publications
publications by categories in reversed chronological order. generated by jekyll-scholar.
2022
- ASCELarge-Scale Automated Sustainability Assessment of Infrastructure Projects Using Machine Learning Algorithms with Multisource Remote Sensing DataAmiradel Shamshirgaran, Seyed Hossein Hosseini Nourzad, Hamidreza Keshtkar, and 2 more authorsJournal of Infrastructure Systems, 2022
Considering the magnitude, lifespan, and environmental impacts of physical infrastructures, integration of sustainability with development policies has proved to be indispensable; accordingly, several rating systems were nationally developed to enhance implementing sustainability into physical infrastructures. Lack of automation and strategic outlook of the conventional approach to infrastructures? sustainability assessment, exacerbated by the lengthy and costly processes involved, highlights the necessity of adopting comprehensive and innovative measures. This paper principally aims at extending the scope of sustainability rating systems such as Envision by proposing a framework for large-scale and automated assessment of infrastructures. Based on the proposed framework, a single model was developed incorporating remote sensing and GIS techniques alongside the support vector machine (SVM) algorithm into the Envision rating system. The proposed model adds a certain degree of automation in assessment process regarding the criterion N.W.1.2 of Envision rating system (i.e., provide wetland and surface water buffers) as a starting point toward entire automation of the Envision system. Given the quantitative scale of the criterion N.W.1.2, our model automatically extracts (1) wetlands, (2) waterbodies, and (3) roadways through Optical Satellite_Sentinel-2A, Synthetic Aperture Radar (SAR) Satellite_ALOS-1 imagery, and shapefile from Florida Department of Transportation (FDOT). The image-based model then examines whether certain applicable specifications of Envision scoring system are met. The level of achievement is determined, and the final score in the criteria N.W.1.2 is calculated afterward. The results indicate that more than half of the existing road segments in the study area failed to obtain the minimum required score, regulated by Envision. This emphasizes the criticality of considering sustainability indicators in future infrastructure planning. In addition, the validated results confirm the feasibility of automation of other indicators of the Envision system that will help authorities see the bigger picture and make more sustainable decisions for future practices and policies.
@article{ASCE, title = {{Large-Scale Automated Sustainability Assessment of Infrastructure Projects Using Machine Learning Algorithms with Multisource Remote Sensing Data}}, author = {Shamshirgaran, Amiradel and Hosseini Nourzad, Seyed Hossein and Keshtkar, Hamidreza and Golabchi, Mahmood and Sadeghi, Mehrdad}, journal = {Journal of Infrastructure Systems}, volume = {28}, issue = {4}, year = {2022}, doi = {https://doi.org/10.1061/(ASCE)IS.1943-555X.0000703} } - ELSEVIERCustomisation of green buildings assessment tools based on climatic zoning and experts judgement using K-means clustering and fuzzy AHPMehrdad Sadeghi, Reza Naghedi, Amiradel Shamshirgaran, and 2 more authorsBuilding and Environment, 2022
Utilising green building regulations and classifications by using well-known assessment tools such as LEED can be challenging in a country with various climates due mainly to specific sustainability priorities for each climate. This paper presents a new framework to customise assessment tools of green buildings for regions or countries with various climates. The framework comprises K-means method to cluster various climates of the region combined with the silhouette value (SV) for clustering verification and local experts’ judgement for local customisation of green building assessment tools. The Fuzzy analytical hierarchy process (AHP) is used to adjust the regulations for each climatic zone. The proposed methodology is demonstrated by its application to the real-world case study of Iran. The K-means clustering with SV divides the country into four distinct climatic zones each representing with four meteorological parameters (MP, DTR, CDD, and HDD). Results show each climatic zone can take weights for sustainability categories and criteria based on its climate e.g. higher weight for “Water Efficiency” in zones with low rainfall and higher weight for “Energy and Atmosphere” in zones with heating or cooling needs. Results also show the two categories of “Energy and Atmosphere” and “Water Efficiency” take the largest weights in all zones by an average of almost 27 and 26%. These two categories, alongside with “Sustainable Site”, had the most changes in their weights for each climatic zone. The findings of this research reveal the effects of local climates on sustainability priorities of a green building assessment tool.
@article{ELSEVIER, title = {{Customisation of green buildings assessment tools based on climatic zoning and experts judgement using K-means clustering and fuzzy AHP}}, author = {Sadeghi, Mehrdad and Naghedi, Reza and Shamshirgaran, Amiradel and Behzadian, Kourosh and Tabrizi, Mohammad Reza}, journal = {Building and Environment}, volume = {223}, issue = {109473}, year = {2022}, doi = {https://doi.org/10.1016/j.buildenv.2022.109473} }
2018
- University of TehranAutomated Sustainability Assessment of Natural World in Infrastructure Projects: A Machine Learning ApproachAmiradel Shamshirgaran, and Seyed Hossein Hosseini NourzadInternational Conference on Natural Resources Management in Developing Countries, 2018
Infrastructures are problematic in nature. Considering the size, lifespan and importance, infrastructures greatly impact their surrounding environment and nearby communities. Hence, the concept of sustainability has turned into an indispensable part of evaluating infrastructure’s performance. However, today’s infrastructures overall grade is impressively low, even in highly developed countries. This paper focuses on bringing computational tools (e.g., machine learning and machine vision) to environmental sustainability assessment of infrastructures in order to automate assessing the extent of sustainability based on Envision rating system for sustainable infrastructure. The machine learning conceptual model that developed in this study, concentrates on Natural World category of Envision rating system, and uses aerial images from Google Earth’s database as inputs. After qualification and classification of images, three proposed machine learning models (Siting, Land and Water, and Biodiversity) assess the performance of infrastructures upon given criteria. Then, an overall score based on the weights of criteria grants to the infrastructure’s project. Considering the concept of automation, although time-consuming, error prone processes and human dependency of current assessing methods turns into accelerated, accurate and automated ones, but the fact that reaching to a sufficient level of autonomy is highly challenging should not be neglected.
@article{University_of_Tehran, title = {{Automated Sustainability Assessment of Natural World in Infrastructure Projects: A Machine Learning Approach}}, author = {Shamshirgaran, Amiradel and Hosseini Nourzad, Seyed Hossein}, journal = {International Conference on Natural Resources Management in Developing Countries}, year = {2018}, doi = {civilica.com/doc/780472} }