BIM-based Generative Modular Housing Design and Implications for Post-Disaster Housing Recovery

The adverse social and financial impacts of catastrophic disasters are increasing as population centers grow. After disastrous events, the government agencies must respond to post-disaster housing issues quickly and efficiently and provide sufficient resources for the reconstruction of destroyed and damaged houses for full rehabilitation. However, post-disaster housing reconstruction is a highly complex process because of the large number of projects, shortage of resources, and heavy pressure for delivery of the projects after a disastrous event. This complexity and lack of an inconsistent, systematic approach for planning lead to an ad-hoc decision-making process and inefficient recovery. This research explored modular construction as a highly time-efficient approach to tackle the abovementioned challenges and facilitate the housing reconstruction process. Firstly, this research investigated the feasibility of using the modular construction method for rapid post-disaster housing reconstruction through a targeted literature review and survey of subject matter experts to broaden the understanding of modular construction-based post-disaster housing reconstruction, benefits, and barriers. Second, this research focused on improving the design and pre-planning phase of modular construction that can facilitate the successful implementation of modular construction in a post-disaster situation. To this end, a BIM-based generative modular housing design system was developed by using Generative Adversarial Networks (GANs) to automate the entire design process by incorporating manufacturing and construction constraints to fit the needs of the modular construction method. The framework was further extended by developing an optimization model to optimize the modularization strategy in the early design phase which was capable of reflecting the entire multi-stage process of modular construction (production, transportation, and assembly), and considering both individual project’s requirements and post-disaster housing reconstruction portfolio’s requirements. The outcomes of this study fit the MC industry that may be used by designers and modular housing companies looking to automate their design process. It is also expected to provide critical benchmarks for planners, decision-makers, and community developers to facilitate their decision-making process on considering modular construction as an efficient way for mass post-disaster housing reconstruction and addressing communities’ housing needs following a disastrous event

Exploring structure sharing in services and using the principles of product design to conceptualise modular workstations

The object of this thesis is to explore the design requirements of a modular and automated mobile workstation, and conceptualise it by exploring product design principles. During the thesis, a dynamic market survey has been conducted to explore existing competition and understand the user requirements for such a product. A thorough study was conducted to understand the rise and fall of open offices in the professional work environment and its relation to individual productivity. Engineering design principles such as Structure Sharing and Modularisation were effectively explored and utilised during this thesis. Structure sharing as a concept was used to explore sharing that occurs in the organisational structure of an industry providing shared services to the customers. AirBnB, a global shared-hospitality service provider, was used as the primary case-study for this purpose. An approach has been made to understand modularity and inclusive design, and find a common ground to apply the concepts of product development in the field of large-scale distributed construction. The results of this research can now be used for the conceptual design of a workstation for diverse users, by applying structure sharing at an organisational level, as studied for case specific services in this thesis. This design can then be evaluated for resource effectiveness using existing design methodologies or can be used to develop new methodologies

Repurposing existing skeletal spatial structure (SkS) system designs using the Field Information Modeling (FIM) framework for generative decision-support in future construction projects

Skeletal spatial structure (SkS) systems are modular systems which have shown promise to support mass customization, and sustainability in construction. SkS have been used extensively in the reconstruction efforts since World War II, particularly to build geometrically flexible and free-form structures. By employing advanced digital engineering and construction practices, the existing SkS designs may be repurposed to generate new optimal designs that satisfy current construction demands of contemporary societies. To this end, this study investigated the application of point cloud processing using the Field Information Modeling (FIM) framework for the digital documentation and generative redesign of existing SkS systems. Three new algorithms were proposed to (i) expand FIM to include generative decision-support; (ii) generate as-built building information modeling (BIM) for SkS; and (iii) modularize SkS designs with repeating patterns for optimal production and supply chain management. These algorithms incorporated a host of new AI-inspired methods, including support vector machine (SVM) for decision support; Bayesian optimization for neighborhood definition; Bayesian Gaussian mixture clustering for modularization; and Monte Carlo stochastic multi-criteria decision making (MCDM) for selection of the top Pareto front solutions obtained by the non-dominant sorting Genetic Algorithm (NSGA II). The algorithms were tested and validated on four real-world point cloud datasets to solve two generative modeling problems, namely, engineering design optimization and facility location optimization. It was observed that the proposed Bayesian neighborhood definition outperformed particle swarm and uniform sampling by 34% and 27%, respectively. The proposed SVM-based linear feature detection outperformed k-means and spectral clustering by 56% and 9%, respectively. Finally, the NSGA II algorithm combined with the stochastic MCDM produced diverse “top four” solutions based on project-specific criteria. The results indicate promise for future utilization of the framework to produce training datasets for generative adversarial networks that generate new designs based only on stakeholder requirements

Product Platform as a Concept to Increase Production Competitiveness

Dalam era pasar global yang dinamis dengan tingkat persaingan tinggi, perkembangan suatu produk dan proses produksi menjadi perhatian utama bagi perusahaan. Beberapa hambatan yang muncul adalah daur hidup produk yang makin singkat, meningkatnya persaingan internasional, perubahan teknologi dan keanekaragaman pilihan konsumen yang mendorong percepatan proses pengembangan dari suatu produk baru. Untuk dapat mengelola tingkat kompleksitas keragaman produk yang dimiliki perusahaan untuk ditawarkan kepada pasar, beberapa industri mengaplikasikan konsep Platfomn. Sementara untuk dapat meningkatkan keunggulan daya saing produksi, pendekatan yang digunakan adalah pengembangan produk dengan tingkat kompleksitas rendah dan memiliki tingkat investasi minimal dalam hal perancangan, produksi dan pemasaran. Konsep Platform produk mengidentifikasi dan menjabarkan persamaan proses/perlakuan terhadap produk, pasar target, proses pengembangan dan pengiriman oleh perusahaan pada tingkat harga yang efisien. Konsep Platform yang dibahas adalah stardarisasi komponen, platform produk, platform proses, platform pengetahuan, manusia dan hubungan antar platform tampaknya menjadi strategi yang berhasil untuk menciptakan berbagai biaya rendah. Dinamika kekuatan pasar yang mendorong perusahaan untuk mengembangkan produk-produk desainer dengan menciptakan modul standar sebanyak mungkin dari seluruh produk yang dimiliki. Diharapkan dengan menerapkan metode modul standar tersebut membuat perusahaan dapat lebih tangguh bersaing dibandingkan hanya memiliki suatu desain utuh dalam era globalisasi.Keywords: Product Platform, Production Competitiveness, Modularity Metho

Design And Analysis Of Cold Forged AUV Propeller

Most of power of an Autonomous Underwater Vehicle (AUV) is utilized by its propulsion system. Since AUV power only depends on onboard battery, the power consumption becomes crucial issue in optimizing the AUV performance. In this research a propeller need to be fabricated for AUV that will be developed by Underwater Robotic Research Group (URRG). The specific propeller design must be discovered in order to optimize the AUV power consumption. PVL code that has been developed by Kerwin (2001) was used as a tool to design the specific propeller

Annotated bibliography of Software Engineering Laboratory literature

An annotated bibliography of technical papers, documents, and memorandums produced by or related to the Software Engineering Laboratory is given. More than 100 publications are summarized. These publications cover many areas of software engineering and range from research reports to software documentation. All materials have been grouped into eight general subject areas for easy reference: The Software Engineering Laboratory; The Software Engineering Laboratory: Software Development Documents; Software Tools; Software Models; Software Measurement; Technology Evaluations; Ada Technology; and Data Collection. Subject and author indexes further classify these documents by specific topic and individual author