Artificial neural network predictive modelling for optimum resource operation for blockwork erection case study: Kampala district.
Ankunda, Ashley Bettina
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Efficient resource utilization is crucial for achieving optimal performance in construction projects. In the context of blockwork erection, the allocation and coordination of resources play a significant role in ensuring productivity, cost-effectiveness, and timely completion. This paper proposes the application of artificial neural network (ANN) predictive modelling to optimize resource operation in blockwork erection. The study focuses on developing an ANN-based predictive model that takes into account various input parameters such as project characteristics, workforce availability, material type and availability, and task dependencies, among others. The model aims to predict the optimal allocation of resources, including labor, materials, and equipment, to maximize productivity and minimize overall project duration. To develop the ANN model, historical data from past blockwork erection projects was collected, encompassing the various project parameters and resource allocation scenarios. This data was used for training, validating, and testing the ANN model. The model was designed to learn the complex relationships between input parameters and resource allocation outcomes, enabling it to make accurate predictions for new project scenarios. The proposed ANN model offers several advantages over traditional resource allocation approaches. By leveraging the computational power of neural networks, it can capture and learn from intricate patterns within the data, leading to improved prediction accuracy. Additionally, the model can adapt and update its predictions as new data becomes available during project execution, enhancing its effectiveness in dynamic construction environments. The research outcomes provide valuable insights into the optimal allocation and operation of resources for blockwork erection. The ANN model serves as a decision support tool for construction project managers, enabling them to make informed resource allocation decisions and enhance project performance. Ultimately, the application of this predictive modelling approach can lead to improved efficiency, cost savings, and the timely completion of blockwork construction projects.