho producto y una perdida en costes para la empresa. De hecho, tanto es así, que desde hace más de una década el sector industrial ha dedicado un esfuerzo considerable a la implantación de metodologías de monitorización inteligente. Dichos métodos son capaces extraer información respecto a la condición de las diferentes maquinarias y procesos involucrados en el proceso de fabricación. No obstante, esta información extraída corresponde al estado actual del proceso. Por lo que obtener información respecto a la condición futura de dicho proceso representa una mejora significativa para poder ganar tiempo de respuesta para la detección y corrección de desviaciones en la operación de dicho proceso. Por lo tanto, la combinación del conocimiento futuro del comportamiento del proceso con la consecuente evaluación de la condición del mismo, es un objetivo a cumplir para la definición de las nuevas generaciones de sistemas de monitorización de procesos industriales. En este sentido, la presente tesis tiene como objetivo la propuesta de metodologías para evaluar la condición, actual y futura, de procesos industriales. Dicha metodología debe estimar la condición de forma fiable y con una alta resolución. Por lo tanto, en esta tesis se pretende extraer la información de la condición futura a partir de un modelado, basado en series temporales, de las señales críticas del proceso, para después, en base a enfoques no lineales de preservación de la topología, fusionar dichas señales proyectadas a futuro para conocer la condición. El rendimiento y la bondad de las metodologías propuestas en la tesis han sido validadas mediante su aplicación en un proceso industrial real, concretamente, con datos de una planta de fabricación de alambrón de cobre