Skip to main content

Advertisement

Springer Nature Link
Log in

Geospatial mapping of potential aggregate resources using integrated GIS-AHP, geotechnical, petrographic and machine learning approaches

  • Research
  • Published:
Earth Science Informatics Aims and scope Submit manuscript

Abstract

The growing demand for natural aggregate resources in the construction industry requires the development of efficient techniques for identifying, demarcating, and quantifying suitable aggregate sources for minor and major projects. To accomplish this purpose, multi-criteria decision analyses (MCDA), including weighted overlay analysis (WOA), the analytic hierarchy process (AHP), and the Random Forest (RF) machine learning (ML) approaches, were employed to identify the most suitable aggregate sites in District Kurram, Pakistan. Moreover, comprehensive geotechnical and petrographic analyses were conducted on two distinct sites, affirming the efficacy of the MCDM approach for evaluating aggregate resources. The WOA results classify the region into low suitable 44%, moderately suitable 38%, and highly suitable 18% areas. Simultaneously, the AHP technique for resource extraction revealed a corresponding distribution with 39.53% lowly suitable, 29.12% moderately suitable, and 31.35% highly suitable, and the RF model classified 35.4% of the terrain as “lowly suitability,” 27.0% as “moderately suitable,” and 37.6% as “highly suitable,” showing an improved classification accuracy compared to traditional MCDA methods. Among the three models evaluated, the RF model, with the highest (AUC of 0.92), exhibited the best performance in aggregate suitability mapping, significantly surpassing the accuracy of the AHP (AUC of 0.88) and WOA (AUC of 0.83) models. Geotechnical and petrographic analyses validated the MCDA and ML approaches, confirming that the sites meet engineering standards. Simple regression analysis highlighted the crucial relationship, including a positive association between water absorption and Los Angeles abrasion value, and negative correlations between aggregate impact value with flakiness index, and Los Angeles abrasion value with elongation Index. Moreover, this research emphasizes the role of petrological content in influencing the engineering properties of rocks. Consequently, this integrated approach empowers informed decision-making by regional authorities, ensuring sustainable utilization for various civil engineering projects.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Subscribe and save

Springer+ Basic
$34.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11
Fig. 12
Fig. 13
Fig. 14
Fig. 15
Fig. 16
Fig. 17
Fig. 18
Fig. 19
Fig. 20
Fig. 21

Similar content being viewed by others

Data availability

All data generated or analyzed during this study are included in this manuscript. The machine learning code used was sourced from publicly available platforms, such as GitHub, and was modified to align with the objectives of our study. Additional information or data can be provided upon reasonable request from the corresponding author.

References

  • 127 A (American S for T and MC (1990) ASTM C 127-88, Standard Method of Test for Specific Gravity and Absorption of Coarse Aggregate. https://www.astm.org/c0127-88r01.html

  • Abbas SM (2012), Geology and structure of the westernmost hill ranges. Sadda area, Kurram agency, North Pakistan. http://nceg.uop.edu.pk/mphil-graduates.html

  • Abbasi NP, Abbasi GP (2024) A GIS modeling for the spatial planning and developmental attraction of Land Use Land Cover Pattern in Cooperative Sukkur Society District Sukkur, Sindh. J Dev Soc Sci 5:437–448

    Google Scholar 

  • Adebola M, Abdulazeez S (2014) The suitability of some selected Granite deposits for Aggregate Stone production in Road Construction. Int J Eng Sci Fed Univ Technol Niger 3:75–81

    Google Scholar 

  • Aguilera RF (2004) A triple porosity model for petrophysical analysis of naturally fractured reservoirs. Petrophysics-The SPWLA J Form Eval Reserv Descr 45:(2). https://onepetro.org/petrophysics/issue/45/02

  • Ahmed I, Basharat M, Sousa L, Mughal MS (2021) Evaluation of building and dimension stone using physicomechanical and petrographic properties: a case study from the Kohistan and Ladakh batholith, Northern Pakistan. Environ Earth Sci 80:1–17

  • Ahmed SF, Alam MS, Bin, Hassan M et al (2023a) Deep learning modelling techniques: current progress, applications, advantages, and challenges  56:3521–13617

  • Ahmed SF, Alam MS, Bin, Hassan M et al (2023b) Deep learning modelling techniques: current progress, applications, advantages, and challenges. Artif Intell Rev 56:13521–13617

    Article  Google Scholar 

  • Al-Ruzouq R, Shanableh A, Yilmaz AG et al (2019) Dam site suitability mapping and analysis using an integrated GIS and machine learning approach. Water 11:1880

