Enhancing Construction Quality Governance and Technical Compliance in Multi-Site Regional Development Projects in Saudi Arabia

References

[1] Trigunarsyah B, Moohialdin A, Islam MS. Quality management system framework for construction companies in the Kingdom of Saudi Arabia. International Journal of Productivity and Quality Management. 2023;39(3):302–329. https://doi.org/10.1504/IJPQM.2023.132262

[2] Alfalah G, Alasaibia A, Alshamrani O, Al-Sakkaf A. A holistic framework for assessing the quality of building construction in Saudi Arabia. Buildings. 2023;13(7):1666. https://doi.org/10.3390/buildings13071666

[3] Aljarallah N, Alsugair AM, Almohsen AS, Al-Gahtani KS. Significant factors affecting the quality of housing infrastructure project construction in Saudi Arabia using PLS-SEM. Sustainability. 2023;15(20):14998. https://doi.org/10.3390/su152014998

[4] Shaawat ME, Alqahtani SMS, Qasem A, Jamil R, Almohassen AS, Bongwirnso UM. A performance quality index to assess professional conduct of contractors at sustainable construction projects in Saudi Arabia. Sustainability. 2023;15(9):7500. https://doi.org/10.3390/su15097500

[5] Abdallah AA, Shaawat ME, Almohassen AS. Causes of miscommunication leading to project delays and low work quality in the construction industry of Saudi Arabia. Ain Shams Engineering Journal. 2024;15(3):102447. https://doi.org/10.1016/j.asej.2023.102447

[6] Alshihri S, Al-Gahtani K, Almohsen A. Risk factors that lead to time and cost overruns of building projects in Saudi Arabia. Buildings. 2022;12(7):902. https://doi.org/10.3390/buildings12070902

[7] Choudhry RM, Zafar I, Almatawa MS. Proactive approach to measure safety management on building projects in Saudi Arabia. Advances in Civil Engineering. 2023;2023:6306157. https://doi.org/10.1155/2023/6306157

[8] Okonta ED, Okeke FO, Ojelabi ET, et al. Exploring the role of quality management practices (QMS) in mitigating construction failures and building collapse. Discover Civil Engineering. 2024;1:111. https://doi.org/10.1007/s44290-024-00115-6

[9] Lambers R, Lamari F, Skitmore M, Rajendra D. Key residential construction defects: a framework for their identification and correlated causes. Construction Innovation. 2024;24(6):1425–1459. https://doi.org/10.1108/CI-05-2022-0103

[10] Love PED, Matthews J, Sing MCP, Porter SR, Fang W. State of science: Why does rework occur in construction? What are its consequences? And what can be done to mitigate its occurrence?. Engineering. 2022;18:246–258. https://doi.org/10.1016/j.eng.2022.05.010

[11] Love PED, Matthews J, Walker DHT, Ika LA. Building absorptive capacity in a mega-project program alliance: Learning to mitigate rework. Developments in the Built Environment. 2024;18:100402. https://doi.org/10.1016/j.dibe.2024.100402

[12] Luo H, Lin L, Chen K, Antwi-Afari MF, Chen L. Digital technology for quality management in construction: A review and future research directions. Developments in the Built Environment. 2022;12:100087. https://doi.org/10.1016/j.dibe.2022.100087

[13] Biswas HK, Sim TY, Lau SL. Impact of building information modelling and advanced technologies in the AEC industry: A contemporary review and future directions. Journal of Building Engineering. 2024;82:108165. https://doi.org/10.1016/j.jobe.2023.108165

[14] Cassandro J, Mirarchi C, Gholamzadehmir M, Pavan A. Advancements and prospects in building information modeling (BIM) for construction: a review. Engineering, Construction and Architectural Management. 2025;32(9):6006–6026. https://doi.org/10.1108/ECAM-04-2024-0435

[15] Zheng Z, Zhou YC, Lu XZ, Lin JR. Knowledge-informed semantic alignment and rule interpretation for automated compliance checking. Automation in Construction. 2022;142:104524. https://doi.org/10.1016/j.autcon.2022.104524

[16] Huo H, Ji Y, Xie K, Lu Y. Design and application of intelligent construction quality inspection system based on BIM-RFID. Advances in Civil Engineering. 2023;2023:5638265. https://doi.org/10.1155/2023/5638265

[17] Zhang D, Li M, Tian D, Song L, Shen Y. Intelligent text recognition based on multi-feature channels network for construction quality control. Advanced Engineering Informatics. 2022;53:101669. https://doi.org/10.1016/j.aei.2022.101669

[18] Wang Y, Zhang Z, Wang Z, Wang C, Wu C. Interpretable machine learning-based text classification method for construction quality defect reports. Journal of Building Engineering. 2024;89:109330. https://doi.org/10.1016/j.jobe.2024.109330

[19] Wang D, Yin K, Wang H. Intelligent classification of construction quality problems based on unbalanced short text data mining. Ain Shams Engineering Journal. 2024;15(10):102983. https://doi.org/10.1016/j.asej.2024.102983

