Generic Bayesian network models for making maintenance decisions from available data and expert knowledge

To maximise asset reliability cost-effectively, maintenance should be scheduled based on the likely deterioration of an asset. Various statistical models have been proposed for predicting this, but they have important practical limitations. We present a Bayesian network model that can be used for maintenance decision support to overcome these limitations. The model extends an existing statistical model of asset deterioration, but shows how (1) data on the condition of assets available from their periodic inspection can be used, (2) failure data from related groups of asset can be combined using judgement from experts and (3) expert knowledge of the deterioration’s causes can be combined with statistical data to adjust predictions. A case study of bridges on the rail network in Great Britain (GB) is presented, showing how the model could be used for the maintenance decision problem, given typical data likely to be available in practice

Operation reliability index for maintenance decision making in bridges

Maintenance management developed by several approach to optimize cost recently. Meanwhile decision making during operation is difficult task for mangers to keep them safe as well as stakeholder demands satisfaction and costs with regard to resources limitation. This paper presents an approach for decision making process to select alternatives based on their costs. For this manner, the uncertainty of defect probability combine with other availability and performance features to find priority of maintenance equipment and their reliability. This multi-dimensional decision making do not deal with the essential imprecision of subjective judgment based on quantitative evaluation. To demonstrate the use and capability of the model, a case study is presented. In this case, results shows the quality value combined by delay as an effectiveness parameters (91.08) and then decision tree will complete it by risk and reliability factors

Stakeholders’ impact on the reuse potential of structural elements at the end-of-life of a building: A machine learning approach

The construction industry, and at its core the building sector, is the largest consumer of non-renewable resources, which produces the highest amount of waste and greenhouse gas emissions worldwide. Since most of the embodied energy and CO2 emissions during the construction and demolition phases of a building are related to its structure, measures to extend the service life of these components should be prioritised. This study develops a set of easy-to-understand instructions to facilitate the practitioners in assessing the social sustainability and responsibility of reusing the load-bearing structural components within the building sector. The results derived by developing and then employing advanced machine learning techniques indicate that the most significant social factor is the perception of the regulatory authorities. The second and third ranks among the social reusability factors belong to risks. Since there is a strong correlation between perception and risk, the potential risks associated with reusing structural elements affect the stakeholders’ perception of reuse. The Bayesian network developed in this study unveil the complex and non-linear correlation between variables, which means none of the factors could alone determine the reusability of an element. This paper shows that by using the basics of probability theory and combining them with advanced supervised machine learning techniques, it is possible to develop tools that reliably estimate the social reusability of these elements based on influencing variables. Therefore, the authors propose using the developed approach in this study to promote materials' circularity in different construction industry sub-sectors

A Bayesian-Based Framework for Making Inspection and Maintenance Decisions from Data and Expert Knowledge