    Article  Google Scholar 

  • Ali M, Yar P, Khan S et al (2022) Land use and land cover modification and its impact on biodiversity and the ecosystem services in District Kurram, Pakistan. https://doi.org/10.37360/blacpma.22.21.3.22

  • Ali N, Chen J, Fu X et al (2024a) Integrating machine learning ensembles for Landslide susceptibility mapping in Northern Pakistan. Remote Sens 16:988

    Article  Google Scholar 

  • Ali N, Fu X, Chen J et al (2024b) Advancing Reservoir evaluation: machine learning approaches for Predicting Porosity curves. Energies 17:3768

    Article  Google Scholar 

  • Amoako K, Ahenkorah I, Baffoe E et al (2018) Engineering Characterisation of aggregates from some selected areas in Kumasi, Ghana. Int J Adv Eng Manag Sci 4:239993

    Google Scholar 

  • ANJUM MN, Ali N, Rehman ZU et al (2018) Rock Aggregate potential of the Limestone Units in the Khyber formation, Khyber ranges, Pakistan. Int J Econ Environ Geol 9. https://doi.org/10.13140/RG.2.2.19394.84161

  • Asif AR, Islam I, Ahmed W et al (2022) Exploring the potential of Eocene carbonates through petrographic, geochemical, and geotechnical analyses for their utilization as aggregates for engineering structures. Arab J Geosci 15:1105

    Article  CAS  Google Scholar 

  • ASTM C (1997) C 88–90, standard test method for soundness of aggregate by use of sodium sulfate or magnesium sulfate. Annu B ASTM Stand 4:37

    Google Scholar 

  • ASTM International (2014) D7012-14: Standard Test Methods for Compressive Strength and Elastic Moduli of Intact Rock Core Specimens under Varying States of Stress and Temperatures. ASTM International. https://www.astm.org/d7012-14e01.html

  • ASTM (2016) C188-16 standard test method for density of cement. https://standards.globalspec.com/std/3863179/astm-c188-16

  • ASTM C170/C170M-17 A (2017) Standard Test Method for Compressive Strength of Dimension Stone. ASTM Int. https://webstore.ansi.org/search/findin=1&st=ASTM+C170%2FC170M-17

  • ASTM International (2008) C131/C131M: standard test method for resistance to degradation of small-size Coarse Aggregate by Abrasion and Impact in the Los Angeles Machine. ASTM International. https://www.astm.org/c0131_c0131m-20.html

  • Ayodele TR, Ogunjuyigbe ASO, Odigie O, Munda JL (2018) A multi-criteria GIS based model for wind farm site selection using interval type-2 fuzzy analytic hierarchy process: the case study of Nigeria. Appl Energy 228:1853–1869

    Article  Google Scholar 

  • Azarafza M, Hajialilue Bonab M, Derakhshani R (2022) A deep learning method for the prediction of the index mechanical properties and strength parameters of marlstone. Mater (Basel) 15:6899

    Article  CAS  Google Scholar 

  • Baek R, Heinrich M, Letouzé-Zezula G (2003) GIS-based assessment of aggregates in Carinthia (Austria). Geologija 46:333–337

    Google Scholar 

  • Barakat A, Ouargaf Z, Touhami F (2016) Identification of potential areas hosting aggregate resources using GIS method: a case study of Tadla-Azilal Region, Morocco. Environ Earth Sci 75:1–16

    Article  Google Scholar 

  • Bayisa R, Kumar RT, Seifu K (2015) Quarry site selection and geotechnical characterization of ballast aggregate for Ambo-Ijaji railway project in central Ethiopia: an integrated GIS and geotechnical approach. Engineering Geology for Society and Territory-Volume. Springer 6:329-335 

  • Beigbabayi B, Mobaraki MA (2012) Using AHP modeling and GIS to evaluate the suitability of site with climatic potential for cultivation of autumn canola in Ardabil Province. Ann Biol Res 3:2307–2317

    Google Scholar 

  • BENDER, FK & RAZA (1995) Geology of Pakistan. x + 414 pp.+ maps in box. Beitrage zur Regionalen Geologie der Erde, Band 25. Berlin, Stuttgart: Gebrüder Borntraeger.Geological Magazine 135.6 (1998): 819–842

  • Breiman L (2001) Random forests. Mach Learn 45:5–32

    Article  Google Scholar 

  • British Standard Institutions (1998) Testing aggregates-Part 105: Methods for determination of particle shape-Sect. 105.2 Elongation index of coarse aggregate. Bs 812–105.2 10

  • Brown TJ (2012) Mineral potential mapping: a new spatial decision support tool for industry and planners. In: Proceedings of the 16th Extractive Industry Geology Conference. Extractive Industry Geology Conferences Ltd, pp 76–87

