秘鲁绿山斑岩铜钼矿床地质特征及成因

绿山(Cerro Verde)斑岩铜钼矿床位于秘鲁南部古新世—始新世斑岩铜钼成矿带的北端。矿体赋存于前寒武系Charcani片麻岩、晚白垩世Yarabamba花岗闪长岩、古新世英安斑岩和石英电气石角砾岩中,热液蚀变由中心向外分别为:钾化带—泥化带—石英绢云母化带—青磐岩化带。矿床形成与板块俯冲有关的岛弧环境,受区域性印加普丘断裂控制,在古新世英安斑岩侵位,带来了大量成矿热液,形成了世界级的超大型斑岩铜钼矿床—绿山斑岩铜钼矿床。     

内蒙古烧饼沟铜多金属矿地质特征及找矿前景

通过矿床地质特征及成岩、成矿因素研究,认为海西晚期发生挤压造山产生NE向褶皱,沿褶皱轴海西晚期似斑状中细粒花岗闪长岩、石英闪长岩侵入,在与西别河组片岩的接触带内侧,形成接触破碎蚀变岩型—裂隙充填型的热液铜多金属矿床,矿区内蚀变矿化多赋存在北东向构造内。围岩蚀变较为发育,与成矿关系密切的为硅化、黄铁矿化、碳酸盐化、萤石化等。文章在以往地质成果的基础上,通过成矿地质背景、矿区地质概况和矿床地质特征的阐述,着重对成矿控制因素、矿床成因及找矿标志进行分析,认为该区具有较好的成矿地质条件和找矿前景。     

东昆仑沙丘低缓磁异常区深部铁多金属矿床找矿标志

那陵郭勒河沙丘地区位于东昆仑祁漫塔格早古生代裂陷槽,经历了早古生代及晚古生代—早中生代的复合造山作用,发育多期次岩浆活动和成矿作用,具有较好的铁多金属找矿前景。文章通过研究沙丘铁多金属矿的成矿地质背景、矿床特征、地磁异常和地球化学异常等,探讨了其找矿标志。研究表明:沙丘地区铁多金属矿床成因类型属接触交代型(矽卡岩型),矽卡岩与磁铁矿体在空间分布紧密共生,是重要的找矿标志;引起该区磁异常的主要地质体为闪锌磁铁矿矿石、闪锌磁铁矿化矽卡岩、矽卡岩、闪长岩、硅质岩、玄武岩、钾长花岗斑岩;利用物探磁异常找寻磁铁矿作用显著,在低缓磁异常区、正负异常接触部位是找寻铁多金属矿最重要也是最直接的找矿标志。     

辽宁王营子钼矿地质特征及找矿标志

辽宁省朝阳市王营子钨钼矿位于努鲁儿虎山脉大青山东部,属辽西凌源—北票金成矿带高杖子—大庙—东五家子金成矿区中部,蝴蝶沟—王营子金钼多金属矿化带。研究区内地层、岩浆岩发育较好,构造未见明显特征。主要岩体以斑状花岗岩、似斑状花岗岩为主,矿体严格受似斑状花岗岩控制,富集于钾化似斑花岗岩中,矿脉与围岩无明显界线。基于野外实际勘查、成矿地质条件等的研究,认为王营子钼矿为细脉浸染型斑岩型钼矿,并总结了找矿标志。     

西藏普雄矿区岩浆岩岩石地球化学特征及地质意义

普雄矿区位于西藏冈底斯成矿带东段,毗邻驱龙—甲玛铜多金属矿集区,是该地区近年来新发现的具斑岩—矽卡岩型铜多金属矿潜力矿区。经前期地质勘查工作,已发现数条具工业价值的矽卡岩型铜多金属矿体,矿床规模有望达到中—大型;成矿元素以Cu为主,伴Fe、Au、Pb、Zn。随勘探工作不断深入,成矿体系确定和成矿潜力评价成为勘探工作难点,区内岩浆岩特征研究成为解决这一问题的重点。文章对矿区出露的岩浆岩进行岩石学和地球化学研究,结果表明:区内岩浆岩岩石类型以辉绿玢岩、石英闪长岩及花岗闪长岩为主,属过铝质钙碱性—高钾钙碱性岩石系列。岩浆岩均为轻稀土富集,重稀土亏损,除花岗闪长岩外,其他岩石未见Eu异常。综上:普雄矿区岩浆岩以中基性闪长质I型岩石系列为主,微量和稀土元素特征同典型斑岩—矽卡岩型铜矿类似,具埃达克岩性质;此外,接触带矽卡岩的发现及矿区岩浆混合作用异常发育,都暗示该矿区有望找到斑岩—矽卡岩铜成矿系统。     

The conditional mean spectra by disaggregating the eta spectral shape indicator

Conditional spectra are a recent development in this field, which utilizes the advantages of spectral shape indicators, for example, epsilon and eta. The application of an eta indicator in conditional spectra calculations depends mainly on calculating the peak ground velocity epsilon, data about which are not readily available in the current literature. This issue has been solved by linear regression between the conventional epsilon and the peak ground velocity epsilon. However, not enough attention has been paid in the literature to the disaggregation of the eta indicator. For this reason, the disaggregation of seismic hazard based on the use of an eta indicator has been investigated in this paper, based on a simplified linear seismic source. The obtained results were compared with the available approach in the literature, which shows that this refinement has a meaningful effect on the conditional spectra specifically in the short period range. Furthermore, eta‐based conditional spectra are used at different hazard levels to select ground‐motion records. A three‐storeyed building is then analysed, and the corresponding annual probability of failure is calculated based on the full dataset as well as on the records, which were selected based on conditional spectra.     

