Journal of Civil Engineering Beyond Limits (CEBEL)

Harsh Pachori

Software productization—the structured transformation of domain-specific internal tools into scalable, commercially viable solutions—has become a strategic imperative in architectural practice. Yet existing literature conflates productization with generic Building Information Modeling (BIM) adoption, leaving the product-development lifecycle, licensing architectures, and interoperability engineering underexplored. This systematic literature review synthesizes fifty-two peer-reviewed studies, industry surveys, and technical reports published between 2009 and 2025, retrieved from ScienceDirect, Web of Science, MDPI, and authoritative industry sources.The review applies the Technology Acceptance Model (TAM) and Diffusion of Innovations (DOI) frameworks to map the market adoption of BIM-based software across five domains: market foundation, technical implementation, user adoption, productivity results, and the barriers. Large architectural firms implement BIM technologies more successfully than SMEs owing to economies of scale, procurement mandate thresholds, greater IT infrastructure capacity, and the ability to absorb transitional productivity losses—factors that compound the capital and training differentials. Results measured quantitatively include such improvements as reduction in the number of coordination mistakes, faster project deliveries, and energy performance due to simulations provided by the application. Barriers include lack of semantic interoperability, security threats in the collaborative cloud-based systems, and organizational barrier. Furthermore, a structural gap has been identified in the existing literature regarding this issue. A comprehensive mapping of the complete productization lifecycle—from internal prototype through licensed commercial deployment—is absent from the synthesized literature on architectural practice. Policy and standardization implications are discussed, with recommendations for open-standard mandates and SME-targeted digitalization programs.

https://doi.org/10.36937/cebel.2027.11159

Sebghatullah Jueyendah Hedayatullah Hawadi Khwaja Abdul Wahab Sediqi

Recycled powder mortar (RPM) represents a sustainable cementitious material whose compressive strength (CS) is governed by complex nonlinear interactions among material and mixture parameters. This study proposes an explainable, metaheuristic-optimized ensemble machine learning (ML) framework for accurate prediction and interpretation of CS of RPM. A dataset comprising 204 observations was employed, with recycled powder type (kind), particle size, water/binder (W/B), and mass replacement ratio (MRR) considered as input variables. Extra Trees (ET), Gradient Boosting (GB), and Histogram-Based Gradient Boosting (HGB) were integrated within bagging, voting, and stacking architectures, while hyperparameters were optimized using genetic algorithm (GA), particle swarm optimization (PSO), differential evolution (DE), and grey wolf optimizer (GWO). The dataset was partitioned into 80% training and 20% testing subsets, with model robustness further evaluated through shuffled 10-fold cross-validation. Predictive performance was quantified using R², RMSE, MAE, and MAPE. The GA-optimized ET–GB–HGB stacking model demonstrated superior predictive performance, achieving R² values of 0.949, 0.966, and 0.947, with corresponding RMSE values of 2.149, 1.931, and 2.129 MPa for the training, testing, and 10-fold cross validation datasets, respectively. SHAP and permutation importance consistently established the hierarchy MRR > particle size > kind > W/B, with MRR accounting for more than half of global SHAP importance. Sobol sensitivity and SHAP interaction analyses further confirmed MRR as the dominant factor and particle size–MRR as the strongest coupled interaction. Response-surface analysis additionally elucidated their nonlinear effects on CS. Overall, the proposed framework establishes a robust, interpretable, and data-driven methodology for accurate CS prediction and performance-informed design of sustainable RPM mixtures.

https://doi.org/10.36937/cebel.2027.11173