  • Bryan DP (1994) Aggregate Resource Study of Southern Washoe County, Nevada. Nev Bur Min  Geol 94(3). https://archive.library.unr.edu/public/repositories/3/archival_objects/70995

  • British Standards Institution (1990) BS 812-110: 1990 Testing aggregates - part 110: Methods for determination of aggregate crushing value (ACV). Br Stand Inst. https://knowledge.bsigroup.com/products/testing-aggregates-methods-fordetermination-of-aggregate-crushingvalue-acv?version

  • British Standards Institution (1990) BS 812: 112, 1990 Testing aggregates part 112, Methods for Determination of Aggregate Impact Value (AIV). Br Stand Inst. https://studylib.net/doc/25684992/testing-aggregates.-method-fordetermination-of-aggregate

  • Cemiloglu A, Zhu L, Mohammednour AB et al (2023) Landslide susceptibility assessment for Maragheh County, Iran, using the logistic regression algorithm. Land 12:1397

    Article  Google Scholar 

  • Chowdhuri I, Pal SC (2024) Hydrochemical properties of groundwater and land use and land cover changes impact on agricultural productivity: an empirical observation and integrated framework approaches. J Geochemical Explor 258:107402

    Article  CAS  Google Scholar 

  • Chowdhury MS (2024) Comparison of accuracy and reliability of random forest, support vector machine, artificial neural network and maximum likelihood method in land use/cover classification of urban setting. Environ Challenges 14:100800

    Article  Google Scholar 

  • Costa FL, Torres AS, Neves RA (2016) Analysis of concrete structures deteriorated by alkali-aggregate reaction: case study. J Build Pathol Rehabil 1:1–8

    Google Scholar 

  • Cutler DR, Edwards TC Jr, Beard KH et al (2007) Random forests for classification in ecology. Ecology 88:2783–2792

    Article  Google Scholar 

  • Dang KB, Burkhard B, Windhorst W, Müller F (2019) Application of a hybrid neural-fuzzy inference system for mapping crop suitability areas and predicting rice yields. Environ Model Softw 114:166–180

    Article  Google Scholar 

  • Das BK, Saha A, Das AK et al (2024) Assessment of the physicochemical properties of the sediment from the Gangetic floodplain wetlands of West Bengal, India, with GIS and a statistical approach. Front Environ Sci 12:1397715

    Article  Google Scholar 

  • Diercks M-L, Grützner C, Welte J, Ustaszewski K (2023) Challenges of geomorphologic analysis of active tectonics in a slowly deforming karst landscape (W Slovenia and NE Italy). Geomorphology 440:108894

    Article  Google Scholar 

  • Dunham RJ (1962) Classification of carbonate rocks according to depositional textures. Am Assoc Pet Geol. https://pubs.geoscienceworld.org/aapg/books/edited-volume/1475/chapter-abstract/107178011/Classification-of-Carbonate-Rocks-According-to

  • El Jazouli A, Barakat A, Khellouk R (2019) GIS-multicriteria evaluation using AHP for landslide susceptibility mapping in Oum Er Rbia high basin (Morocco). Geoenvironmental Disasters 6:1–12

    Article  Google Scholar 

  • El Wamdeni Z, Aqnouy M, Mhamdi HS et al (2024) Inventory and analysis of quarries using Geographic Information System and Remote sensing techniques for Eco-friendly Quarrying practices. Ecol Eng Environ Technol 25.https://doi.org/10.12912/27197050/192057

  • Engidasew TA, Barbieri G (2014) Geo-engineering evaluation of Termaber basalt rock mass for crushed stone aggregate and building stone from Central Ethiopia. J Afr Earth Sci 99:581–594

    Article  Google Scholar 

  • Fattah KA, Lashin A (2016) Investigation of mud density and weighting materials effect on drilling fluid filter cake properties and formation damage. J Afr Earth Sci 117:345–357

    Article  CAS  Google Scholar 

  • Feizizadeh B, Blaschke T (2013) Land suitability analysis for Tabriz County, Iran: a multi-criteria evaluation approach using GIS. J Environ Plan Manag 56:1–23

    Article  Google Scholar 

  • Feng S, Hu Q (2007) Changes in winter snowfall/precipitation ratio in the contiguous United States. J Geophys Res Atmos 112

  • Gorkem C, Sengoz B (2009) Predicting stripping and moisture induced damage of asphalt concrete prepared with polymer modified bitumen and hydrated lime. Constr Build Mater 23:2227–2236. https://doi.org/10.1016/j.conbuildmat.2008.12.001