Field measurement and aeroelastic wind tunnel test of wind‐induced vibrations of lattice tower

Lattice towers are among the most vulnerable structures during typhoons. In this study, the wind engineering research field base near Shanghai Pudong International Airport was used to measure the mean wind speeds and directions at a height of 10 m, as well as the accelerations atop a 40‐m high lattice tower during Typhoons Neoguri and Nakri to investigate the characteristics of the near‐ground wind and the responses of the lattice tower. Moreover, an aeroelastic model of the lattice tower was generated by the discrete stiffness method. Using the wind tunnel test of the aeroelastic model, the root mean square accelerations of the tower were studied with different wind speeds and directions. The responses of the tower in the across‐wind direction were found always higher than those in the along‐wind direction, which meant the vortex shedding of each single cylinder had an impact on the responses of the tower in the across‐wind direction. The test results were compared with those of field measurements to evaluate the accuracy of the aeroelastic model, and the two were generally in a good agreement. Thus, the aeroelastic model could precisely simulate the dynamic characteristics of a prototypical lattice tower using the discrete stiffness method.     

Dynamic stress analysis for fatigue damage prognosis of long-span bridges

For fatigue damage prognosis of a long-span steel bridge, the dynamic stress analysis of critical structural components of the bridge under the future dynamic vehicle loading is essential. This paper thus presents a framework of dynamic stress analysis for fatigue damage prognosis of long-span steel bridges under the future dynamic vehicle loading. The multi-scale finite element (FE) model of the bridge is first developed using shell/plate elements to simulate the critical structural components (local models) and using beam/truss elements to simulate the rest part of the bridge (global model). With the appropriate coupling of the global and local models, the multi-scale FE model can accurately capture simultaneously not only the global behavior in terms of displacement and acceleration but also the local behavior in terms of stress and strain. A vehicle traffic load model is then developed for forecasting the future vehicle loading based on the recorded weigh-in-motion (WIM) data and using the agent-based traffic flow microsimulation. The forecasted future vehicle loading is finally applied on the multi-scale model of a real long-span cable-stayed bridge for dynamic stress analysis and fatigue damage prognosis. The obtained results show that the proposed framework is effective and accurate for dynamic stress analysis and fatigue damage prognosis.     

Student perceptions of model-based estimating

The use of building information modeling (BIM) is well established in architecture, engineering and construction. However, previous studies note a lack of BIM expertise in academia, leading to slow adoption and limited knowledge on which to base BIM-related content. To improve course development, it is important to understand students’ perceptions of BIM-related subject matter. This study exposed estimating students (n = 214) to a model-based quantity takeoff (QTO) tutorial using Revit. Students’ perceptions of a Revit-based BIM’s usefulness for easily providing reliable and accurate material quantities were measured using a pre-test, tutorial and post-test design. Results revealed significant differences in students’ mean levels of agreement that an accurate QTO is completed for an estimator with the click of a button when a design team generated BIM model is available. Results also revealed significant differences in students’ level of agreement that a Revit-based BIM makes performing QTO by hand obsolete. This study provides educators’ insight into students’ perceptions of BIM use in QTO. Understanding student perceptions is paramount for construction management educators when designing a course and attempting to convey the value of foundational estimating skills while acknowledging the existence of more technologically advance estimating methods. Limitations and opportunities for further research are discussed.     

Prediction of the crack condition of highway pavements using machine learning models

Departments of Transportation regularly evaluate the condition of pavements through visual inspections, nondestructive evaluations, image recognition models and learning algorithms. The above methodologies, though efficient, have drawn attention due to their subjective errors, uncertainties, noise effects and overfitting. To improve on the outcomes of the shallow learning models already used in pavement crack prediction, this paper reports on an investigation of the use of recursive partitioning and artificial neural networks (ANN; deep learning frameworks) in predicting the crack rating of pavements. Explanatory variables such as the average daily traffic and truck factor, roadway functional class, asphalt thickness, and pavement condition time series data are employed in the model formulation. Overall, it is observed that the recursive partitioning (regression tree – R² > 0.8 and classification tree – R² > 0.6) and ANN (continuous response – R² > 0.8 and categorical response – R² > 0.6) are compelling machine learning models for the prediction of the crack ratings based on their goodness-of-fit statistics, mean absolute deviation (MAD < 0.4) and the root mean square errors (RMSE between 0.30 and 0.65).