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In construction engineering, a schedule’s input data, which is usually not exactly known in the planning phase, is considered deterministic when generating the schedule. As a result, construction schedules become unreliable and deadlines are often not met. While the optimization of construction schedules with respect to costs and makespan has been a matter of research in the past decades, the optimization of the robustness of construction schedules has received little attention. In this paper, the effects of uncertainties inherent to the input data of construction schedules are discussed. Possibilities are investigated to improve the reliability of construction schedules by considering alternative processes for certain tasks and by identifying the combination of processes generating the most robust schedule with respect to the makespan of a construction project.
In order to minimize the probability of foundation failure resulting from cyclic action on structures, researchers have developed various constitutive models to simulate the foundation response and soil interaction as a result of these complex cyclic loads. The efficiency and effectiveness of these model is majorly influenced by the cyclic constitutive parameters. Although a lot of research is being carried out on these relatively new models, little or no details exist in literature about the model based identification of the cyclic constitutive parameters. This could be attributed to the difficulties and complexities of the inverse modeling of such complex phenomena. A variety of optimization strategies are available for the solution of the sum of least-squares problems as usually done in the field of model calibration. However for the back analysis (calibration) of the soil response to oscillatory load functions, this paper gives insight into the model calibration challenges and also puts forward a method for the inverse modeling of cyclic loaded foundation response such that high quality solutions are obtained with minimum computational effort. Therefore model responses are produced which adequately describes what would otherwise be experienced in the laboratory or field.