    Article  Google Scholar 

  • Heung B, Ho HC, Zhang J et al (2016) An overview and comparison of machine-learning techniques for classification purposes in digital soil mapping. Geoderma 265:62–77

    Article  Google Scholar 

  • Huabin W, Gangjun L, Weiya X, Gonghui W (2005) GIS-based landslide hazard assessment: an overview. Prog Phys Geogr 29:548–567

    Article  Google Scholar 

  • Hussain A, Yanjun Q (2012) Evaluation of Dina aggregates for pavement construction in Pakistan. Advanced materials research. Trans Tech Publ, pp 239–242. https://doi.org/https://doi.org/10.4028/www.scientific.net/AMR.548.239

  • Hussain J, Zhang J, Fitria F et al (2022a) Aggregate Suitability Assessment of Wargal Limestone for Pavement Construction in Pakistan. Open J Civ Eng 12:56–74

    Article  CAS  Google Scholar 

  • Hussain J, Zhang J, Lina X et al (2022b) Resource Assessment of Limestone Based on Engineering and Petrographic Analysis. 8:421–437

  • Hussain J, Zhang J, Iqbal SM et al (2023a) Exploring the potential of late permian aggregate resources for utilization in engineering structures through geotechnical, geochemical and petrographic analyses. Sci Rep 13:5088

    Article  CAS  Google Scholar 

  • Hussain J, Zhang J, Saleem A et al (2023b) Suitability Assessment constraints of potential Aggregate resources using an Integrated GIS Approach. J Mater Civ Eng 35:4023307

    Article  Google Scholar 

  • Hussaina K, Bina D, Asgharb A et al (2022) Geotechnical Parameter Assessment and Bearing Capacity Analysis for the Foundation Design. https://doi.org/10.26480/esmy.02.2022.120.129

  • Iluore K, Lu J (2022) Long short-term memory and gated recurrent neural networks to predict the ionospheric vertical total electron content. Adv Sp Res 70:652–665

    Article  Google Scholar 

  • Iluore K, Lu J, Ozegin KO (2024) Ionospheric GPS-VTEC forecasting using hybrid deep learning model (LSTM-CNN). J Space Saf Eng. https://doi.org/10.1016/j.jsse.2024.11.004

  • Ismaili M, Krimissa S, Namous M et al (2023) Assessment of soil suitability using machine learning in arid and semi-arid regions. Agronomy 13:165

    Article  CAS  Google Scholar 

  • Jiao S, Zhang X, Xu Y (2017) A review of Chinese land suitability assessment from the rainfall-waterlogging perspective: evidence from the Su Yu Yuan area. J Clean Prod 144:100–106

    Article  Google Scholar 

  • Kamal MA, Sulehri MA, Hughes DAB (2006) Engineering characteristics of road aggregates from northern Pakistan and the development of a toughness index. Geotech Geol Eng 24:819–831. https://doi.org/10.1007/s10706-005-6610-9

    Article  Google Scholar 

  • Kamran A, Ali L, Ahmed W et al (2022) Aggregate evaluation and Geochemical Investigation of Limestone for Construction Industries in Pakistan: an Approach for Sustainable Economic Development. Sustainability 14:10812

    Article  CAS  Google Scholar 

  • Karakaş A (2014) Defining the suitability of new crushed rock aggregate source areas in the North of Kocaeli Province using GIS. Bull Eng Geol Environ 73:1183–1197

    Article  Google Scholar 

  • Kazmi AH (1984) Geology of the Indus suture zone in the Mingora-Shangla area of Swat. Geol Bull Univ Peshawar 17:127–144

    Google Scholar 

  • Kerr B, Ferbey T, Levson VM (2005) Implementing geomatics technology for aggregate exploration, northeast british Columbia. Summ Act 83–86

  • Khajevand R (2023) Determining dry and saturated strength of rocks by using the Schmidt hammer. Iran J Sci 47:779–790

    Article  Google Scholar 

  • Khalid N, Hina S, Zaidi KS et al (2025) An investigation of feature reduction, transferability, and generalization in AWID datasets for secure Wi-Fi networks. PLoS ONE 20:e0306747

    Article  CAS  Google Scholar 

  • Khan W, Zhang K, Liang H (2024) Evaluating the neotectonic activities of the Eastern Makran at the northern edge of the triple junction in southwest Pakistan. Turkish J Earth Sci 33:176–191

    Article  Google Scholar 

  • Khanna SK, Justo CEG (1990) A text book on highway engineering. Nem Chand Bros, Roorkee

