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This study presents a series of physical model tests and numerical simulations using PFC2D (both with a dip slip angle=60° and

a soil bed thickness of 0.2 m in model scale)at the acceleration conditions of 1g, 40g, and 80 g to model reverse faulting. The soil

deposits in prototype scale have thicknesses of 0.2 m, 8 m, and 16 m, respectively. This study also investigates the evolution of a

surface deformation profile and the propagation of subsurface rupture traces through overlying sand. This study proposes a

methodology for calibrating the micromechanical material parameters used in the numerical simulation based on the measured

surface settlements of the tested sand bed in the self-weight consolidation stage. The test results show that steeper surface slope

on the surface deformation profile, a wider shear band on the major faulting-induced distortion zone, and more faulting appeared

in the shallower depths in the 1-g reverse faulting model test than in the tests involving higher-g levels. The surface deformation

profile measured from the higher-g physical modeling and that calculated from numerical modeling show good agreement. The

width of the shear band obtained from the numerical simulation was slightly wider than that from the physical modeling at the

same g-levels and the position of the shear band moved an offset of 15 mm in model scale to the footwall compared with the results

of physical modeling.

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The decisions made in the planning phase of a building project greatly affect its future operation and maintenance (O&M) cost. Recognizing the O&M cost of condominiums’ common facilities as a critical issue for home owners, this research aims to develop an artificial neural network (ANN) O&M cost prediction model to assist developers and architects in effectively assessing the impacts of their decisions made in the planning phase of condominium projects on future O&M costs. A regression cost prediction model was also developed as a benchmark model for testing the predictive accuracy of the ANN model. Six critical building design attributes (building age, number of apartment units, number of floors, average sale price, total floor area, and common facility floor area) which are usually available in the project planning phase, were identified as the input factors to both models and average monthly O&M cost as the output factor. 55 of the 65 existing condominium properties randomly selected were treated as the training samples whose data were used to develop the ANN and regression models the other ten as the test samples to compare and verify the predictive performance of both models. The study results revealed that the ANN model delivers more accurate and reliable cost prediction results, with lower average absolute error around 7.2% and maximum absolute error around 16.7%, as compared with the regression model. This study shows that ANN is an effective method in predicting building O&M costs in the project planning phase. Keywords: Project management, Facility management, Common facilities, Cost modeling.

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When geogrid reinforcement is used as a treatment method for improving soft subgrade as a roadway foundation, a top layer of subgrade is usually excavated and backfilled with geogrid-reinforced aggregates. This treatment method produces an adequate platform for the planned roadway construction site, where heavy traffic loading is constantly moving. This paper presents a quantitative assessment of subgrade improvement by geogrid reinforcement based on numerical modelling and parametric studies. First of all, the preliminary numerical models were verified by comparing the analysis results with previous studies. Secondly, the major numerical models in this study were assumed to be a simplified simulation of a geogrid-reinforced two-layer system with an aggregate layer above a subgrade layer. The numerical models were applied a quasi-static loading and unloading cycle, in order to monitor the permanent deformation at the surface of the models. Afterwards, thickness of aggregate layer, and subgrade CBR values were varied in order to summarize the outcomes of each case. This approach makes it possible to quantify the effects of geogrid reinforcement and aggregate material in terms of an enhanced California Bearing Ratio (CBR) of a single subgrade clay layer. Results have shown that when the aggregate thickness is up to 450mm, the contribution of enhanced CBR is mostly from aggregate material. However, when the aggregate thickness is about 150mm with a relatively weak subgrade material, the inclusion of geogrid material can contribute about 50% of the enhanced value.

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This study evaluated the effects of Warm Mix Asphalt (WMA) additives on the compaction temperature and properties of Crumb Rubber Modified (CRM) asphalt binder and mixture. Two different WMA additives (named as Sas and Evm) were used to prepare warm-mix CRM asphalt binder and mixture. The viscosity of different warm-mix CRM asphalt binders and mastics were measured at different temperatures. The rheological and mechanical properties of different warm-mix CRM asphalt binders were tested. At the mixture level, the volumetric properties of different warm-mix CRM asphalt mixtures were experimented by Gyratory compactor at different temperatures and the performance of different warm-mix CRM asphalt mixtures were evaluated. It was found that, both of the two WMA additives could lower the compaction temperatures of CRM asphalt mixtures by 10°C~20°C. However, they have different influences on rheological properties of CRM binder and performance of CRM mixture. The Sas warm-mix additive can improve the anti-rutting performance of CRM mixture but may degrade its low-temperature performance and moisture stability. The Evm warm-mix additive has no adverse effects on the high-temperature and low-temperature performance of CRM asphalt mixtures and can improve its moisture stability.