DESIGNS AND SPECIFICATIONS FOR BUILDINGS”

FIELD OF THE INVENTION

The present invention generally relates to a system and method for generating architectural designs of buildings and, more particularly, to a system and method for producing detailed specifications of buildings.

BACKGROUND OF THE INVENTION

The forecasted growth in global population and the subsequent surge in demand for construction present significant challenges for architects and the field of architecture. By the year 2030, the UN-Habitat predicts that approximately 3 billion people, accounting for around 40 percent of the world's population, will require access to suitable housing. This staggering statistic translates to an immense need for the construction of 96,000 new affordable and accessible housing units on a daily basis.

Despite the critical role that architects play in designing habitable shelters, they currently cater to less than a quarter of the global population. This discrepancy highlights the vast gap between the demand and supply for architectural services. Moreover, a significant portion of the world's population still relies on traditional or makeshift construction methods, which are often less efficient, less standardized, and have a significant scope for improvement.

Given the monumental increase in construction demand, architects are faced with the challenge of meeting these needs using conventional manual techniques. Many of the prevalent practices that the architects follow, are timeconsuming and labour-intensive. While these methods differ across various professionals and practitioners, they often fail to address the architectural needs at a global level that requires a more formal, systematic, and scalable approach.

In recent years, there has been a notable trend towards the development of more complex and sophisticated software systems in this field. Some attempts have been made to provide computerized software solutions for architectural design, aiming to automate the generation of construction drawings based on predefined inputs. However, ensuring the overall effectiveness and quality of such software systems presents its own set of challenges.

Currently, the available architectural software systems serve to support architects, designers, and developers in designing more efficiently and visualizing their creations in two-dimensional or three-dimensional environments. However, these tools rely heavily on human interaction, skill, and effort. Professionals are required to input extensive data parameters and specifications to manually generate accurate drawings and visualizations for their designs. This process is not only time-consuming but also complex and expensive. This reliance on extensive human effort, can lead to increased inefficiencies and potential errors.

As the demand for construction continues to grow, it becomes increasingly important to explore innovative solutions that can streamline the architectural design and construction processes. Automation and advanced technology have the potential to revolutionize the field of architecture, enabling professionals to address the global construction need with greater efficiency, accuracy, and speed. There have been some attempts in the prior art to provide systems assisting architectural processes and building construction designs.

The United States Patent Application No: US9384174B 1 to Visions Comp Imaging Systems Ltd is an automated system for assisting the architectural process that utilizes an input form to gather design information about an architectural project or part of a project for assembly and placement in the aspects of the contract document. The U.S Patent includes four main modules that may consist of a Schedule Module, a Detail Module, a Specification Module, and a Manufacturer's Module. The U.S Patent focuses on automating the generation of contract documents rather than simulating two dimensional, three-dimensional architectural designs.

The Chinese Patent Application No: CN107665500A to Wei Hsein Chen discloses an interior design system using real-time rendering technology. The 3D real-time rendering technology is useful in the process of internal space designing. The online design platform enables the user to design the space and allows a variety of permutation and combinations of designs for the user. It is to be noted that such a system allows interior designing, however, it does not enable a user to construct and develop the architectural design and specification for the overall building.

The Japanese Patent Application No. JP2002111887A to Andrews Stuart Argyle discloses a home automation centralized control system. This Japanese invention relates to improvements made on home automation which is capable of operating electrical circuits such as lighting fixtures, home appliances, hot water supply equipment for bathrooms etc. from a remote location. It is to be noted that such a centralized home automation control system is for home appliances and other equipment, but the invention does not mention a system that visualises and generates an architectural design for a building.

There is a need for a system to generate architectural designs and specifications for buildings such as three-dimensional visualisation, two- dimensional drawings, detailed material costing, bill of materials for construction, energy efficiency evaluation, and the likes with minimal human intervention and skills.

SUMMARY OF THE INVENTION:

A system for generating architectural designs and specifications for buildings includes a plurality of electronic devices. The user interface unit communicates with a plurality of users through the plurality of electronic devices. The system includes a processing unit processing the input data and a database unit storing datasets defining basis to generate outputs.