    Google Scholar 

  • Khatoon S, Hina H (2022) The Economic Geography of Road infrastructure in Pakistan: exploratory spatial data analysis (ESDA. J Dev Soc Sci 3:972–985

    Google Scholar 

  • Kwan AKH, Mora CF, Chan HC (1999) Particle shape analysis of coarse aggregate using digital image processing. Cem Concr Res 29:1403–1410. https://doi.org/10.1016/S0008-8846(99)00105-2

    Article  CAS  Google Scholar 

  • Labi S (2014) Introduction to civil engineering systems: a systems perspective to the development of civil engineering facilities. Wiley

    Google Scholar 

  • Langer WH (2001) Potential environmental impacts of quarrying stone in karst: a literature review. https://pubs.usgs.gov/of/2001/ofr-01-0484/

  • Liaw A, Wiener M (2002) Classification and regression by randomForest. R news 2:18–22

    Google Scholar 

  • Linke S, Lehner B, Ouellet Dallaire C et al (2019) Global hydro-environmental sub-basin and river reach characteristics at high spatial resolution. Sci data 6:283

    Article  Google Scholar 

  • Liu Y, Dai J, Guo L et al (2022) Porous hydrogen-bonded frameworks assembled from metal-nucleobase entities for Xe/Kr separation. CCS Chem 4:381–388

    Article  CAS  Google Scholar 

  • Macintosh A, Roberts H, Constable A (2017) An empirical evaluation of environmental citizen suits under the environment protection and biodiversity conservation Act 1999 (cth). Syd L Rev 39:85

    Google Scholar 

  • Malczewski J (2006) GIS-based multicriteria decision analysis: a survey of the literature. Int J Geogr Inf Sci 20:703–726

    Article  Google Scholar 

  • Malczewski J, Rinner C (2015a) Development of GIS-MCDA. Multicriteria decision analysis in geographic information science. Springer, pp 55–77

    Chapter  Google Scholar 

  • Malczewski J, Rinner C (2015b) Multicriteria decision analysis in geographic information science. Springer

    Book  Google Scholar 

  • Mao Y, Chen L, Nanehkaran YA et al (2023) Fuzzy-based intelligent model for rapid rock slope stability analysis using Qslope. Water 15:2949

    Article  Google Scholar 

  • Meissner CR Jr, Hussain M, Rashid MA, Sethi UB (1975) Geology of the Parachinar Quadrangle, Pakistana. Off. US Govt. Print. Off

  • Memarbashi E, Azadi H, Barati AA et al (2017) Land-use suitability in Northeast Iran: application of AHP-GIS hybrid model. ISPRS Int J Geo-Information 6:396

    Article  Google Scholar 

  • Mesa-Mingorance JL, Ariza-López FJ (2020) Accuracy assessment of digital elevation models (DEMs): a critical review of practices of the past three decades. Remote Sens 12:2630

    Article  Google Scholar 

  • Musarat MA, Alaloul WS, Khan AM et al (2024) A survey-based approach of framework development for improving the application of internet of things in the construction industry of Malaysia. Results Eng 101823. https://doi.org/10.1016/j.rineng.2024.101823

  • Naeem M, Jafri MK, Moustafa SSR et al (2016) Seismic and well log driven structural and petrophysical analysis of the Lower Goru formation in the Lower Indus Basin, Pakistan. Geosci J 20:57–75

    Article  CAS  Google Scholar 

  • Naeem M, Zafar T, Touseef Bilal M, Oyebamiji A (2019) Physical characterization and alkali carbonate reactivity (ACR) potential of the rocks from Bauhti Pind and Bajar area Hassan Abdal, Pakistan. SN Appl Sci 1:1–9. https://doi.org/10.1007/s42452-019-0736-5

    Article  CAS  Google Scholar 

  • Nanehkaran YA, Mao Y, Azarafza M et al (2021) Fuzzy-based multiple decision method for landslide susceptibility and hazard assessment: a case study of Tabriz, Iran. Geomech Eng 24:407–418

    Google Scholar 

  • Nanehkaran YA, Chen B, Cemiloglu A et al (2023) Riverside landslide susceptibility overview: leveraging artificial neural networks and machine learning in accordance with the United Nations (UN) sustainable development goals. Water 15:2707

    Article  Google Scholar 

  • Naveed, Ahsan, Muhammad Munawar Iqbal Gondal S-R (2012) Aggregate suitability studies of limestone outcrops in Dhak Pass, western salt range, Pakistan. Agric Appl Sci 4:69–75

    Google Scholar 

  • Neville AM (1995) Properties of concrete. Longman London

  • Nezami MR, de Bruijne MLC, Hertogh MJCM, Bakker HLM (2024) Assessment criteria for inter-organizational collaboration in interconnected infrastructure projects. Eng Constr Archit Manag

