Although the integration of building information Modeling (BIM), lean construction (LC), and sustainability is widely acknowledged, the principal domains through which these three concepts intersect have not yet been systematically synthesized. Prior research has tended to emphasize environmental aspects of sustainability, while the social and economic dimensions remain comparatively underexplored. In addition, many studies have focused on specific applications of BIM or LC. To address this gap, this study adopts a sequential exploratory mixed-methods design. In contrast to previous work, the review deliberately includes only studies addressing dual or triple integrations among BIM, LC, and sustainability. This results in a dataset of 121 peer-reviewed articles. A grounded theory (GT)-informed literature review approach is employed to allow integration domains to emerge inductively rather than being constrained by predefined categories. This is complemented by a small-scale Likert-scale survey of 10 experts, with responses analyzed using nonparametric statistical tests to provide indicative prioritization of the identified domains. Six integration domains emerge from the analysis: stakeholder management, waste and material management, resource management, cost management, project efficiency, and energy management. Among these, waste and material management and energy management appear most prominent across both the literature synthesis and expert survey insights. However, the limited sample size and the absence of statistical significance after Bonferroni correction highlight the exploratory nature of these findings. This study contributes a novel conceptual synthesis of BIM–LC–sustainability integration. It structures the findings into a comprehensive framework that illustrates how the emergent domains interact with and support the three pillars of sustainability. By identifying key thematic domains and proposing exploratory propositions, it establishes a foundation for future empirical validation. For practitioners and policymakers, the framework provides a structured reference for identifying opportunities where coordinated implementation can enhance project outcomes.
Hydrodynamic forces on bridge piers are fundamental parameters in pier design, providing a quantitative foundation for structural optimization, and navigational safety assurance. However, current research has focused primarily on the flow field structures around piers, whereas the hydrodynamic forces acting on piers have received insufficient attention. To bridge this gap, this study integrates flow field characteristics with the hydrodynamic behaviors of bridge piers through numerical simulation, and investigates the influence of pier configuration on hydrodynamic forces by combining quantitative analysis with qualitative interpretation of the surrounding flow field. Under the baseline conditions, the total drag coefficient of the double-cylinder pier is 0.9276, whereas that of the elongated circular pier is 0.4054, corresponding to a drag reduction of 56.3%; the influences of pier spacing and inflow velocity on the variation of hydrodynamic forces are also explored. The results indicate that the flow field provides a supportive basis for interpreting the hydrodynamic characteristics. Compared with the traditional double-cylinder configuration, the elongated circular pier generally exhibits superior hydrodynamic performance, and pier spacing and inflow velocity are identified as important factors affecting the hydrodynamic characteristics.
High bench dumping process makes the dump presents a trendy of high and steep slope, and rainfall is an important cause of inducing high and steep slope to slide. Especially under continuous rainfall conditions, the seepage law and stability evolution during the rainfall process of waste dumping sites with slope heights exceeding 200 m should be given more attention. In this study, taking a high and steep step soil dumping site in a rainy area as an example, GeoStudio finite element software was used to conduct saturated unsaturated transient seepage analysis and stability analysis for two working conditions: the current situation (highest step height of 257 m) and the design (optimized to four steps), with continuous rainfall for 10 days and a 10 day stop after rainfall. A comparative analysis was conducted on the seepage changes and stability evolution characteristics of high steep steps and multibench soil dumping sites. According to research: (1) rainfall mainly affected the matric suction of the soil within a range of 30 m below the slope surface and formed a transient saturation zone, with minimal impact on the deeper discharged soil. The higher the height of the dump, the smaller the influence of rainfall on the distribution of pore water pressure. (2) For multistep slopes, rainfall convergence from high to low steps caused significant differences in seepage patterns among steps; the first step was least affected, while the fourth step was most affected by rainfall. (3) The stability of the slope of the waste dump is controlled by different modes of deep and shallow sliding during different periods of rainfall; The decrease rate of safety factor during rainfall is greater than the recovery rate after the rainfall stops. (4) The safety factor of multilevel optimized slopes is much higher than that of high and steep slopes. After 10 days of rainfall, the slope remains stable with optimized design, while the high and steep slope reaches a critical unstable state. Therefore, in rainy areas, high and steep slopes should be avoided, and it is strongly recommended to optimize the height and slope of the steps.
Accurate estimation of maximum dry density (MDD) and optimum moisture content (OMC) is important for geotechnical design; however, laboratory compaction testing is often time- and resource-intensive. This study presents a metaheuristic-enhanced machine learning (ML) framework coupling the giant Pacific octopus optimizer (GPO) with artificial neural networks (ANNs), Lasso regression, and random forest (RF) models, using five routinely measured index properties (gravel content [G], sand content [S], fines content, liquid limit [LL], and plastic limit [PL]) as inputs. A dataset of 182 soil samples with diverse gradation and plasticity characteristics was used to develop and evaluate the proposed models using repeated 5-fold cross-validation, with an independent test set employed for additional verification. Among the developed models, GPO-ANN achieved the highest repeated cross-validation accuracy for MDD, with a mean R2 of 0.8500 ± 0.0424, while for OMC it attained a mean R2 of 0.8383 ± 0.0460, closely comparable to that of GPO-Lasso (0.8413 ± 0.0426). These results indicate that GPO-ANN provided the most consistent predictive performance among the three investigated GPO-based models across different data partitions. Cosine amplitude method (CAM)- and SHapley Additive exPlanation (SHAP)-based analyses assigned the highest importance to S and Gs for MDD prediction, whereas both gradation- and plasticity-related variables contributed to OMC prediction. These patterns were broadly consistent with expected geotechnical behavior. The proposed framework provides an interpretable approach for the preliminary estimation of MDD and OMC from routinely measured soil index properties. It may support early-stage geotechnical assessment and testing prioritization, while laboratory compaction testing remains necessary for project-specific design and verification.
Mountain highway cut slopes subjected to excavation disturbance and rainfall infiltration often undergo progressive deformation rather than instantaneous collapse. Although previous studies have examined excavation unloading, rainfall infiltration, weak structural control, and drainage anomalies, the time–dependent interaction among these processes has not been sufficiently integrated. This review synthesizes case-based evidence on the progressive failure of highway cut slopes under excavation–rainfall coupling. Publications from 1989 to 2026 were retrieved using Scopus and Web of Science Core Collection as the core databases, with Taylor & Francis Online used as an auxiliary publisher-specific source and Google Scholar used for supplementary checking. After systematic screening, 95 case items representing 94 independently counted engineering cases were retained. Five non-mutually exclusive process features were coded: staged evolution, local initiation and spatial expansion, weak-plane or structural control, repeated sliding or reactivation, and crack-controlled infiltration or delayed hydrological response. The results show that hydrological pathways or delayed responses, staged deformation, and structural control are recurrent features in the screened cases. The synthesized evidence indicates that excavation–rainfall coupled failure is better understood as a progressive sequence involving excavation-induced sensitization, rainfall-driven amplification, local initiation, deformation accumulation, and slope-wide expansion, rather than as the simple superposition of isolated triggers. Field monitoring, laboratory testing, physical modeling, and numerical analysis provide complementary evidence for identifying stage transitions and interpreting failure mechanisms. Based on these findings, a process-oriented framework is proposed for risk identification and early warning by linking observable slope responses with hydrological and geological conditions.