The user interface unit includes an input module for accepting input data, the processing unit includes an intelligent engine configured for processing input data and generating output in two-dimensional and/or three-dimensional rendered views. The system also includes a controller configured for communicating with the user interface unit and the database unit and processing data on the Intelligent Engine. The database unit includes an engine dataset and an architectural dataset configured for storing architectural specifications and attributes of buildings in a predefined climatic zone. The engine data set stores multiple datasets required for generating the output including building designs and specifications. The system includes an Abstract Building Model ‘ABM’ configured for generating a multitude of output data being generated from a single typology definition. The intelligent engine includes a climatic zone selector module configured for selecting building models suitable for the intended construction site.

Further, the intelligent engine includes a typology mapper module being configured for determining the typology suited for a building. Further, the intelligent engine includes a material permutation generator module producing all the valid material selections potentially applied to the ABM. Further, the intelligent engine includes an object importer generator module being configured for importing object geometries formulated in separate files into the ABM.

The intelligent engine also includes an estimator module being configured for computing estimates for the building. The intelligent engine also includes the structural generator module being configured to generate complex structural geometries within the ABM. The engine dataset includes a typology mapping dataset that is configured to identify a correct building typology for making user specific output. The typology mapper module is configured to select an appropriate abstract building model, rotation orientation, material compatibility rules, and material master relevant to each use case. The engine data set includes a material compatibility graph dataset for storing material compatibility rules. The abstract building model dataset includes a plurality of global constants that are imported into the building models to assist with the resolution of objects in the abstract models. The typology includes a set of parameters unique to a predefined building design in accordance with a plurality of factors including climatic context, dimensionality and spatial configuration. The abstract building model dataset is configured to store various ABM corresponding to a particular climatic zone. The engine data sets include a texture dataset that is configured for defining textures in accordance with building models.

The intelligent engine includes a climatic zone selector module for selecting the appropriate climatic zone of the building to be rendered. The intelligent engine includes a typology mapper module configured for determining typology suited for a building based on parameters received from the user and the climatic zone of the building. The structural generator module is configured for generating objects ABM producing complex structural geometries within the building. The material permutation generator module produces all legitimate material permutations for a feature based on the material compatibility rules defined in the material compatibility dataset. The reactor module is configured to generate a feature mutation for a given abstract feature model of the building.

A method for generating architectural designs and specifications for buildings system comprising steps of receiving predefined user inputs; initializing engine data sets; selecting appropriate climatic zone; selecting appropriate typology of the building; generating material permutations for each feature; generating a feature mutation from an abstract feature; calculating the cost of each feature mutation; selecting an optimal feature mutation; generating the final building model; rendering the 3-Dimensional building output; rendering the 2- Dimensional building output; generating the costing details; generating the bill of materials; and generating the energy -efficiency index.

BRIEF DESCRIPTION OF DRAWINGS

The objectives and advantages of the present invention will become apparent from the following description read in accordance with the accompanying drawings herein.

FIG. 1 shows a system for generating architectural details and specifications of buildings in accordance with the present invention;

FIG. 2 is a schematic diagram of the system for generating architectural details and specifications of buildings of FIG.1;

FIG. 3 is a system diagram of the engine dataset in accordance with the present invention of FIG.2;

FIG. 4 is a system diagram of the intelligent engine in accordance with the present invention of FIG.2;

FIG. 4A is a system diagram of the output module in accordance with the present invention of FIG. 2;

FIG. 5 is a flow diagram of the system for generating architectural details and specifications of buildings of FIG. 2; FIG. 5A is a continued flow diagram of the system for generating architectural details and specifications of buildings of FIG. 5;

FIG. 5B is a continued flow diagram of the system for generating architectural details and specifications of buildings of FIG. 5A;

FIG. 6 shows an output generated in accordance with the present invention providing architectural details and specifications generated as per user input parameters of FIG. 2; and

FIG. 6A shows a segregated estimate for the output provided in FIG. 6.

