In construction engineering, project schedules are commonly established by the critical path method. Nevertheless, these schedules often lead to substantial fluctuations in the resource profile that are not only impractical but also costly for the contractors to execute. Therefore, in order to smooth out the resource profile, construction managers need to perform resource-leveling procedures. This paper proposes a novel approach for resource leveling, named as resource leveling based on differential evolution (RLDE). The performance of the RLDE is compared to that of Microsoft Project software, the genetic algorithm, and the particle swarm optimization algorithm. Experiments have proved that the newly developed method can deliver the most desirable resource-leveling result. Thus, the RLDE is an effective method and it can be a useful tool for assisting managers/planners in the field of project management.
Permeation grouting is a commonly used approach for soil improvement in construction engineering. Thus, predicting the results of grouting activities is a crucial task that needs to be carried out in the planning phase of any grouting project. In this research, a novel artificial intelligence approach-autotuning support vector machine-is proposed to forecast the result of grouting activities that employ microfine cement grouts. In the new model, the support vector machine (SVM) algorithm is utilized to classify grouting activities into two classes: success and failure. Meanwhile, the differential evolution (DE) optimization algorithm is employed to identify the optimal tuning parameters of the SVM algorithm, namely, the penalty parameter and the kernel function parameter. The integration of the SVM and DE algorithms allows the newly established method to operate automatically without human prior knowledge or tedious processes for parameter setting. An experiment using a set of in situ data samples demonstrates that the newly established method can produce an outstanding prediction performance.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.