Design of an Iterative Deep Context Embedding and RII-Fused Evidential Framework for Predicting Construction Delay Severity under Heterogeneous Project Environments

Design of an Iterative Deep Context Embedding and RII-Fused Evidential Framework for Predicting Construction Delay Severity under Heterogeneous Project Environments | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Design of an Iterative Deep Context Embedding and RII-Fused Evidential Framework for Predicting Construction Delay Severity under Heterogeneous Project Environments Ankit G. Chandak, Pritam Malakar, Ajay G. Dahake, Kunal Ramrao Ghadge This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8533740/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Construction project schedule overruns continue to cost money, generate contractual complications, and disrupt supply chains. Decades of research have identified and ranked delay factors using indices like the Relative Importance Index (RII), but analytical and machine-learning approaches are generally limited to binary or multiclass delay classification sets. They rarely quantify prediction uncertainty, disregard contextual interactions, and treat project context as flat variables. Current models cannot generalize across regions, procurement regimes, and contractor capacities, limiting their project decision-making value. To address these limits, this work provides an end-to-end, analytically validated Deep Context Embedding and RII-Fusion pipeline for delay severity rating prediction. HPCE uses a graph-transformer architecture to learn dense contextual embeddings from heterogeneous graphs of projects, contractors, locations, and procurement trends. Format retains structural dependencies that tabular encodings lose. RII-Prior Attention Fusion (RPAF) regularizes attention weights over delay-factor embeddings using probabilistic priors to combine expert knowledge with learned context embeddings. Domain expertise is integrated into learning dynamics instead of using RII as a post hoc rating. DEOS provides a complete ordinal severity distribution, anticipated severity score, and deconstructed epistemic and aleatoric uncertainty for predictive severity modeling. This evidence-based paradigm assesses expected delay severity risk-awarely beyond point estimates. To make the model robust in various construction contexts, Counterfactual Invariant Representation Regularization (CIRR) fixes the severity mechanism across regions and procurement types and quantifies factor-level sensitivity under controlled counterfactual perturbations Finally, Conformal Prediction with Drift Guard (CPDG) ensures deployment-level reliability with calibrated prediction intervals and embedding-space drift detection for changing project conditions. The framework provides uncertainty-aware, context-sensitive severity scoring that is rigorously confirmed. The results improve forecast accuracy, adaptability across environments, and interpretability over earlier techniques. Construction delay analytics improves scheduling, contractual risk management, and policy formulations with deployable, decision-grade severity forecast. Construction Delay Severity Deep Context Embeddings Relative Importance Index Fusion Evidential Ordinal Learning Robust Project Analytics Scenarios Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-8533740","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":570928240,"identity":"37bf5e79-c6c8-429b-ba41-6ce967ca3e1d","order_by":0,"name":"Ankit G. 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Decades of research have identified and ranked delay factors using indices like the Relative Importance Index (RII), but analytical and machine-learning approaches are generally limited to binary or multiclass delay classification sets. They rarely quantify prediction uncertainty, disregard contextual interactions, and treat project context as flat variables. Current models cannot generalize across regions, procurement regimes, and contractor capacities, limiting their project decision-making value. To address these limits, this work provides an end-to-end, analytically validated Deep Context Embedding and RII-Fusion pipeline for delay severity rating prediction. HPCE uses a graph-transformer architecture to learn dense contextual embeddings from heterogeneous graphs of projects, contractors, locations, and procurement trends. Format retains structural dependencies that tabular encodings lose. 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