Over the past two decades, the twisting skyscraper has emerged as a prominent typology in contemporary urbanism, driven by real-estate markets capitalizing on iconic silhouettes and panoramic view premiums. However, the engineering realization of helical high-rises is governed by a fundamental multidisciplinary trade-off: the geometric manipulation required to mitigate aerodynamic excitation simultaneously degrades internal structural stiffness, generates second-order gravity torsion, and introduces severe constructability penalties. This paper presents a systematic review of the architectural, aerodynamic, structural, and construction engineering trade-offs inherent in twisting tall buildings. Drawing upon peer-reviewed parametric studies (CFD and FEA) and landmark built case studies—including the Shanghai Tower, Cayan Tower, and Grove at Grand Bay—this study demonstrates that while helical forms can reduce characteristic across-wind forces by up to 24 P-Δ torsional amplification, and a 5
Construction-stage carbon governance requires methods that can operate with ordinary procurement records while exposing evidence limitations. This study presents an auditable bill-of-quantities (BOQ)-driven workflow for life-cycle modules A1-A5. The method combines governed text normalization, numerical confidence scores cross-walked to C0-C3 review classes, independent product-factor and unit-compatibility checks, canonical quantity and mass fields, correction logs, coverage disclosure, and an input-derived synthetic A5 demand-supply model. A retained portfolio of 256 BOQ rows across five anonymized school-type projects is examined under a declared generic factor basis. The final product-compatibility audit separates 68 primary factor-compatible A1-A4 rows (26.6
Banana fiber reinforced concrete has emerged as a sustainable construction material owing to its renewable nature and improved crack resistance. However, experimental evaluation of its mechanical properties is labor-intensive, time-consuming, and costly. This study develops an Artificial Neural Network model to predict the compressive and split tensile strengths of Banana fiber reinforced concrete using experimental data. The experimental program incorporated untreated and alkali-treated banana fibres with varying fiber volume fractions, while fly ash was used as a partial cement replacement and Super AURMAX 200 as a chemical admixture. A total of 109 experimental datasets were compiled, of which 76 datasets were used for training and 33 datasets for testing. The Artificial Neural Network model employed a multilayer feed-forward architecture trained using the back propagation algorithm. Model performance was evaluated using RMSE, MAE, MAPE, and regression analysis. Experimental results indicated that 0.5
This paper presents a new, low-carbon prefabrication process for load-bearing structural walls. It reuses large flat concrete and stone rubble sourced from demolition waste, is readily scalable, and operates under controlled factory conditions, combining the structural logic of masonry with industrialized concrete prefabrication methods. The process begins by sourcing the mineral rubble pieces from demolition sites or recycling centers. Each piece is digitally inventoried by processing pictures taken on the worksite. The pieces are then virtually selected and arranged to determine their best assembly geometry, maximizing load-bearing capacity while minimizing void volume. Next, the pieces are placed horizontally on a tilting table according to the generated layout. A low-impact recycled concrete is poured into the voids. Once the material has hardened, the wall is tilted and lifted in its vertical orientation. Four full-scale walls have been built to assess technical feasibility, environmental performance, and structural behavior, and to provide relevant insights into productivity and potential process improvements. Preliminary compression testing demonstrates sufficient resistance for structural walls in low- to mid-rise buildings under conventional loads. Prototypical fabrication highlighted lower fabrication speed and higher labor demand than for traditional precast concrete walls, while optimistic estimates of batch processing suggest similar fabrication rates. A Life Cycle Assessment shows reductions of up to 83
Architectural design projects register decisions about site, access, movement, density, open space, and contextual integration, yet this reasoning is distributed across heterogeneous drawings, models, plans, diagrams, and critiques. This article introduces Architectural Pattern Analytics (APA) as a framework for transforming design-studio projects into structured spatial evidence through architectural abstraction, spatial encoding, visual aggregation, interpretive synthesis, and architectural interpretation. APA is demonstrated in a one-semester studio at the Faculty of Architecture of the University of Lisbon, involving 22 s-year students who developed proposals for a padel school on the Cascais waterfront, Portugal. Building mass, exterior space, circulation, and accessibility were source-encoded from anonymized submissions; density and location were derived from those encodings and the registered site base. The quantitative demonstration relies principally on building-mass and circulation data, while exterior-space and accessibility evidence supports supplementary readings. CAD-supported registration, spreadsheet-based encoding, heatmap visualization, and comparative spatial reading identified a primary building nucleus, recurrent circulation spine, secondary activity zones, minority siting deviations, and underexplored lower and southern areas. These are cohort-level architectural interpretations grounded in evidence of different status, not equivalent outputs of six independent layers. The contribution is an auditable protocol that preserves architectural judgment. This case demonstrates feasibility in one studio; cross-context applicability requires independent testing.
