CONBPS : an expert system to improve the efficiency of the construction process

The aim of the research outlined in this paper is to develop a best practice process model for building projects based on the use of an expert system. The CONstruction Best Practice System (CONBPS) focusses on projects which are based on the traditional procurement strategy, using the JCT 80 standard form of contract. The model clearly identifies the sequence of construction activities. It also identifies the roles and responsibilities of the major parties on the building team and the issues within the project cycle, which can prove critical to project success. The system incorporates many user-friendly functions, including the provision of multi-choice icons and the provision of an on-line help function. Besides, it also provides interim and final reports which are used to advise the participants on the success factors that they have ignored and to which aspects they should pay more attention. A framework was initially developed focussing on the whole design process with a full knowledge-based system developed for the Inception Stage. CONBPS can be used as a teaching/learning tool to assist teachers and students to better understand the construction process. Also, it could prove useful to project managers and all the participants in the construction process.

Development of an expert system modelling the construction process

The construction industry in the UK needs to improve its performance and provide clients with an improved level of satisfaction. The inefficient design and construction process is usually criticised as one of the main causes of poor performance.

A new construction process system, CONstruction Best Practice System (CONBPS), has been developed based on the use of an expert system. CONBPS is based on the traditional procurement strategy as it is probably the most popular procurement method in the UK and yet it is subject to most criticism. This model clearly identifies the roles and responsibilities of the major parties within the building team and identifies the activities and the key issues within the project cycle. The completed model reflects the full project cycle from inception to completion.

The prototype of this system has been demonstrated to the construction participants for their comments. The practitioners included architects, quantity surveyors, planning supervisors, private and public clients. The method of collecting data was through the use of semi-structured interviews.

Following feedback from practitioners, the CONBPS has been updated. This version is more robust; besides, it is more practical and user-friendly as it incorporates the comments from practitioners, who are also the potential users.

The primary aim of this paper is to discuss the development of the updated CONBPS. The improvement of the updated CONBPS includes the information for constructing the system, the computerised functions, system structure, knowledge representation structure and the system operation.

Development of construction best practice expert system

The performance level of construction industry in the UK is generally considered to be low. The reasons for this situation are twofold, firstly due to the temporary organisational structure of construction team and secondly the inefficient construction process. Previous research in this area has focused on developing a generic model to represent the construction process. It is necessary to develop a process model, which clearly identifies the roles and responsibilities of the major parties on the building team and identifies the key issues within the project cycle. The method for presenting this model is by using an expert system. The primary aim of this paper is to discuss the development of the CONstruction Best Practice System (CONBPS). The theoretical framework of CONBPS and the development and evaluation of the system will be described. The future research will also be discussed. Finally, the advantage of this model will be identified.

A novel field learning algorithm for dual imbalance text classification

Fish-net algorithm is a novel field learning algorithm which derives classification rules by looking at the range of values of each attribute instead of the individual point values. In this paper, we present a Feature Selection Fish-net learning algorithm to solve the Dual Imbalance problem on text classification. Dual imbalance includes the instance imbalance and feature imbalance. The instance imbalance is caused by the unevenly distributed classes and feature imbalance is due to the different document length. The proposed approach consists of two phases: (1) select a feature subset which consists of the features that are more supportive to difficult minority class; (2) construct classification rules based on the original Fish-net algorithm. Our experimental results on Reuters21578 show that the proposed approach achieves better balanced accuracy rate on both majority and minority class than Naive Bayes MultiNomial and SVM.

Fuzzy haptic augmentation for telerobotic stair climbing

Teleoperated robotic systems provide a valuable solution for the exploration of hazardous environments. The ability to explore dangerous environments from the safety of a remote location represents an important progression towards the preservation of human safety in the inevitable response to such a threat. While the benefits of removing physical human presence are clear, challenges associated with remote operation of a robotic system need to be addressed. Removing direct human presence from the robot's operating environment introduces telepresence as an important consideration in achieving the desired objective. The introduction of the haptic modality represents one approach towards improving operator performance subject to reduced telepresence. When operating in an urban environment, teleoperative stair climbing is not an uncommon scenario. This work investigates the operation of an articulated track mobile robot designed for ascending stairs under teleoperative control. In order to assist the teleoperator in improved navigational capabilities, a fuzzy expert system is utilised to provide the teleoperator with intelligent haptic augmentation with the aim of improving task performance.

Computer aided ultrasonic thickness strain analysis of automotive sheet metal stampings using FEA information

This paper discusses a computer-aided methodology for thickness strain analysis (TSA) of sheet metal stampings using geometry and strain information that is extracted from finite element analysis (FEA) results. The system utilises both FEA results and an ultrasonic gauge capability expert system to assist press shop personnel, providing them with capabilities such as optimum measurement point location and an estimate of gauge error. Key advantages of this enhanced TSA methodology are related to overall efficiency and accuracy gains.