DETAILED DESCRIPTION OF THE INVENTION

The invention described herein is explained using specific exemplary details for better understanding. However, the invention disclosed can be worked on by a person skilled in the art without the use of these specific details.

References in the specification to "one embodiment" or "an embodiment" means that a particular feature, structure, characteristic, or function described in connection with the embodiment is included in at least one embodiment of the invention. The appearances of the phrase “in one embodiment” in various places in the specification are not necessarily all referring to the same embodiment.

References in the specification to “preferred embodiment” means that a particular feature, structure, characteristic, or function is described in detail thereby omitting known constructions and functions for a clear description of the present invention. The foregoing description of specific embodiments of the present invention have been presented for purposes of illustration and description. They are not intended to be exhaustive or to limit the present invention to the precise forms disclosed, and many modifications and variations are possible in light of the above teaching.

Referring to FIG. 1, a system for generating architectural designs and specifications for buildings 100, hereinafter, referred to as the system 100 is described. The users 101 access the system 100 through a plurality of electronic devices 104. It is to be noted that, the electronic devices 104 may include cellular mobile devices, laptops, personal computers, handheld digital tablets or the like. The users 101 insert input data 102 to the system 100 through the electronic devices 104. Further, the system 100 processes the received input data 102 and generates an equivalent output data 103 and sends it back to the electronic devices 104 respectively. In the present invention, the users 101 communicate with the system 100 over the Internet, Intranet or the like.

Referring to FIGS. 1 to 2, the system for generating architectural designs and specifications for buildings 100, is described hereinafter. The system 100 includes a user interface unit 205, a processing unit 210 and a database unit 215. The user interface unit 205 includes an input module 208, a parameters module 204 and an output module 212. The processing unit 210 includes an intelligent engine 216 and a controller 220. The database unit 215 includes an architectural dataset 224 and an engine dataset 228. The processing unit 210 bidirectionally communicates with the user interface unit 205 and the database unit 215. In accordance with the present invention, the users 101 enter a plurality of user inputs and parameters through the electronic devices 104. The input module 208 receives a plurality of the user inputs and parameters from the electronic devices 104, that are further fed to the processing unit 210.

In context of the present invention, the controller 220 is configured to communicate with the electronic devices 104, the input module 208, and the output module 212 to receive inputs from the users 101 in the form of user preferences and parameters, and to deliver outputs i.e., generated information related to the design, specifications, and construction of a building as per the user’ s requirements.

It is noted that the input parameters are selectable by a user 101 and are the user preferences that are provided to the input module 208 through the electronic devices 104. For example, in an embodiment, the user selectable inputs are provided in the form of constraint inputs, dropdown menus, or selectable options on an electronic device 104 such as a touchscreen. In another embodiment, the inputs are provided by the user 101 with the help of a keyboard, voice commands, navigation using a mouse or the like.

In accordance with the present invention, user parameters representing the location of the construction site are defined by the exact latitude and longitude of the construction site. The location is traced by fetching the geo-coordinates using the Global Positioning System (GPS) or by stating the address of the location. The location details provided by the users 101 assists the intelligent engine 216 to evaluate the climatic zone and to determine the relevant material data and the architectural typologies relevant for that specific location.

The system 100 also scans the site topography with built in LiDAR (Light Detection and Ranging) sensors connected to certain smart devices to map three- dimensional mesh of the site and its surroundings. These meshes are then used to suggest appropriate structural components for the construction of the desired building.

The input module 208 enables the users 101 to provide the size of the building by specifying the appropriate dimensions along the North, South, East, West axis for the desired construction area. In addition, the users 101 specify the preferred direction of the entrance door for the building. The number of floors, number of rooms, and the requirement of toilets as per the personal choice and availability of the space is insertable by the users 101 in the input module 208.

The user 101 also specifies the available budget for construction through the input module 208. These user parameters provided to the input module 208 are a set of inputs from the user 101 in order to customize the outputs of the system 100 as per the requirements of the users 101. Further, the parameters module 204 receive the user parameters from the input module 208 and send it to the intelligent engine 216 for processing.

