Dicussions and Conclusion

The rationale for this publication emanated from the challenges facing the efficient delivery of construction projects in developing countries. Although, some aspects of the book focus on key applications in Cameroon, a holistic approach was adopted where an overview of challenges related to construction for developing countries was considered. To re-focus issues addressed in this book, this last Chapter provides a summary of what has been covered in each Chapter. Also, major achievements and challenges will be discussed. Nevertheless a construction matter can not be seriously handled today without taking into account environmental issues. For this reason, we will say just a few words about environment. Indeed, wherever infrastructures in general and buildings in particular through production and emission of toxic matters and gas have harmful consequences on the environment. They must be assessed and monitored to reduce the risk of pollution. Among many environmental assessment methods, life cycle assessment (LCA) seems to be the most suitable. Wherever, it involves a lot of data and must be handling with a lot of caution. The life cycle assessment tools used in developed countries is not suitable in developing countries context. It should be benefit for them to develop a simple and suitable methodology easy to manage, in order to propose an environmental impacts measurement of construction projects' and buildings. The High Environmental Quality, which a priori ensures minimized environmental impact and promotes the principles of sustainable development, is the solution that seems best suited for the design of many social infrastructures projected in developing countries. This is going to be for them the future challenge for the next decades.

Rule-Based Ontology Decision Model

In chapter 7, we examined some selected case study applications of some decision support systems. Those considered were the matrix-based used in determining labour cost, sub-chaining method, linear regression, optimization (i.e. minimization) technique and Markov decision process. As earlier discussed, our focus will be on rule-based decision support systems. This is because rule-based systems are more encompassing and can easily be employed to deal with complex decision about construction activities. Hence in this chapter, an overview of rule-based decision system will be examined.

Some Selected Case Study Applications

Having examined the modelling principles of underpinning based decision support systems applied to construction in Chapter 6, this chapter will now demonstrate their detail applications in construction practice. Specifically, 7 decision-support systems will be examined. The choices are based on the fact that data for use in the decision support models are available. The decision-support systems considered are the matrix-based used in determining labor cost, sub-chaining method, linear regression, optimization (i.e. minimization) technique, Markov decision process and rule-based systems.

Overview of Decision Support Systems Applied to Construction

The domain of construction is a very knowledge-intensive domain with so many factors involved. This implies undertaking any action requires an understanding of the different factors and how best to combine them to achieve a favourable and optimal outcome. Thus decision-making has been extensively used in the domain of construction. The aim of this chapter is to undertake a review of various decision support systems and to provide insights into their applications in the domain of construction. Specifically, the principle of cost index, sub-work chaining diagram method, linear regression and cost over-runs in time-overrun context (CCOTOV) model and Markov decision processes (MDP), ontology and rule-based systems have been reviewed. Based on the review the Markov decision processes (MDP), ontology and rule-based systems were chosen as the more suitable for the cost control case considered in this study.

Bridging Modernity by Improving Informal Sector for Substantially Industrialized Construction in Developing Countries

We have argued in Chapters 1-3 that the construction industry in developing countries is dominated by the strong presence of SMEs and “jobbers”. The informal sector workers constitute the essential provider of human resources construction industry. So far, the weaknesses of the construction industry in developing countries have been identified. As a result, various decision models were proposed for largely improving labor cost management and scheduling (time) with the aim of improving productivity. In a single volume like this, it is unrealistic to cover all aspects to improve performance. This chapter will now provide ideas on how quality of projects can also be improved so as to maintain a balance between cost, time and quality. Furthermore, the management of onsite workshops that can lead to construction productivity will be examined.

Modeling Principles of Decision Support Systems

In Chapter 5, some mathetical techniques that underpin decision support systems have been examined. The rationale behind reviewing the mathematical techniques was to facilitate understanding of their applications. This chapter will build on Chapter 5, to examine the modelling intentions behind some decisions in the domain of construction. Within finite time and resources, the matrix-based, optimization techniques and the sub-works chaining diagram (SWCD techniques will be further explored.

Case Study Applications in Software Environments

The previous chapter examined the concepts of Semantic Web. In order to build a case for the Semantic, the weaknesses of the current Web system was discussed. Furthermore the different concepts of the Semantic especially ontologies and SWRL rules were examined. Rules based on SWRL were modelled to capture different aspects of construction labour cost estimation. This Chapter will demonstrate its implementation of the developed rules in Chapter 8 in a software environment. The software used for the implementation is Protégé-OWL, one of the most popular ontology engineering software.

General Introduction

The construction industry is one of the most important sectors in the economy of each country. However, it has been noted with a multiplicity of problems that have negative impacts on efficiency, productivity and quality. While these problems are being tackled in the developed countries using advanced decision support systems, knowledge of such systems applied to construction projects in developing countries is sketchy. Being a general introductory chapter, this chapter will sign-post everything covered in this book.

Selected Mathematical Theories Underpinning Decision Models

In Chapter 4, various decision support systems have been examined. The rational for Chapter 4 was to appraise the diiferent decision-support systems that have been used in construction without necessarily detailing the complexities and mathematical underpinnings. This chapter will provide the theory that underpins some selected decision support systems. These are regression models (RLM), artificial neural networks (ANN), Matrices, Markov decision processes (MDP) and the ontology rule-based decision support systems.

Challenges Facing Building Construction in Developing Countries

In Chapter 2, an overview of construction practices in developing countries was presented. It is now well-established that the industry faced enormous challenges. To overcome these challenges and hence improve efficiency and productivity requires a deeper understanding of the challenges. In this chapter, the main challenges related to construction material and equipment, man-power and management will be examined. The consequences of these challenges which often lead cost overruns and delays will be discussed. The aim of this chapter is to provide a deeper understanding of these challenges so as to inform the choice of the decision support systems to be used in the subsequent chapters.