Shantanu Kumar
Current Organizations / Institutes
The organizations/institutes from the scholar's latest article
Research Subjects
Shantanu Kumar has a total of 2 co-authors. Here are the top co-authors by number of shared publications:
Mohammed S. Hashem M. Mehany
Department of Construction Management, Colorado State University, Fort Collins, CO 80521, USA
1 shared publication- Mohammed S. Hashem M. Mehany
Associate Professor, Dept. of Construction Management, Colorado State Univ., Fort Collins, CO 80523. ORCID:
1 shared publication
Latest Publications
- 17 August 2021
- Published by Elsevier in Socio-Economic Planning Sciences
- Vol. 79, 101141
A standardized framework for quantitative assessment of cities’ socioeconomic resilience and its improvement measures
- 17 August 2021
- Published by Elsevier in Socio-Economic Planning Sciences
- Vol. 79, 101141
A standardized framework for quantitative assessment of cities’ socioeconomic resilience and its improvement measures
- 1 May 2020
- Published by Canadian Science Publishing in Canadian Journal of Civil Engineering
- Vol. 47 (5) , 596-608
Optimizing the cost, LEED credits, and time trade-offs using a genetic algorithmic model
Over the last century, the complexity of construction projects has increased exponentially and its factors (e.g., time, budget, and quality) have generated complicated trade-offs. This research focuses on the trade-off between time, cost, and sustainability represented in Leadership in Energy and Environmental Design (LEED) credits (particularly materials and resources). The research is broken into preliminary and validation studies wherein the preliminary study uses an exhaustive search to find the optimized solution. In the validation case study, dataset size increased exponentially, and it became computationally incompatible to find the optimized solution. Genetic algorithm (GA) is used to find the optimized solution based on user-defined priority factors. Usage of GA is validated using the preliminary study data and then applied to the validation study data. A trade-off is seen between the priority factors and the optimized solution. The optimization model is successful in minimizing the time and cost, concurrently maximizing the points associated with LEED credits for a validation case study.
- 1 May 2020
- Published by Canadian Science Publishing in Canadian Journal of Civil Engineering
- Vol. 47 (5) , 596-608
Optimizing the cost, LEED credits, and time trade-offs using a genetic algorithmic model
Over the last century, the complexity of construction projects has increased exponentially and its factors (e.g., time, budget, and quality) have generated complicated trade-offs. This research focuses on the trade-off between time, cost, and sustainability represented in Leadership in Energy and Environmental Design (LEED) credits (particularly materials and resources). The research is broken into preliminary and validation studies wherein the preliminary study uses an exhaustive search to find the optimized solution. In the validation case study, dataset size increased exponentially, and it became computationally incompatible to find the optimized solution. Genetic algorithm (GA) is used to find the optimized solution based on user-defined priority factors. Usage of GA is validated using the preliminary study data and then applied to the validation study data. A trade-off is seen between the priority factors and the optimized solution. The optimization model is successful in minimizing the time and cost, concurrently maximizing the points associated with LEED credits for a validation case study.