The users 101 are also given an opportunity to opt for energy efficient buildings or carbon neutral buildings. In case any such choice is made by the users 101, the system 100 gives preference to suggesting an environment friendly, sustainable, and energy efficient building design as opposed to a conventional building design. The users 101 are given options of design that have varying energy efficiency measures ranging from 25% to 100%. The costing and specifications of the building also change accordingly.

The output data 103 is generated by the intelligent engine 216. The generated output data 103 includes 3-dimensional models (3D), 2-dimensional model (2D) drawings, detailed costing of the building, and the Bill Of Materials (BOM) for the provided real time input data 102. The output module 212 receives the output data 103 and displays the data 103 on the electronic devices 104.

The output module 212 is configured to receive the output data 103 from the Intelligent Engine 216. The output data 103 includes a compiled list of user’s personal information such as, name, email address, and phone number used during the sign-up process by the users 101 and all the user parameters submitted by the users 101 to design the building.

Further, the output data 103 includes a 3 -dimensional model that is a visual representation showcasing all the features of the building and appropriate construction materials customised to fit the users 101 input parameters and expectations. The 3D model is interactive in nature and provides the users 101 with the capabilities to zoom and pan to see the rendered 3D model in detail on the electronic devices 104. Various elements of the model such as the exterior walls, roof, and fenestration may be turned on and off to visualise the building in various stages of construction using on-screen toggle buttons. The output data 103 also includes 2-dimensional representations of the designed building that show the spatial plan and sectional drawings of the building and specify the name tags and dimensions of the individual spaces within the building. Additionally, the users 101 may also select individual components in these 2-dimensional representations to view details of a particular component, such as its size and material.

Additionally, a detailed costing sheet expressing accurate material and labour costs for all the structural elements of the building is also presented as a part of the output data 103. The output data 103 also includes a BOM that is a compiled material list that expresses the quantities of each material required for the construction. The list is used by the users 101 to procure construction materials from the market.

The output data 103 also includes an Energy Efficiency Index (EEI). EEI is an index that represents the embodied energy of materials and processes encountered through the process of construction of a building. Further, it also analyses the carbon impact of the building on our planet. The EEI uses colour coded tables to express the efficiency of a particular material, architectural feature or the likes. The output data 103 also includes a thumb rule timeline for the construction of the building based on location, type, and size of construction.

The generated output is an interactive view presenting the details of the desired building. Various forms of information are available in this view such as, the material details, construction details, annotated dimensions on the 2D and 3D outputs, material specifications, or the like. The output module 212 provides various user interface components such as buttons, drop-down menus, filters and other selection tools that allow the users 101 to interact with the output data 103 easily. Further, the output module 212 has interactive capabilities that enable the users 101 to communicate with the system to track the progress of construction and to receive notifications for other relevant information pertaining to the construction process.

In accordance with the present invention, the architectural dataset 224 are formulated by architects and architectural researchers by mapping, measuring, and studying various architectural settlements and construction techniques in a said climatic zone.

Now referring to FIGS. 1 to 3, a plurality of datasets in accordance with the present invention are described hereinafter. Accordingly, a dataset includes a bundle of information defined by several elements such as architectural details, building typology details, structural details, material attributes, rules and protocols etc. It is, however, noted that the datasets are advantageously accessible to the intelligent engine 216. The datasets in this embodiment preferably define a predefined set of information required by the intelligent engine 216 that is developed or acquired through extensive architectural research. The engine dataset 228 of the system for generating architectural designs and specifications for buildings 100 is described hereinafter. The engine dataset 228 is developed based on the accumulated architectural dataset 224 to feed information to the system 100. The engine dataset 228 includes a climatic zone dataset 316. Initially, the climatic zone dataset 316 contains extensive master data about the geo-climatic zones across various geographic territories. The climatic zone dataset 316 defines a dense two-dimensional grid overlaid on the geographic map of that territory. Further, each individual cell in that grid is associated with a corresponding geo- climatic zone. Alternatively, the climatic zone dataset 316 defines a set of polygonal areas overlaid on the geographical map of that territory. Each polygon is then associated with a corresponding geo-climatic zone.

