Application of a fuzzy logic model for quality indexing in reinforced cement concrete construction
Gaurav Singh1, Laxmi Kant Mishra1 and Virendra Kumar Paul2
Department of Civil Engineering,Motilal Nehru National Institute of Technology Allahabad, Prayagraj-211004, Uttar Pradesh,India1
Building Engineering and Management,School of Planning and Architecture New Delhi, Delhi-110002,India2
Building Engineering and Management,School of Planning and Architecture New Delhi, Delhi-110002,India2
Corresponding Author : Gaurav Singh
Recieved : 21-June-2024; Revised : 08-May-2025; Accepted : 12-May-2025
Abstract
It is essential to quantify construction quality rather than rely solely on subjective evaluations to effectively control and assess the standard of any construction work. This study employs a fuzzy mathematical model as an effective methodology to reduce subjectivity and establish a uniform quantification scale for assessing the overall quality index of reinforced cement concrete (RCC) construction. The model evaluates various quality attributes related to materials, construction processes, and the hardened state of concrete. A case study was conducted to validate the proposed model by assessing the quality indices of three RCC buildings constructed by different agencies. The findings revealed that Building 1 achieved the highest overall quality index of 76.847 (on a scale of 100), indicating superior construction quality. Building 2 followed with a score of 73.883, while Building 3 had the lowest quality index of 72.221. Based on the numerical ratings, Building 1 was classified as "excellent," whereas Buildings 2 and 3 were rated as "good." This framework replaces subjective assessments with objective numerical values, enabling consistent quality evaluation. It offers construction companies a competitive advantage by enhancing construction quality, aiding in facility ranking, project prioritization, and serving as a critical criterion in contractor selection.
Keywords
Fuzzy logic, Quality indexing, Reinforced cement concrete (RCC), Construction quality assessment, Quality attributes, Subjective evaluation elimination.
Cite this article
Gaurav, Mishra LK, Paul VK. Application of a fuzzy logic model for quality indexing in reinforced cement concrete construction. International Journal of Advanced Technology and Engineering Exploration. 2025;12(126):688-707. DOI : 10.19101/IJATEE.2024.111101076
References
[1]
Sam VS, Anand N, Lublóy É, Andrushia D. Investigation on key influencing factors affecting temperature distribution in concrete. Periodica Polytechnica Civil Engineering. 2025; 69(1):220-30.
[2]
Kacker R, Singh SK, Kasar AA. Understanding and addressing multi-faceted failures in building structures. Journal of Failure Analysis and Prevention. 2024; 24(4):1542-58.
[3]
Trustgod J. Size effect of coarse aggregate within concrete matrix on the mechanical properties of concrete. NIPES-Journal of Science and Technology Research. 2024; 6(3):243-52.
[4]
Al KH, Sweis GJ, Sammour F, Maaitah WO, Sweis RJ, Alkailani M. Predicting construction cost index using fuzzy logic and machine learning in Jordan. Construction Innovation. 2024.
[5]
Alavi SA, Noel M, Moradi F, Layssi H. Development of a machine learning model for on-site evaluation of concrete compressive strength by SonReb. Journal of Building Engineering. 2024; 82:108328.
[6]
Ogunleye E. Innovations and applications of laterized concrete in sustainable construction. Global Journal of Engineering and Technology Advances. 2023; 16(3):107-20.
[7]
Chacón R, Posada H, Ramonell C, Jungmann M, Hartmann T, Khan R, et al. Digital twinning of building construction processes. case study: a reinforced concrete cast-in structure. Journal of Building Engineering. 2024; 84:1-23.
[8]
Chowdhury MU, Mahamud T, Sarker MS, Al MMA. Towards resilient infrastructure: assessing steel–concrete composite and RCC structures in Bangladesh. Discover Civil Engineering. 2024; 1(1):1-20.
[9]
Nilimaa J. Smart materials and technologies for sustainable concrete construction. Developments in the Built Environment. 2023; 15:1-19.
[10]
Arora G, Kumar D, Singh B. Tree based regression models for predicting the compressive strength of concrete at high temperature. In conference series: earth and environmental science 2024 (pp. 1-11). IOP Publishing.
[11]
Ivanov ML, Chow WK. Structural damage observed in reinforced concrete buildings in adiyaman during the Turkiye kahramanmaras earthquakes. Structures. 2023; 58:105578. Elsevier.
[12]
Asadi R, Wilkinson S, Rotimi JO. The common causes of rework in construction contracts: a diagnostic approach. Journal of Engineering, Design and Technology. 2023; 21(4):1107-33.
[13]
Singh A, Kalita PC, Singh G. Construction quality analysis and index. In AIP conference proceedings 2023. AIP Publishing.
