CIBC6035: COST PLANNINGSQL

Java Python CIBC6035

Cost Planning

Preliminary Estimating

Thursday 12th August 2024

PRELIMINARY ESTIMATE – Warehouse & Office

2. Produce an estimate for the proposed warehouse and office building to be built on a level site in Mt. Wellington Auckland. The proposed project consist of a single storey warehouse building and a four (4) storeys office building complete with all necessary site works. All four storeys for the office building are identical in every aspect (size, functions, etc.), all are lettable shell only (tenant fitout excluded).

Tender date is to be January 2025. The building works for the project will take 12 months construction period, from tender date to completion.

The Site Plan is attached, showing building orientation, b CIBC6035: COST PLANNINGSQL oundaries, road access and associated site works.

3. Choose an appropriate estimating method, taking into account this very early stage of the design, and the degree of design information given.

4. Using attached cost data, adjust the rates as you consider appropriate to arrive at a sensible estimate. Explain the process you have used and its logic.

5. Present your preliminary estimate in the form. of a one page Client Summary, listing exclusions and any other important information which should be included with the preliminary estimate.

6. Grassed area: $30/m2; Asphalt drive $78/m2, concrete kerbing $35/m, porcelain tiles 120 $/m2, landscape paving 110$/m2. All rates from Rawlinsons 2013/4 = 3Qtr.2019

7. Allow $60,000 for external drainage, $70,000 for external signage & lighting, $8,000 for road marking         

(Kriging_NSGA2)克里金模型结合多目标遗传算法求最优因变量及对应的最佳自变量组合研究(Matlab代码实现)内容概要:本文介绍了克里金模型(Kriging)与多目标遗传算法NSGA-II相结合的方法,用于求解最优因变量及其对应的最佳自变量组合,并提供了完整的Matlab代码实现。该方法首先利用克里金模型构建高精度的代理模型,逼近复杂的非线性系统响应,减少计算成本;随后结合NSGA-II算法进行多目标优化,搜索帕累托前沿解集,从而获得多个最优折衷方案。文中详细阐述了代理模型构建、算法集成流程及参数设置,适用于工程设计、参数反演等复杂优化问题。此外,文档还展示了该方法在SCI一区论文中的复现应用,体现了其科学性与实用性。; 适合人群:具备一定Matlab编程基础,熟悉优化算法和数值建模的研究生、科研人员及工程技术人员,尤其适合从事仿真优化、实验设计、代理模型研究的相关领域工作者。; 使用场景及目标:①解决高计算成本的多目标优化问题,通过代理模型降低仿真次数;②在无法解析求导或函数高度非线性的情况下寻找最优变量组合;③复现SCI高水平论文中的优化方法,提升科研可信度与效率;④应用于工程设计、能源系统调度、智能制造等需参数优化的实际场景。; 阅读建议:建议读者结合提供的Matlab代码逐段理解算法实现过程,重点关注克里金模型的构建步骤与NSGA-II的集成方式,建议自行调整测试函数或实际案例验证算法性能,并配合YALMIP等工具包扩展优化求解能力。
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