For example, in the grid-based approach, the geography of India is divided into seven geo-climatic zones namely Montane, Mountainous, Humid Subtropical, Semi-Arid, Arid, Tropical Wet and Dry / Tropical Savanna and Tropical Wet / Tropical Monsoon. This climatic information aids the intelligent engine 216 to identify the appropriate architecture information.

The engine dataset 228 includes a typology dataset 304 that is configured to identify a correct building typology for each use case. In this preferred embodiment, the typology dataset 304 is essentially a lookup table created based on the typology information in the architectural dataset 224. The engine data set 228 includes an abstract building model dataset 324 that is configured to store various Abstract Building Models hereinafter referred to as ‘ABM 328’(not shown) corresponding to a particular climatic zone.

A curated data set of locally usable materials for every climatic zone is part of the architectural dataset 224. This includes various attributes such as the cost per unit, dimensions, and the associated textures for each material. This information is then translated into a material master dataset 308 as part of the engine dataset 228. The engine dataset includes a material master dataset 308 that includes composite materials, sub-materials, abstract materials, elemental materials, and the corresponding attributes associated with each of them. Every material has its own attributes which have been specified in the material master dataset 308.

These are the physical attributes of the material which include the material name, the thickness of the material, visual texture corresponding to that material, scale-control for the texture. The material master dataset 308 also includes an index value for embodied energy and carbon emission for every material. In addition to these, the material master dataset 308 also mentions the cost of composite materials, both the material rate and labour rate are specified with the respective units of measure.

In context of the present invention, the composite material is defined as the material used in the building of a model, which is simultaneously visualised in the 3D model and utilised for cost calculations. Every composite material consists of one or more ingredient materials that are termed as sub-materials in the present invention. Additionally, the Abstract Material is defined as the material that is used solely for the purposes of cost calculation. Further, the elemental materials are primarily for visualization and exhibit textural characteristics. Elemental materials do not have capabilities to compute costs directly. The engine dataset 228 also includes a material rules dataset 312. The material compatibility rules are defined by the architects in the architectural dataset 224 that is converted into a Directed Acyclic Graph hereinafter referred to as ‘DAG’ expression, that is essentially stored inside the material rules dataset 312.

There are multiple DAGs in a particular climatic zone based on various types of structures such as Load-Bearing and Framed Structures. The DAG determines the various possible permutations of materials that the engine could choose from, for a given feature. The DAG is expressed using three components namely, Features, Sub Features, and the corresponding Composite or Abstract Materials.

Apart from this, the material rules dataset 312 is also responsible for specifying the intended allocation of the user's 101 budget across the various features of the building. These allocations eventually assist the intelligent engine 216 to determine a suitable material permutation for each feature so as to meet the aggregate budgetary expectations for that building.

In context of the present invention, the engine dataset 228 constitutes an Abstract Building Model i.e., ABM 328 is a geometric and semantic expression of the building to be designed. The ABM 328 fuses a multitude of building parameters into a unified representational language. The building parameters are for example “the constituent components that the building is made of’, “a geometric representation of every component”, “the semantic interrelationships and hierarchies between those components i.e., patterns in which the components come together to realize larger structures”, “the possible materials to be used in the construction of each component” and “the logic to determine the cost of each component and the aggregate-costs of larger components”.

In the present invention, every house or a building, every distinct architectural structure or pattern, is comprehensively captured in the form of an ABM 328. The ABM 328 is a tangible blueprint or specification for a given typology. It is noteworthy that the ABM 328 implements a heavily parameterized model that generates a multitude of results that are generated from a single typology definition. In context of the present invention, a single typology is defined by a set of parameters unique to a particular building design that is based on various factors such as climatic context, dimensionality, spatial configuration etc. The abstract building model dataset 324 is configured to store various ABM 328 corresponding to a particular climatic zone.