Fuzzy concepts and formal methods

It has been recognised that formal methods are useful as a modelling tool in requirements engineering. Specification languages such as Z permit the precise and unambiguous modelling of system properties and behaviour. However some system problems, particularly those drawn from the information systems problem domain, may be difficult to model in crisp or precise terms. It may also be desirable that formal modelling should commence as early as possible, even when our understanding of parts of the problem domain is only approximate. This thesis suggests fuzzy set theory as a possible representation scheme for this imprecision or approximation. A fuzzy logic toolkit that defines the operators, measures and modifiers necessary for the manipulation of fuzzy sets and relations is developed. The toolkit contains a detailed set of laws that demonstrate the properties of the definitions when applied to partial set membership. It also provides a set of laws that establishes an isomorphism between the toolkit notation and that of conventional Z when applied to boolean sets and relations. The thesis also illustrates how the fuzzy logic toolkit can be applied in the problem domains of interest. Several examples are presented and discussed including the representation of imprecise concepts as fuzzy sets and relations, system requirements as a series of linguistically quantified propositions, the modelling of conflict and agreement in terms of fuzzy sets and the partial specification of a fuzzy expert system. The thesis concludes with a consideration of potential areas for future research arising from the work presented here.

A characterization theorem for t-representable n-dimensional triangular norms

n-dimensional fuzzy sets are an extension of fuzzy sets that includes interval-valued fuzzy sets and interval-valued Atanassov intuitionistic fuzzy sets. The membership values of n-dimensional fuzzy sets are n-tuples of real numbers in the unit interval [0,1], called n-dimensional intervals, ordered in increasing order. The main idea in n-dimensional fuzzy sets is to consider several uncertainty levels in the memberships degrees. Triangular norms have played an important role in fuzzy sets theory, in the narrow as in the broad sense. So it is reasonable to extend this fundamental notion for n-dimensional intervals. In interval-valued fuzzy theory, interval-valued t-norms are related with t-norms via the notion of t-representability. A characterization of t-representable interval-valued t-norms is given in term of inclusion monotonicity. In this paper we generalize the notion of t-representability for n-dimensional t-norms and provide a characterization theorem for that class of n-dimensional t-norms. © 2011 Springer-Verlag Berlin Heidelberg.

Bayesian networks and decision trees in the diagnosis of female urinary incontinence

This study compares the effectiveness of Bayesian networks versus Decision Trees in modeling the Integral Theory of Female Urinary Incontinence diagnostic algorithm. Bayesian networks and Decision Trees were developed and trained using data from 58 adult women presenting with urinary incontinence symptoms. A Bayesian Network was developed in collaboration with an expert specialist who regularly utilizes a non-automated diagnostic algorithm in clinical practice. The original Bayesian network was later refined using a more connected approach. Diagnoses determined from all automated approaches were compared with the diagnoses of a single human expert. In most cases, Bayesian networks were found to be at least as accurate as the Decision Tree approach. The refined Connected Bayesian Network was found to be more accurate than the Original Bayesian Network accurately discriminated between diagnoses despite the small sample size. In contrast, the Connected and Decision Tree approaches were less able to discriminate between diagnoses. The Original Bayesian Network was found to provide an excellent basis for graphically communicating the correlation between symptoms and laxity defects in a given anatomical zone. Performance measures in both networks indicate that Bayesian networks could provide a potentially useful tool in the management of female pelvic floor dysfunction. Before the technique can be utilized in practice, well-established learning algorithms should be applied to improve network structure. A larger training data set should also improve network accuracy, sensitivity, and specificity.

Practitioners' opinions on a new construction process model

Construction projects have been criticised for low performance for many decades. One of the reasons for this situation is the temporary multi-organisation of the construction team; the ultimate reason may be due to the divergent background and specialist skills of the project team members. The aim of this paper is to investigate the different opinions of construction participants within the construction process. It focuses on the projects which use the traditional procurement strategy in the building sector as this process is often perceived as being far from efficient. The methodology used in this research is by interview; the targeted interviewees include architects, quantity surveyors, clients and planning supervisors. An expert system which has been developed to model the construction process, called the CONstruction Best Practice System (CONBPS), has been demonstrated and the participants’ comments sought. The findings are that different construction parties have different perceptions of the construction process and their understanding of each other’s role is ambiguous.

An agent-based hybrid intelligent system for financial investment planning

Many complex problems including financial investment planning require hybrid intelligent systems that integrate many intelligent techniques including expert systems, fuzzy logic, neural networks, and genetic algorithms. However, hybrid intelligent systems are difficult to develop due to complicated interactions and technique incompatibilities. This paper describes a hybrid intelligent system for financial investment planning that was built from agent points of view. This system currently consists of 13 different agents. The experimental results show that all agents in the system can work cooperatively to provide reasonable investment advice. The system is very flexible and robust. The success of the system indicates that agent technologies can significantly facilitate the construction of hybrid intelligent systems.