The Analytic Hierarchy Process (AHP) approach is widely used for multiple criteria decision-making in construction management. However, the traditional AHP requires that decision makers remain consistent in making pairwise comparisons among numerous decision criteria. Accurate expression of relative preferences on the criteria is difficult for decision makers due to the limitations of the 9-value scale of Saaty. Although Saaty proposed a method to assess the consistency of pairwise comparisons, no automatic mechanism exists for improving the consistency for AHP. This work proposes an adaptive AHP approach (A3) that uses a soft computing scheme, Genetic Algorithms, to recover the real number weightings of the various criteria in AHP and provides a function for automatically improving the consistency ratio of pairwise comparisons. A real world construction management example for determining the weightings of the multiple criteria for a best-value bid is chosen as a case study to demonstrate the applicability of the proposed A3. The application results show that the proposed A3 is superior to the traditional AHP in terms of cost effectiveness, timeliness, and improved decision quality.