In accordance with the present invention, the intelligent engine 216 takes a componentized approach whereby the entirety of the building is represented as a collection of parts. For example, a house consists of a Foundation, a Ground Floor, and a Roof. Each of these parts, in turn, are composed of further sub-parts. The intelligent engine 216 follows a hierarchy as that of a tree data structure in the ABM 328. The lowest nodes in the tree represent individual physical components. For each such component, its parameterized geometry is specified.

Moreover, certain rules are specified on how to determine the cost of that component and how to determine the material of that component. The higher-order nodes in the tree often represent the semantic structure of the building as perceived by the architects and building users. The higher-order nodes thus serve as a way to logically group and organize the lower components; this becomes especially relevant in large buildings having thousands of components in them.

Further, in the ABM 328 tree, the most atomic constituent is a component node. All component nodes have a physical manifestation in the real world such as an actual column, beam, or a wall of a house. Contrast this with higher order nodes such as features, sub-features, or structures which are merely used for logical groupings and don’t have a physical manifestation of themselves at all. Further, the geometric shapes and dimensions of components need to be specified so they are visible in the building plans and models.

In accordance with the present invention, the component dimensions vary based on the size of the building, material used for the component itself, and based on the dimensions and materials used in its adjacent components or the like. It is hence that the component dimensions provided by the model maker in the ABM 328 are parameterized and dynamic in nature. The intelligent engine 216 is thus able to generate buildings of different sizes using a given ABM 328.

In the building model, repetitive elements such as doors and windows comprise complex syntax which makes the code for the ABM 328 lengthy. To manage this, separate JSON scripts are created for these elements and imported into the building model using an "Object Importer Generator". Object files are created using hard-coded values in their syntax to specify the precise dimensions of a certain object. Elemental Materials are used by these object files to apply various visual textures to them.

The abstract building model dataset 324 includes a plurality of global constants that are used to aid mathematical expressions. For example, the heights of doors, floor-to-floor height, the width of a staircase, or the like. These are standard static values which are to be utilised by the model designer where necessary. To avoid the repetitive use of said values, any dimensional specification is determined to be a plurality global constant. This enhances the maintainability of the model files by avoiding the scattering of static values across them.

After generating the final geometrical components of the building, it is necessary to assign appropriate textures to make the building representation look realistic in the 3-Dimensional Output. To achieve this, the engine dataset 228 includes a texture dataset 320 that is used to map textures for all materials used in constructing the building model. The texture dataset 320 stores listing of every material from the material master dataset 308 or the list of materials, including materials used in the object file, in the texture dataset 320 and assigning them the corresponding texture image name.

Referring to FIGS 1 to 4, the intelligent engine 216 of the system 100 is described hereinafter. The intelligent engine 216 includes a climatic zone selector module 404, a typology mapper module 408, a material permutation generator module 416, a reactor module 420, a structural generator module 412, an object importer generator module 428 and an estimator module 424. The climatic zone selector module 404 helps select building models and materials that are best suited to the prevalent geo-climatic conditions at the intended location of the construction site. The climatic zone selector module 404 entails three steps to determine the correct geo-climatic conditions of the site. In an initial step, the climatic zone selector module 404 establishes an effective data structure that tracks the geo-climatic conditions across various territories. In the next step, the precise geo-coordinate of the construction site is determined by the climatic zone selector module 404. In a further step, the geo -coordinate of the site is used to determine the corresponding territory and its geo-climatic classification.

In a further step, one of the two approaches to determine the precise geocoordinates of the site are such that in a first approach, a user inputs a postal address of the intended construction site. The climatic zone selector module 404 invokes Geocoding APIs to resolve this postal address to a specific geo -coordinate (latitude and longitude). In another approach, the user 101 physically stands at the desired location (site) and activates a GPS enabled electronic device 104. This captures the geo-coordinates of that site. A GPS -integrated computing device such as an electronic device 104 or an embedded GPS tracking module may be used for the same.