PhDIt is estimated that more than one-third of current infrastructure maintenance expenditure is wasted through poor decision-making. To make better decisions about maintenance, there is a need to provide better predictions of asset deterioration, and further, to use this information to plan inspections and appropriate repair actions. A number of statistical modelling techniques have been proposed to predict deterioration. However, these approaches can be difficult to apply in practice, for example when the time of deterioration is only known approximately from periodic inspections. Also, these approaches lack an easy way to incorporate knowledge about the deterioration process that can readily be considered when judgements are made by experienced maintainers. Moreover, in practice, the size of available datasets on deterioration is often limited; hence there is a need to blend data with knowledge. This thesis presents a framework for predicting deterioration and reasoning about the effects of repair using both the available data and expert knowledge that can support inspection and maintenance-related decisions. The framework uses Bayesian modelling, combining two types of Bayesian approaches: Bayesian statistical models and Bayesian Networks (BNs). Bayesian statistical models are used to estimate the parameter of statistical distributions, modelled as continuous variables. On the other hand, BNs model causal or influential relationships between (primarily) discrete variables to make predictions and can be based on elicited knowledge. This thesis builds on earlier work that combines these two forms of model, with both the continuous variables from Bayesian statistical models and the discrete variables of BNs. We refer this type of model to as a hybrid BN. The use of hybrid BNs is possible using an already existing algorithm that dynamically discretises continuous variables in a BN. BNs within the framework can be combined to model the different aspects of deterioration needed in different circumstances. The rate of deterioration can be learnt from censored deterioration data inferred from inspection records and knowledge elicited from engineers. Asset sharing similar characteristics can be grouped, and when a group contains only a few instances in the available data, data from related groups can be used to constrain the parameter learning. Deterioration through multiple condition states can be modelled. The deterioration of different components of complex structures can be combined. Finally, we model the effect of repair actions and show how to plan maintenance. A case study using data from the US National Bridge Inventory is used to validate the deterioration prediction models. We show how real-world inspection records can be integrated with engineering knowledge to predict the deterioration. Compared with other published approaches, the proposed models show better performance, especially when the group of similar assets is small. We then apply the models to reason about inspection and maintenance-related decisions. We use case studies of maintenance practices in the GB and US to show how the models can be used to assist both operational and strategic maintenance decision making. Many features of the proposed framework need to be adapted and combined to create a maintenance model applicable in a particular circumstance. Examples include the number of deterioration states, the decomposition of assets into components and the grouping of assets. The challenge is to create a complex and large-scale asset management system to allow a maintenance analyst to apply the framework, without needing expertise in Bayesian modelling. By representing our framework as a set of generic models using an extended form of BN – a probabilistic relational model – we show, with a simple prototype, how such a system could be realised

Artificial Intelligence in Civil Engineering

Artificial intelligence is a branch of computer science, involved in the research, design, and application of intelligent computer. Traditional methods for modeling and optimizing complex structure systems require huge amounts of computing resources, and artificial-intelligence-based solutions can often provide valuable alternatives for efficiently solving problems in the civil engineering. This paper summarizes recently developed methods and theories in the developing direction for applications of artificial intelligence in civil engineering, including evolutionary computation, neural networks, fuzzy systems, expert system, reasoning, classification, and learning, as well as others like chaos theory, cuckoo search, firefly algorithm, knowledge-based engineering, and simulated annealing. The main research trends are also pointed out in the end. The paper provides an overview of the advances of artificial intelligence applied in civil engineering

Evaluating building energy performance: a lifecycle risk management methodology

There is widespread acceptance of the need to reduce energy consumption within the built environment. Despite this, there are often large discrepancies between the energy performance aspiration and operational reality of modern buildings. The application of existing mitigation measures appears to be piecemeal and lacks a whole-system approach to the problem. This Engineering Doctorate aims to identify common reasons for performance discrepancies and develop a methodology for risk mitigation. Existing literature was reviewed in detail to identify individual factors contributing to the risk of a building failing to meet performance aspirations. Risk factors thus identified were assembled into a taxonomy that forms the basis of a methodology for identifying and evaluating performance risk. A detailed case study was used to investigate performance at whole-building and sub-system levels. A probabilistic approach to estimating system energy consumption was also developed to provide a simple and workable improvement to industry best practice. Analysis of monitoring data revealed that, even after accounting for the absence of unregulated loads in the design estimates, annual operational energy consumption was over twice the design figure. A significant part of this discrepancy was due to the space heating sub-system, which used more than four times its estimated energy consumption, and the domestic hot water sub-system, which used more than twice. These discrepancies were the result of whole-system lifecycle risk factors ranging from design decisions and construction project management to occupant behaviour and staff training. Application of the probabilistic technique to the estimate of domestic hot water consumption revealed that the discrepancies observed could be predicted given the uncertainties in the design assumptions. The risk taxonomy was used to identify factors present in the results of the qualitative case study evaluation. This work has built on practical building evaluation techniques to develop a new way of evaluating both the uncertainty in energy performance estimates and the presence of lifecycle performance risks. These techniques form a risk management methodology that can be applied usefully throughout the project lifecycle

