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T. K. Stevik

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Review Open access Nov 2026

Implementing Collaboration in Construction Projects: A Dual-Layered Framework for Practice

Collaboration is widely acknowledged as essential for project success; however, its structured implementation across project phases remains poorly understood. Existing research emphasizes principles such as trust and governance but offers limited guidance on translating them into operational practices. This study aims to develop an empirically grounded framework for structuring collaboration across project phases. To achieve this, a dual-layered framework for collaborative project delivery is presented. The first layer organizes empirically derived operational mechanisms into six strategic categories: external requirements, project management methodology, implementation strategy, collaboration methodology, concretization of quality, and health, safety, and environment. The second layer links these categories to three theoretical dimensions—strategic alignment, governance mechanisms, and managerial practices—thereby explicitly linking practical mechanisms to their conceptual foundations. Data were collected through case studies of four large-scale Norwegian public construction projects, using semistructured interviews, document analysis, and a follow-up survey to support triangulation and cross-check the categorization of collaborative mechanisms. The findings reveal how collaboration is operationalized through phase-specific mechanisms, where early-stage alignment and governance arrangements shape subsequent coordination practices and managerial activities during project execution. The study contributes by providing a structured explanation of how collaboration is operationalized across project phases and by linking practical mechanisms to established theoretical dimensions, showing how collaborative mechanisms collectively function and vary across project stages. The resulting framework offers a structured basis for understanding and organizing collaboration across project phases and may inform how collaborative mechanisms are considered and applied across project stages.

A. Shiferaw, Mathew Azarian, T. K. Stevik · 0 citations
#machine learning Preprint Aug 2026

Continual Test-Time Adaptation via Entropy Sensitivity-Guidance in Strict Online Setting

Test-time adaptation (TTA) promises robustness under distribution shift by updating a pretrained model on unlabeled test data, but strict online TTA with batch size one and no access to source data is especially prone to drift or collapse. We introduce Sensitivity-Guided Erasing Adaptation (SEGA), a method for strict online continual TTA (CTTA) on corruption-style streams. SEGA uses a small number of structured erasures to probe how predictive entropy changes as information is removed, and uses the resulting per-sample sensitivity trajectories to coordinate recovery and sample selection rather than relying on raw entropy or batch statistics. This yields a practical feedback signal for long-horizon batch-size-one adaptation without periodic resets or model reservoirs. In experiments on ImageNet-C, CIFAR10/100-C, and corruption-generated aquaculture streams treated as controlled corruption-style proxies, SEGA yields consistent robustness and stability gains over strong CTTA baselines while reducing backward passes through sensitivity-based gating.

Chandler Timm C. Doloriel, Yun-Bei Zhang, M. S. Siddiqui et al. · 0 citations