    Book  Google Scholar 

  • Nigar A, Li Y, Jat Baloch MY et al (2024) Comparison of machine and deep learning algorithms using Google Earth Engine and Python for land classifications. Front Environ Sci 12:1378443

    Article  Google Scholar 

  • Oates JAH (2008) Lime and limestone: chemistry and technology, production and uses. Wiley

    Google Scholar 

  • Ozegin KO, Ilugbo SO (2024) Evaluation of potentially susceptible flooding areas leveraging geospatial technology with multicriteria decision analysis in Edo State, Nigeria. Nat Hazards Res. https://doi.org/10.1016/j.nhres.2024.07.002

  • Petrounias P, Giannakopoulou PP, Rogkala A et al (2018) The effect of petrographic characteristics and physico-mechanical properties of aggregates on the quality of concrete. Minerals 8:577

    Article  CAS  Google Scholar 

  • Pourghasemi HR, Moradi HR, Fatemi Aghda SM et al (2014) GIS-based landslide susceptibility mapping with probabilistic likelihood ratio and spatial multi-criteria evaluation models (North of Tehran, Iran). Arab J Geosci 7:1857–1878

    Article  Google Scholar 

  • Qi B, Xu P, Wu C (2023) Analysis of the infiltration and water storage performance of recycled brick mix aggregates in sponge city construction. Water 15:363

    Article  Google Scholar 

  • Radočaj D, Jurišić M, Gašparović M et al (2021) Cropland suitability assessment using satellite-based biophysical vegetation properties and machine learning. Agronomy 11:1620

    Article  Google Scholar 

  • Rahman T, Sarkar K, Sahu S (2024) Correlations between dynamic Elastic properties and P-Wave velocity for different rock types. Indian Geotech J 54:699–706

    Article  Google Scholar 

  • Rehman SU, Ahmed M, Hasan F et al (2018) Aggregate suitability studies of middle jurassic samana suk formation exposed at Sheikh Aggregate suitability studies of middle jurassic samana suk formation exposed at Sheikh Budin Hill, Marwat Range, Pakistan. J Biodivers Environ Sci 12:159–168

    Google Scholar 

  • Rehman G, Zhang G, Rahman MU et al (2020) The engineering assessments and potential aggregate analysis of mesozoic carbonates of Kohat Hills Range, KP, Pakistan. Acta Geod Geophys 55:477–493. https://doi.org/10.1007/s40328-020-00301-9

    Article  Google Scholar 

  • Robinson GR, Kapo KE, Raines GL (2004) A GIS analysis to evaluate areas suitable for crushed stone aggregate quarries in New England, USA. Nat Resour Res 13:143–159

    Article  Google Scholar 

  • Saaty TL (1970) Optimization in integers and related extremal problems. https://cir.nii.ac.jp/crid/1130000796570059136

  • Saaty TL (2001) Decision making for leaders: the analytic hierarchy process for decisions in a complex world. RWS Publications. https://books.google.co.jp/books/about/Decision_Making_for_Leaders.htmlid=c8KqSWPFwIUC&redir_esc=y

  • Sabbaghi H, Tabatabaei SH (2023) Regimentation of geochemical indicator elements employing convolutional deep learning algorithm. Front Environ Sci 11:1076302

    Article  Google Scholar 

  • Sabbaghi H, Tabatabaei SH, Fathianpour N (2024) Geologically-constrained GANomaly network for mineral prospectivity mapping through frequency domain training data. Sci Rep 14:6236

    Article  CAS  Google Scholar 

  • Salam H, Turab SA, Ali A et al (2024) Kahi mélange complex in Kurram and Waziristan, NW Pakistan: an integrated approach for tectonic implications to India–Afghan suturing. Gondwana Res 126:79–95

    Article  Google Scholar 

  • Sarfraz Y, Basharat M, Riaz MT et al (2021) Evaluation of Physico-Mechanical properties of Crushed Rock aggregates: a Case Study from the Sub-himalaya, Pakistan. Acta Montan Slovaca 26(3). https://doi.org/10.46544/AMS.v26i3.07

  • Scheihing KW, Kübeck C, Sütering U (2022) GIS-AHP ensembles for Multi-actor Multi-criteria Site selection processes: application to Groundwater Management under Climate Change. Water 14:1793

    Article  CAS  Google Scholar 

  • Shah SYA, Bin D, O, et al (2022) Physio-Mechanical properties and Petrographic Analysis of NikanaiGhar Limestone KPK, Pakistan. Open J Civ Eng 12:169–188. https://doi.org/10.4236/ojce.2022.122011