[14]
Ayalew GG, Ayalew GM, Meharie MG. Integrating exploratory factor analysis and fuzzy AHP models for assessing the factors affecting the performance of building construction projects: the case of Ethiopia. Cogent Engineering. 2023; 10(1):1-38.
[15]
Golewski GL. The phenomenon of cracking in cement concretes and reinforced concrete structures: the mechanism of cracks formation, causes of their initiation, types and places of occurrence, and methods of detection-a review. Buildings. 2023; 13(3):1-34.
[16]
Liao W, Lu X, Fei Y, Gu Y, Huang Y. Generative AI design for building structures. Automation in Construction. 2024; 157:105187.
[17]
Ali M, Shams MA, Bheel N, Almaliki AH, Mahmoud AS, Dodo YA, et al. A review on chloride induced corrosion in reinforced concrete structures: lab and in situ investigation. RSC Advances. 2024; 14(50):37252-71.
[18]
Singh G, Mishra LK, Paul VK. Evaluation of quality defining attributes of reinforced cement concrete constructions using analytic hierarchy process. International Journal of Advanced Technology and Engineering Exploration. 2024; 11(114):773-94.
[19]
Zeng J, An M, Smith NJ. Application of a fuzzy based decision making methodology to construction project risk assessment. International Journal of Project Management. 2007; 25(6):589-600.
[20]
Faghfouri A, Germain D, Fortin G. Design optimization of concrete gravity dam subjected to near-field earthquake based on novel lean-bubble sort approach. KSCE Journal of Civil Engineering. 2024; 28(10):4293-308.
[21]
Ghorbani A, Maleki H, Naderpour H, Khatami SM. Advancing compressive strength prediction in self-compacting concrete via soft computing: a robust modeling approach. Journal of Soft Computing in Civil Engineering. 2024; 8(1):126-40.
[22]
Ling S, Chengbin D, Yafeng Y, Yongheng L. Analysis and prediction of compressive and split-tensile strength of secondary steel fiber reinforced concrete based on RBF fuzzy neural network model. Plos One. 2024; 19(2):1-31.
[23]
Raza A, Elhadi KM, Abid M, Deifalla AF, Jameel MS, Alashker Y. Structural performance of FRP composite bars reinforced rubberized concrete compressive members: tests and numerical modeling. Heliyon. 2024; 10(4):1-26.
[24]
Mandal S, Shiuly A, Sau D, Mondal AK, Sarkar K. Study on the use of different machine learning techniques for prediction of concrete properties from their mixture proportions with their deterministic and robust optimisation. AI in Civil Engineering. 2024; 3(1):1-24.
[25]
Wang Y, Iqtidar A, Amin MN, Nazar S, Hassan AM, Ali M. Predictive modelling of compressive strength of fly ash and ground granulated blast furnace slag based geopolymer concrete using machine learning techniques. Case Studies in Construction Materials. 2024; 20:1-18.
[26]
Rane NL, Anand A, Deepak K. Evaluating the selection criteria of formwork system (FS) for RCC building construction. International Journal of Engineering Trends and Technology. 2023; 71(3):197-205.
[27]
Rubin AP, Quintanilha LC, Repette WL. Influence of structuration rate, with hydration accelerating admixture, on the physical and mechanical properties of concrete for 3D printing. Construction and Building Materials. 2023; 363:129826.
[28]
Jiao CJ, Zhang XC, Chen WZ, Chen XF. Mechanical property and dimensional stability of chopped basalt fiber-reinforced recycled concrete and modeling with fuzzy inference system. Buildings. 2022; 13(1):1-12.
[29]
Ali A, Riaz RD, Malik UJ, Abbas SB, Usman M, Shah MU, et al. Machine learning-based predictive model for tensile and flexural strength of 3D-printed concrete. Materials. 2023; 16(11):1-32.
[30]
Kulasooriya WK, Ranasinghe RS, Perera US, Thisovithan P, Ekanayake IU, Meddage DP. Modeling strength characteristics of basalt fiber reinforced concrete using multiple explainable machine learning with a graphical user interface. Scientific Reports. 2023; 13(1):1-15.
[31]
Abbas YM, Khan MI. Prediction of compressive stress–strain behavior of hybrid steel–polyvinyl-alcohol fiber reinforced concrete response by fuzzy-logic approach. Construction and Building Materials. 2023; 379:131212.
[32]
Agor CD, Mbadike EM, Alaneme GU. Evaluation of sisal fiber and aluminum waste concrete blend for sustainable construction using adaptive neuro-fuzzy inference system. Scientific Reports. 2023; 13(1):1-22.