Developing a skill profile prediction model for typologies of offsite construction

The aim of the current research was to develop a skill profile prediction model for the typologies of offsite construction (OSC). This research aim was achieved via five research objectives, from which the key findings and research outcomes were generated. OSC is perceived as an effective solution that can be implemented to address the issues evident in traditional construction. Some of the benefits OSC generated include better working conditions, improved productivity, efficiency, reduced wastage, and improved sustainability. Industry 4.0 has promoted OSC as a way to improve the uptake of new technologies in factory-based manufacturing and onsite assembly processes. Such technological advancements can have a significant impact on the skills used in OSC, as some of the existing skills in the construction industry may be eliminated or substituted (e.g., with those in other industries), and new skills may emerge based on industry needs. The magnitude of these possible OSC skill variations has not been a focus in previous studies on OSC. As such, the current research aimed to develop a skill profile prediction model for the typologies of OSC, through the adoption of a case-study based, qualitative research method. The research generated several significant outcomes: the validated OSC typology, the OSC skill classification developed through a logical approach, and a preliminary model for OSC skill prediction. The model can assist in forecasting future OSC skill requirements. Apart from the abovementioned outcomes, deriving a unit of measurement for skill prediction and identifying the complex, non-linear relationships between OSC types and skill variations represent the key outcomes of the research. As such, the research contributes to the current body of knowledge through its development of a unique OSC typology, a master list of onsite and offsite skills, an OSC skill prediction model and a methodology for the prediction of OSC skills. The focus on OSC elements in buildings rather than infrastructure projects, incorporating a limited number of case studies and developing a preliminary model rather than a market-ready product for OSC skill prediction represent the limitations of the research. Future research directions that could be taken to expand on the findings of the current research are as follows: evaluating the skill variations of different building types in the context of varying predominant materials and conducting a fundamentally quantitative study for OSC skills prediction

Diagnostic and prognostic analysis tools for monitoring degradation in aged structures

This research addresses the problem of prolonging the life of aged structures of historical value that have already outlived their original designed lives many times. While a lot of research has been carried out in the field of structural monitoring, diagnostics and prognostics for high tech industries, this is not the case for historical aged structures. Currently most maintenance projects for aged structures have focused on the instrumentation and diagnostic techniques required to detect any damage with a certain degree of success. This research project involved the development of diagnostic and prognostic tools to be used for monitoring and predicting the ‘health’ of aged structures. The diagnostic and prognostic tools have been developed for the monitoring of Cutty Sark iron structures as a first application. The concept of canary and parrot sensor devices are developed where canary devices are small, accelerated devices, which will fail according to similar failure mechanisms occurring in an aged structures and parrot devices are designed to fail at the same rate as the structure, thus mimicking the structure. The model-driven prognostic tool uses a Physics-of-Failure (PoF) model to predict remaining life of a structure. It uses a corrosion model based on the decrease in corrosion rate over time to predict remaining life of an aged iron structures. The data-driven diagnostic tool developed uses Mahalanobis Distance analysis to detect anomalies in the behaviour of a structure. Bayesian Network models are then used as a fusion method, integrating remaining life predictions from the model-driven prognostic tool with information of possible anomalies from data-driven diagnostic tool to provide a probability distribution of predicted remaining life. The diagnostics and prognostic tools are validated and tested through demonstration example and experimental tests. This research primarily looks at applying diagnostic and prognostic technologies used in high-tech industries to aged iron structures. In order to achieve this, the model-driven and data-driven techniques commonly used had to be adapted taking into consideration the particular constraints of monitoring and maintaining aged structures. The fusion technique developed is a novel approach for prognostics for aged structures and provides the flexibility often needed for diagnostic and prognostic tools