[20] Ma Z, Liu Y, Li J. Review on automated quality inspection of precast concrete components. Automation in Construction. 2023;150:104828. https://doi.org/10.1016/j.autcon.2023.104828

[21] Li CZ, Li S, Ya Y, Tam VWY. Digital inspection techniques of modular integrated construction. Heliyon. 2023;9(11):e21399. https://doi.org/10.1016/j.heliyon.2023.e21399

[22] Yuan X, Smith A, Moreu F, Sarlo R, Lippitt CD, et al. Automatic evaluation of rebar spacing and quality using LiDAR data: Field application for bridge structural assessment. Automation in Construction. 2023;146:104708. https://doi.org/10.1016/j.autcon.2022.104708

[23] Han T, Ma T, Fang Z, Zhang Y, Han C. A BIM-IoT and intelligent compaction integrated framework for advanced road compaction quality monitoring and management. Computers and Electrical Engineering. 2022;100:107981. https://doi.org/10.1016/j.compeleceng.2022.107981

[24] Gao B, Ma Z, Gu J, Han X, Xiang P, Lv X. Fusing multi-source quality statistical data for construction risk assessment and warning based on deep learning. Knowledge-Based Systems. 2024;284:111223. https://doi.org/10.1016/j.knosys.2023.111223

[25] Shi G, Liu Z, Lu D, Wang Z, Jiao Z, et al. Construction error control method of large-span spatial structures based on digital twin. Journal of Building Engineering. 2024;98:111311. https://doi.org/10.1016/j.jobe.2024.111311

[26] Fan JS, Xu MN, Li YC, Ding YF, Liu YF, et al. Mechanics-based digital twin model for structural construction using 3D scanning and FE model updating. Automation in Construction. 2025;173:106109. https://doi.org/10.1016/j.autcon.2025.106109

[27] Yan J, Zhang H, Ai Q, Xu Y, Yang J, et al. Digital twin-based bridge geometric quality inspection using knowledge mapping and data-driven method. Automation in Construction. 2025;178:106383. https://doi.org/10.1016/j.autcon.2025.106383

[28] Han JY, Hsu CR, Huang CJ. Automated progress monitoring of land development projects using unmanned aerial vehicles and machine learning. Automation in Construction. 2024;168:105827. https://doi.org/10.1016/j.autcon.2024.105827

[29] Zeng Z, Wei Y, Wei Z, Yao W, Wang C, et al. Deep learning enabled particle analysis for quality assurance of construction materials. Automation in Construction. 2022;140:104374. https://doi.org/10.1016/j.autcon.2022.104374

[30] Yu L, Huang MM, Jiang S, Wang C, Wu M. Unmanned aircraft path planning for construction safety inspections. Automation in Construction. 2023;154:105005. https://doi.org/10.1016/j.autcon.2023.105005

@article{1724648,
      author = {Omar Nayfeh},
      title = {Enhancing Construction Quality Governance and Technical Compliance in Multi-Site Regional Development Projects in Saudi Arabia},
      journal = {Iconic Research And Engineering Journals},
      year = {2026},
      volume = {10},
      number = {5},
      pages = {192-204},
      issn = {2456-8880},
      url = {https://www.irejournals.com/formatedpaper/1724648.pdf},
      abstract = {Multi-site regional development programmes in Saudi Arabia are routinely plagued by a quality issue in that the technical requirements may be formally documented, but the interpretation of those requirements, the inspection of the work and the closure of non-conformities can vary dramatically from one site to another, from one contractor to another and from one stage of delivery to another. This paper looks at how construction quality governance can be linked to traceable technical compliance without simply reducing the level of assurance to a matter of paperwork or automated inspection. It draws on a range of peer-reviewed research (2020-2025) in the areas of Saudi construction quality management, project rework, digital inspection, information management and infrastructure monitoring. The evidence is grouped under the themes of governance authority, requirement translation, verification integrity, learning from non-conformities and portfolio oversight. The core proposition is that consistent compliance will only be achieved if there is an accountable chain of evidence from an approved requirement to the relevant asset, to the inspection record, to the decision maker and to the verified corrective action. Digital tools can only enhance that chain if the rules around evidence, the role of professional judgement and the responsibilities for escalation are all in place upfront. A three tier governance model is proposed (portfolio standards, project assurance and work package control) and two implementation routes are identified to suit either a very technically sophisticated project or a smaller regional contractor. Performance indicators are also developed which are able to differentiate between the completeness of documentation and the actual demonstration of technical conformity. The paper provides a practical synthesis for owners, consultants, project managers and regulators who are responsible for the governance of geographically dispersed development portfolios. The model is at a conceptual stage and will require prospective validation across a number of project types in Saudi Arabia prior to any performance benefits being quantified.},
      keywords = {construction quality governance; technical compliance; multi-site programmes; inspection; regional development; Saudi Arabia},
      month = {November},
  }