[33]
Gutiérrez-garcía FJ, Alayón-miranda S, González-díaz E. Fuzzy model for predicting the strength of mortars made with pozzalani cement and volcanic sand from electrical resistivity. Journal of Building Engineering. 2023; 79:1-13.
[34]
Han S, Zheng D, Mehdizadeh B, Nasr EA, Khandaker MU, Salman M, et al. Sustainable design of self-consolidating green concrete with partial replacements for cement through neural-network and fuzzy technique. Sustainability. 2023; 15(6):1-27.
[35]
Liu L, Li J, Mohammed KJ, Ali E, Alkhalifah T, Alturise F, et al. Novel modified ANFIS based fuzzy logic model for performance prediction of FRCM-to-concrete bond strength. Advances in Engineering Software. 2023; 182:103474.
[36]
Zadeh LA. Fuzzy sets. Information and Control. 1965; 8(3):338-53.
[37]
Kayak O, Zadeh LA. Fuzzy inference systems: a critical review. Computational intelligence soft computing and fuzzy-neurointegration with applications 1998 (pp.177–97). Springer.
[38]
Garg C, Namdeo A, Singhal A, Singh P, Shaw RN, Ghosh A. Adaptive fuzzy logic models for the prediction of compressive strength of sustainable concrete. In advanced computing and intelligent technologies: proceedings of ICACIT 2021 (pp. 593-605). Springer Singapore.
[39]
Mitaim S, Kosko B. What is the best shape for a fuzzy set in function approximation? In proceedings of 5th international fuzzy systems 1996 (pp. 1237-43). IEEE.
[40]
Nadeem A, Rizvi AA, Noor MY. Applying a higher number of output membership functions to enhance the precision of a fuzzy system. IEEE Transactions on Emerging Topics in Computational Intelligence. 2024; 9(1):394-405.
[41]
Sadeghpour A, Ozay G. Investigating the predictive capabilities of ANN, RSM, and ANFIS in assessing the collapse potential of RC structures. Arabian Journal for Science and Engineering. 2024:1-22.
[42]
Qing ZQ, Na ZL. A comparative analysis of RCC and composite buildings using the new plastic deformation (PD) method. Scientific Reports. 2024; 14(1):1-19.
[43]
Wang T, Gault R, Greer D. Ensemble learning-based fuzzy aggregation functions and their application in TSK neural networks. International Journal of Fuzzy Systems. 2024:1-12.
[44]
Parsamehr M, Ruparathna R. A BIM-based two-stage fuzzy inference system for safety risk prediction in building construction projects. Canadian Journal of Civil Engineering. 2022; 50(1):11-23.
[45]
Reyes-garcía CA, Torres-garcía AA. Fuzzy logic and fuzzy systems. In biosignal processing and classification using computational learning and intelligence 2022 (pp. 153-76). Academic Press.
[46]
Tesfamariam S, Saatcioglu M. Seismic vulnerability assessment of reinforced concrete buildings using hierarchical fuzzy rule base modeling. Earthquake Spectra. 2010; 26(1):235-56.
[47]
Li F, Shen Q. Introduction to fuzzy sets, fuzzy logic, and fuzzy inference systems. In fuzzy rule-based inference: advances and applications in reasoning with approximate knowledge interpolation 2024 (pp. 1-15). Singapore: Springer Nature Singapore.
[48]
Mohammadi V, Mehdizadeh E, Hejazi SM. Ranking the generalized fuzzy numbers based on the center of the area. International Journal of Decision Intelligence. 2024; 3(3):35-47.
[49]
Sajid S. A methodology to build interpretable machine learning models in organizations. Master's Thesis, University of Twente. 2023.
[50]
Li Z, Chau CK, Zhou X. Accelerated assessment and fuzzy evaluation of concrete durability. Journal of Materials in Civil Engineering. 2005; 17(3):257-63.
[51]
Makode M, Raut SP, Mase DP. Analysis condition assessment and strengthening of 32-year-old RCC building. In AIP conference proceedings 2024. AIP Publishing.
[52]
Ross TJ. Fuzzy logic with engineering applications. John Wiley & Sons; 2005.
[53]
Bobyr MV, Milostnaya NA, Kulabuhov SA. A method of defuzzification based on the approach of areas' ratio. Applied Soft Computing. 2017; 59:19-32.
[54]
Nasrullah, Mustamin MT. The effect of implementing building structure and construction systems on energy efficiency. Journal of Science Technology (JoSTec). 2024; 6(1)47-52.
[55]
Chattamvelli R. Correlation in engineering and the applied sciences: applications in R. Springer Nature; 2024.