In a further step, upon determining the geo-coordinate of the intended site, the intelligent engine 216 performs a lookup in the master data of geo-climatic zones to determine the bounding box within which that site exists. The result of this lookup is the geo-climatic zone of that location. The typology mapper module 408 in the intelligent engine 216 determines the typology best suited for a building and further selects an appropriate ABM 328 from the abstract building model dataset 324 based on several parameters provided by the user 101. Based on this lookup, the typology mapper module 408 in the intelligent engine 216 selects an appropriate, material master data 308, and material rules dataset 312 for each use case.

The intelligent engine 216 has an extensive dataset of building typologies 324 represented by abstract building model dataset 324. Each typology is assigned a unique typology-code and, in essence, represents that building’s design, architectural form, and structural pattern. For instance, a building with a ground floor and two rooms, bearing a specific layout, constitutes one typology.

Another building with two floors, five rooms, a staircase, and bearing a different layout constitutes a second typology. Each typology is suited to a specific geo-climatic zone, local construction practices, and to satisfy specific user requirements. Upon receiving the user preferences and determining the geo- climatic zone of the construction site, the intelligent engine 216 performs a lookup in its typology dataset 304 to determine the most suitable typology for that building.

The material permutation generator module 416 in the intelligent engine 216 is responsible to produce all the valid material selections that could be potentially applied to the ABM 328. For a given feature of the building, multiple legitimate material permutations exist. Every resulting material permutation indicates a “possibility” of how that feature could be constructed in real life. The material permutation generator module 416 hereinafter referred to as ‘MPG’ module 416 uses the material rules dataset 312 in order to produce all valid material permutations for a given feature.

Accordingly, the MPG module 416 extracts a single feature DAG from the Master DAG i.e., a subset DAG representing only the sub-features under that feature contained in the material rules dataset 312. The MPG module 416 then performs a Depth First Traversal (DFT) of that Feature-DAG to produce multiple valid permutations of materials for that Feature.

For a given feature of the building, multiple legitimate material permutations exist. Every resulting material permutation indicates a “possibility” of how that feature is constructed in real life. The MPG module 416 produces all legitimate material permutations from a given input DAG. Further, the MPG module 416 extracts a single feature-DAG from the master DAG i.e., a subset DAG representing only the sub-features under that feature. The MPG module 416 then performs a depth-first traversal (DFT) of that feature-DAG starting from the root traversing towards the leaf nodes.

In context of the present invention, the reactor module 420 is the very core of the intelligent engine 216. Its primary role is to generate a feature mutation for a given abstract feature of the building. Every feature in the ABM 328 is initially an abstract feature i.e., without actual numerical values specified for the geometries of all components in that feature. An abstract feature is thus a heavily parameterized representation of that feature, wherein, dimensions of most components in that feature are represented as objects that include algebraic mathematical expressions, computations, relationship between parameters etc.

When specific material attributes, material dimensions, and user input parameters are applied onto this abstract feature by the reactor module 420, it results in a fully realized instance of that abstract feature, hereinafter, termed as one feature mutation. As there are multiple valid material permutations for a given feature, multiple such feature mutations may be generated for each abstract feature in the ABM 328.

The reactor module 420 is invoked with one abstract feature as input along with catalyst variables. In accordance with the present invention, the catalyst variables primarily represent an aggregation of user parameter values received as an input 102, a specific permutation of materials produced by the MPG module 416, and the specific attributes and dimensions of those materials from the material master dataset 308. The reactor module 420 performs a DFT of the abstract feature, visiting every descendent node i.e., sub-feature, structure, and component in that feature, attempting to “process” that node.

The reactor module 420 resolves expressions such that at every node, the reactor module 420 attempts to evaluate the arithmetic expression representing the node geometry. To resolve any variables encountered in an arithmetic expression, the reactor module 420 searches within the given catalyst variables. The original arithmetic expression is replaced with its evaluated value. In accordance with the present invention, the intelligent engine 216 thus converts an abstract geometry into a realized geometry. At the end of this process, an abstract feature is thus converted into a feature mutation.

