Adeer Khan

2023

1. 

   Consideration of simple approaches for structural health monitoring of structures in developing countries - An overview

   Adeer Khan, and Majid Ali

   Sustainable Structures and Materials, 2023

   Abstract HTML PDF

   Structural health monitoring (SHM) is an advanced tool that revolutionizes the capability of a structure to act as a responsive system – detecting changes and responding with performance analysis. But, for developing countries, its need is undermined due to its costly deployment. However, contrary to the costly belief, its use is direly needed in densely populated developing countries. Therefore, a simple and cheaper technique (despite lesser precision and accuracy) can help in the early detection of damages in structures. Unfortunately, SHM implementation has been inadequate in developing countries, consequently, limited literature is available to assess. Therefore, the main goal of this literature review is to identify and analyze various SHM approaches and then propose a simple yet effective approach for achieving the basic amenities of SHM. By analyzing previous highly reputable journals, it was deduced that vibration-based approaches are the most cost-effective and simplistic to implement, which have resurged recently due to the increased use of computational tools that minimize extraneous data and provide efficient noise removal. The use of combination techniques in SHM can be cost-effective and accessible for developing nations, providing solutions for infrastructure sustainability.

2022

1. 

   Machine learning-based monitoring and modeling for spatio-temporal urban growth of Islamabad

   Adeer Khan, and Mehran Sudheer

   The Egyptian Journal of Remote Sensing and Space Science, 2022

   Abstract HTML PDF

   LULC maps are important thematic maps that provide a baseline for monitoring, assessing, and planning activities. This study incorporates spatio-temporal land use/ land cover (LULC) monitoring (1991–2021) and urban growth modeling (2021–2041) of Islamabad, Pakistan to deduce the changes in various LULC classes in the past and the future by incorporating realistic influential thematic layers and Artificial Neural Network-Cellular Automata (ANN-CA) machine learning algorithms. Three decades of Landsat satellite imagery were used to classify LULC maps using a random forest algorithm with high Kappa indexes ranging from 0.93 to 0.97. Simulations for 2011 and 2021 were done for well-calibration of the model with Kappa (>0.85) and spatial similarity (>75%) using the MOLUSCE plugin in QGIS software. Future predictions were done for the years 2031 and 2041 to analyze and study the future urban growth patterns. The satellite-based LULC maps during 1991–2021 exhibited a 142.4 km2 increase in net urban growth. This had detrimental effects on other classes: net decrease of forests by 38.4 km2 and waterbodies by 2.9 km2. The projected increase of urban areas in 2021–2041 will be 58.2 km2. Visual urban sprawl assessment on LULC maps was done to highlight the type of sprawls. Overall, it was sensed that the city’s urbanization has been unplanned and erratic; leading to dire consequences on the environmental and urban systems. Therefore, the study necessitates better monitoring and better planning of urbanization by enforcing policies and necessary measures.