Design changes have far-reaching impacts on civil infrastructure projects, resulting in cost overruns, schedule delays, and even contractual disputes. Current methods for analyzing change propagation rely heavily on subjective expert knowledge, incomplete change-order narratives, or predefined rules, limiting their effectiveness. Researchers Ryoko Arashida and Masahide Horita propose a novel, data-driven framework that models how modifications propagate across interdependent structural components.
Their study offers a scalable approach that extracts change information from geometric revision data, constructs a change indicator matrix, applies constraint-based causal discovery, and quantifies direct and indirect propagation probabilities. To validate their matrix, the duo reviewed 159 change order records from a large-scale expressway project and identified a strong hierarchical asymmetry – where change transmission is most prominent among primary structural components and rare from secondary to primary elements.
Their findings, outlined in “A Data-Driven Framework for Causal Modeling of Design Change Propagation in Civil Infrastructure,” reveal a wider set of structurally plausible propagation pathways than typically recognized in routine practice, which will allow practitioners to better anticipate the ripple effects of modifications. This framework can be used by engineering professionals not as a tool for deterministic forecasting, but to broaden the scope of impact assessment and prioritize review or coordination efforts at interfaces with high downstream vulnerabilities. Learn more about this research and how it can be applied to support proactive impact assessment and integrated change management in the Journal of Construction Engineering and Management at https://ascelibrary.org/doi/10.1061/JCEMD4.COENG-18722. The abstract is below.
Abstract
Design changes are a major source of cost overruns and delays in civil infrastructure projects, and yet the mechanisms by which such changes propagate across interdependent components remain insufficiently understood. Existing approaches to modeling component dependencies often depend on information sources that are inherently subjective, incomplete, or difficult to specify exhaustively in complex civil infrastructure projects. In addition, inferred dependency structures are seldom grounded in formal statistical inference, limiting confidence in their robustness and interpretability. This study develops a data-driven framework that infers change-propagation structures directly from geometric differences between initial and final design models. The project space is partitioned into spatial grids to construct a binary change indicator matrix (CIM). Intercategory dependencies are inferred using constraint-based causal discovery, and propagation probabilities are estimated via maximum likelihood under a Noisy-OR formulation. Direct and indirect effects are aggregated into a propagation probability matrix (PPM). The framework then is applied to a large-scale expressway construction project in Japan using thousands of design drawings and 159 change-order (CO) records. The effects of spatial grid size and geometric change-detection threshold are examined based on their ability to reproduce documented intercategory relations. Spatial discretization is found to play a dominant role in recovering these relations, whereas the influence of geometric tolerance is comparatively minor. Under the selected parameter setting, all documented intercategory relations are reproduced in the inferred network. The resulting PPM exhibits a structured and hierarchically asymmetric pattern: Propagation is strongest among primary structural components, substantial within secondary components, and consistently weak from secondary to primary elements. These findings demonstrate that propagation networks inferred through formal statistical procedures can capture empirically observed change relations, providing a scalable and data-grounded basis for analyzing change propagation in civil infrastructure systems.
Explore this way to determine the downstream effects of design changes to primary structural components in the ASCE Library: https://ascelibrary.org/doi/10.1061/JCEMD4.COENG-18722.
