Selecting a new bridge type at the conceptual design phase is subject to many weaknesses in the processes conducted. Given that the engineers’ decisions are based on their subjectivity, it is worthwhile to establish a Decision Support System (DSS) to effectively address different problems of consistency in decisions. In designing a new bridge, many factors, such as the cost and aesthetic appearance of the bridge, have to be considered due to their ability to affect the final decision. Generally, decision-makers will base their final design decisions on those factors as well as on human subjectivity.
The objective of this research is to propose a methodology to develop systematic procedures that can help decision-makers select the most appropriate bridge type with its diverse components and to forecast its Life-Cycle Cost (LCC) and other characteristics such as the level of public satisfaction and the environmental sustainability of the selected bridge type. The proposed methodology integrates a decision support system with a relevant data structure within an artificial intelligence (AI) environment and bridge information management tools in order to reduce the impact of human subjectivity on the decisions taken during the conceptual design stage of a bridge’s life. The Artificial Neural Network (ANN) with its back-propagation algorithm is adopted in order to identify the appropriate solution by setting up its engine guidelines. Elements of the ANN layers (Engine model) which include: input, hidden and output layers, have to be described based on a systematic and standardized process. The proposed methodology has the potential to be used at lower levels to determine other bridge components such as vertical structures, foundations, and connection types. The objectives of the proposed methodology are as follows: (a) Highlighting the influence of human subjectivity on the decision-making process; (b) Listing and ranking the potential alternatives in term of their performance criteria; (c) Ensuring equivalent and fair consideration for selected factors affecting the decision, and especially reducing the possibility of missing or overlooking the impact of some factors that could be ignored while proceeding with making the right decision by using conventional decision-making approaches; and (d) Developing a systematic methodology that can be considered as a guideline for further use within any decision-making environment, based on a relevant historical database and experts’ input.
For public benefit, governmental and private agencies may use this DSS in order to provide a suitable solution abiding by different opinions in a systematic way taking into consideration the factors that have most influence.
A case study has been conducted with appropriate questionnaires to collect the needed data from experts, the public and previous project sites. This case study has shown the influence of decision maker subjectivity and how it could be controlled by inducing expert opinions through questionnaires to collect valuable data that have influence on the final decision. Also, data related to existing similar projects in the same area have been collected and used in order to show their influence on the results. Data were manipulated in order to analyze them and to show their accuracy and influence on the results. For that, a sensitivity analysis has been conducted in order to determine how the final decision could be affected by a fluctuation in the decision maker opinions contained in the input data for the proposed method.