    Article  CAS  Google Scholar 

  • Smith MR, Collis L (2001) Aggregates: sand, gravel and crushed rock aggregates for construction purposes. https://doi.org/10.1144/GSL.ENG.2001.017

  • Smith RG, Schoen EJ, Tenenbaum JM (2022) Early AI applications at Schlumberger. IEEE Ann Hist Comput 44:88–102

    Article  Google Scholar 

  • Sperandio HV, de Morais MS, de Jesus França LC et al (2025) Land suitability modeling integrating geospatial data and artificial intelligence. Agric Syst 223:104197

    Article  Google Scholar 

  • Standard B (2000) Method for determination of particle shape; flakiness index. British standard 812-105.1. Br Stand Institution, London, UK

    Google Scholar 

  • Standard A (2011) ASTM D4791: standard test method for flat particles, elongated particles, or flat and elongated particles in Coarse Aggregate. Am Soc Int Assoc Test Mater. https://www.astm.org/catalogsearch/result/?q=ASTM+D4791

  • Tassi A, Gigante D, Modica G et al (2021) Pixel-vs. object-based landsat 8 data classification in Google earth engine using random forest: the case study of maiella national park. Remote Sens 13:2299

    Article  Google Scholar 

  • Telloli C, Aprile A, Marrocchino E (2021) Petrographic and physical-mechanical investigation of natural aggregates for concrete mixtures. Mater (Basel) 14:5763

    Article  CAS  Google Scholar 

  • Terzer S, Wassenaar LI, Araguás-Araguás LJ, Aggarwal PK (2013) Global isoscapes for δ 18 O and δ 2 H in precipitation: improved prediction using regionalized climatic regression models. Hydrol Earth Syst Sci 17:4713–4728

    Article  Google Scholar 

  • Thakur JK, Singh SK, Ramanathan AL et al (2012) Geospatial techniques for managing environmental resources. Springer Science & Business Media. https://books.google.com.jm/books?id=PIF2dUwsBp0C&printsec

  • Ugur I, Demirdag S, Yavuz H (2010) Effect of rock properties on the Los Angeles abrasion and impact test characteristics of the aggregates. Mater Charact 61:90–96. https://doi.org/10.1016/j.matchar.2009.10.014

    Article  CAS  Google Scholar 

  • Ullah R, Ullah S, Rehman N et al (2020) Aggregate suitability of the late Permian Wargal Limestone at Kafar Kot Chashma Area, Khisor Range, Pakistan. Int J Econ Environ Geol 11:89–94

    Google Scholar 

  • Velegrakis AF, Ballay A, Poulos SE et al (2010) European marine aggregates resources: origins, usage, prospecting and dredging techniques. J Coast Res, Special Issue No. 51. EUMARSAND: European Marine Sand and Gravel Resources (Summer 2010) 1–14. https://www.jstor.org/stable/40928815

  • Virtriana R, Ihsan KTN, Anggraini TS et al (2025) Development of location suitability prediction for health facilities using random forest machine learning in 2030 integrating remote sensing and GIS in West Java, Indonesia. Environ Adv 19:100604

    Article  Google Scholar 

  • Wilson MJ, Wilson L, Patey I, Shaw H (2014) The influence of individual clay minerals on formation damage of reservoir sandstones: a critical review with some new insights. Clay Min 49:147–164

    Article  CAS  Google Scholar 

  • Yadav G, Bokhari MU, Alzahrani SI et al (2025) Emotion-aware ensemble learning (EAEL): revolutionizing Mental Health diagnosis of corporate professionals via Intelligent Integration of Multi-modal Data sources and ensemble techniques. IEEE Access. https://doi.org/10.1109/ACCESS.2025.3529032

  • Yang Y, Xing Y, Fang K et al (2023) Influence of dynamic load and water on energy accumulation and dissipation in sandstone. Sci Rep 13:22010

    Article  CAS  Google Scholar 

  • Yellishetty M, Karpe V, Reddy EH et al (2008) Reuse of iron ore mineral wastes in civil engineering constructions: a case study. Resour Conserv Recycl 52:1283–1289

    Article  Google Scholar 

  • Yılmaz NG, Goktan RM, Kibici Y (2011) Relations between some quantitative petrographic characteristics and mechanical strength properties of granitic building stones. Int J Rock Mech Min Sci 48:506–513

    Article  Google Scholar 

  • Zada W, Hussain J, Anwar M et al (2023) Physico-mechanical and petrographic insights of Lockhart Limestone, sections of Islamabad, Pakistan. Geotech Res 1–10

