新奥法在三连拱特大断面隧道施工中的应用

在建的中环湾仔绕道和东区走廊连接路项目(绕道项目)是一条位于香港岛北岸的策略性干道,其建造目的在于缓解香港岛区北岸东西向的交通挤塞情况。拟介绍的一段暗挖隧道是绕道项目的重要组成部分,该暗挖隧道长约160 m,由3条行车管道组成,横断面总跨度达50 m,总开挖横断面面积达460 m2,且在现有红磡海底隧道港岛区入口下方约20 m处穿过,是整个绕道项目的控制性工程。主要总结如何成功运用新奥法指导机械钻打法开挖三连拱硬岩隧道,并通过分部、分阶段开挖的精心安排克服大跨度开挖带来的困难,以及如何应用岩石质量分类Q系统(NGI Q-System)结合隧道实际的地质情况,从多个角度优化临时支护设计。此外,介绍如何综合运用监控量测、应急排水系统和配重砖等手段降低既有红磡海底隧道的上浮的风险。     

Simulation of foamed concrete compressive strength prediction using adaptive neuro-fuzzy inference system optimized by nature-inspired algorithms

Concrete compressive strength prediction is an essential process for material design and sustainability. This study investigates several novel hybrid adaptive neuro-fuzzy inference system (ANFIS) evolutionary models, i.e., ANFIS–particle swarm optimization (PSO), ANFIS–ant colony, ANFIS–differential evolution (DE), and ANFIS–genetic algorithm to predict the foamed concrete compressive strength. Several concrete properties, including cement content (C), oven dry density (O), water-to-binder ratio (W), and foamed volume (F) are used as input variables. A relevant data set is obtained from open-access published experimental investigations and used to build predictive models. The performance of the proposed predictive models is evaluated based on the mean performance (MP), which is the mean value of several statistical error indices. To optimize each predictive model and its input variables, univariate (C, O, W, and F), bivariate (C–O, C–W, C–F, O–W, O–F, and W–F), trivariate (C–O–W, C–W–F, O–W–F), and four-variate (C–O–W–F) combinations of input variables are constructed for each model. The results indicate that the best predictions obtained using the univariate, bivariate, trivariate, and four-variate models are ANFIS–DE– (O) (MP= 0.96), ANFIS–PSO– (C–O) (MP= 0.88), ANFIS–DE– (O–W–F) (MP= 0.94), and ANFIS–PSO– (C–O–W–F) (MP= 0.89), respectively. ANFIS–PSO– (C–O) yielded the best accurate prediction of compressive strength with an MP value of 0.96.     

Quantification of stormwater runoff using a combined GIS and curve number approach: a case study for an urban watershed in the Ozark Highlands,USA

Managing stormwater runoff is crucial to preserving water quality in rapidly developing urban watersheds. The objective of this study was to develop a methodology to test existing stormwater drainage infrastructure, identify potential areas of improvement, and estimate potentially contaminated runoff by combining two widely used stormwater runoff prediction models. A watershed containing much of the University of Arkansas-Fayetteville campus was targeted for this study because stormwater from this watershed drains into a local river designated as an impaired water body due to siltation. The curve number method was used to estimate runoff for various flood-return periods and antecedent moisture conditions, while a flow-direction model integrated topography, land use, and stormwater drainage infrastructure in a GIS. The methodology developed and results generated will help stormwater planners visualise localised runoff and potentially adapt existing drainage networks to accommodate runoff, prevent flooding and erosion, and improve the stormwater quality entering nearby surfacewater bodies.