  • Zarif IH, Tuğrul A (2003) Aggregate properties of devonian limestones for use in concrete in Istanbul, Turkey. Bull Eng Geol Environ 62:379–388

    Article  Google Scholar 

  • Zhang Z, Zeng R, Meng X et al (2023) Effects of changes in soil properties caused by progressive infiltration of rainwater on rainfall-induced landslides. CATENA 233:107475

    Article  CAS  Google Scholar 

  • Zhou L, Pan S, Wang J, Vasilakos AV (2017) Machine learning on big data: opportunities and challenges. Neurocomputing 237:350–361

    Article  Google Scholar 

  • Zhu H, Ju Y, Yang M et al (2022) Grain-scale petrographic evidence for distinguishing detrital and authigenic quartz in shale: how much of a role do they play for reservoir property and mechanical characteristic? Energy 239:122176

    Article  CAS  Google Scholar 

Download references

Acknowledgements

The authors sincerely thank three reviewers who have provided comments and suggestions that largely improved the manuscript. We also extend our appreciation to the Institute of Rock and Soil Mechanics, UCAS, Wuhan for providing facilities for lab experiments. Javid Hussain is an awardee for the ANSO Scholarship 2023-PhD. Nafees Ali is an awardee for the ANSO Scholarship 2021-PhD.

Funding

This study was conducted without external funding. All resources and materials used in this research were provided by the authors or their affiliated institutions.

Author information

Authors and Affiliations

  1. State Key Laboratory of Geomechanics and Geotechnical Engineering, Institute of Rock and Soil Mechanics, Chinese Academy of Sciences, Wuhan, 430071, China

    Javid Hussain, Nafees Ali, Xiaodong Fu, Jian Chen, Sayed Muhammad Iqbal & Altaf Hussain

  2. University of Chinese Academy of Sciences, Beijing, 100049, China

    Javid Hussain, Nafees Ali, Xiaodong Fu, Jian Chen, Sayed Muhammad Iqbal & Altaf Hussain

  3. China-Pakistan Joint Research Center on Earth Sciences, 45320, Islamabad, Pakistan

    Javid Hussain, Nafees Ali, Xiaodong Fu & Jian Chen

  4. Hubei Key Laboratory of Geo-Environmental Engineering, 430071, Wuhan, China

    Javid Hussain, Nafees Ali, Xiaodong Fu & Jian Chen

  5. Department of Geology, Khushal Khan Khattak University Karak, 27200, Karak, Khyber Pakhtunkhwa, Pakistan

    Hikmat Salam

Authors
  1. Javid Hussain
    View author publications

    You can also search for this author inPubMed Google Scholar

  2. Nafees Ali
    View author publications

    You can also search for this author inPubMed Google Scholar

  3. Xiaodong Fu
    View author publications

    You can also search for this author inPubMed Google Scholar

  4. Jian Chen
    View author publications

    You can also search for this author inPubMed Google Scholar

  5. Sayed Muhammad Iqbal
    View author publications

    You can also search for this author inPubMed Google Scholar

  6. Altaf Hussain
    View author publications

    You can also search for this author inPubMed Google Scholar

  7. Hikmat Salam
    View author publications

    You can also search for this author inPubMed Google Scholar

Contributions

JH: Conceptualization, Data curation, Formal Analysis, Methodology, Investigation, Software, writing–original draft, Writing–review and editing. CJ: Investigation, Project administration, Resources, Supervision, Validation, Writing–review, and editing. XDF: Data Curation, Formal Analysis, Supervision, Validation, Writing–review and editing. SMI: Data Curation, Formal Analysis, Investigation, Methodology, Software, Writing–review and editing. NA: Conceptualization, Data curation, Methodology, Investigation, Software, Supervision, Validation, Project Administration, Visualization, Writing–review and editing. AH: Data Curation, Formal Analysis, Investigation, Resources, Writing–review and editing, HI: Data Curation, Formal Analysis, Investigation, Resources, Writing–review and editing.

Corresponding author

Correspondence to Jian Chen.

Ethics declarations

Competing interests

The authors declare no competing interests.

Additional information

Communicated by Hassan Babaie.

Publisher’s note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Hussain, J., Ali, N., Fu, X. et al. Geospatial mapping of potential aggregate resources using integrated GIS-AHP, geotechnical, petrographic and machine learning approaches. Earth Sci Inform 18, 336 (2025). https://doi.org/10.1007/s12145-025-01794-0

Download citation

  • Received:

  • Accepted:

  • Published:

  • DOI: https://doi.org/10.1007/s12145-025-01794-0

Keywords