Further, the reactor module 420 resolves material attributes and analyses the abstract material reference at a given node, determines the specific material to be associated with this node, and injects the attributes of this specific material. In accordance with the present invention, the intelligent engine 216 thus converts an abstract material reference into a realized material reference.

Further, the reactor module 420 evaluates structural conditions when the reactor encounters a conditional-structure in the ABM 328, it tests if the said condition holds-true. Essentially, the reactor module 420 assesses if a certain catalyst variable has a true value. It will then conditionally include or exclude this particular structure in the feature mutation.

Further, the building model contains various structural components with complex geometries. For example, staircases, sloping roofs etc. deriving these geometries requires several objects including non-trivial mathematical calculations that are quite complex to express in the ABM 328 directly. To overcome this, the structural generator module 412 is used. In accordance with the present invention, the structural generator module 412 is used for structures that have complex geometries.

The reactor module 420 invokes a structural generator module 412 when a reactor encounters a reference to a specific structural generator module 412 in the ABM 328. Here, the reactor module 420 invokes the structural generator module 412, receives the output from the structural generator module 412 i.e., a list of components, and augments those components under that structure in the ABM 328, thus enriching / expanding the ABM 328. Generators are thus used as plugins in the building models, where components are automatically generated or imported, and inserted into the ABM 328. Currently, generators are used for two main purposes: firstly for importing object files into the ABM 328 using the object import generator module 428. Secondly, dynamically generating components under a feature structural component generator module 412.

The object importer generator module 428 imports object geometries that are formulated in separate files, into the ABM 328. The structural generator module 412 in the system follows a plug-in architecture, which includes a standard interface between the structural component generator module 412 and the reactor module 420. This decoupling approach enables the system to add new generators without making any major changes to the existing system. As a result, the system 100 may evolve incrementally in terms of creativity and complexity.

Additionally, the structural generator module 412 is designed to produce structural components of varying dimensions. The structural generator module 412 receives catalyst variables as input. These catalyst variables are used by the structural generator module 412 to dynamically produce components based on the values of those variables. This makes the generated structures elastic and dynamic by nature. In accordance with the present invention, the estimator module 424 is designed to compute an accurate and high-resolution cost specification for the building. The intelligent engine 216 takes a bottom-up approach to determine the precise cost of the building. The process commences by systematically analysing and computing the cost of each individual component in the abstract building model dataset 324. These component-wise costs are further aggregated to determine the costs of higher-order nodes in the building, such as, the cost of each structure, subfeature, and feature.

For instance, the cost of a structure is an aggregation of the cost of all components which constitute that structure. Finally, the cost of all features is aggregated to determine the total cost of the building. Such a bottom-up approach results in a costing analysis at a very high resolution, instead of a rough approximation, thus providing users 101 with a high degree of confidence in the specifications and cost estimates generated by the intelligent engine 216.

The material master dataset 308 includes costing attributes such as the material cost and the labour cost for each material used by the intelligent engine 216. These cost values in the material master dataset 308 are derived from the prevalent market prices for that material, the level of skill and expertise required to construct that component, and the prevalent labour-market dynamics in a particular geographic area where the building is to be constructed. This information is first captured in the architectural dataset 224 based on extensive research, and further converted into the engine dataset 228 for use by the intelligent engine 216. The cost of every node i.e., a component, structure, sub-feature, or feature in the building is computed based on the costing strategy specified for that node in the abstract building model 324. The present invention supports three costing strategies, which are explained hereinafter. A first costing strategy includes the cost of certain components that is determined based on specific dimensions of that component, such as its length, surface area, or cubic volume. For instance, the cost of a wall is based on the surface area of that wall.

In this case, the abstract building model 324 specifies which dimension i.e., the length, area, or volume is to be used for purposes of costing that component. Further, the material master dataset 308 includes the material cost-per-unit and the labour cost-per-unit for that material. The estimator module 424 multiplies the dimension of the component with the cost-per-unit value of the material used in that component in order to determine the actual material and labour cost for that component.

The estimator module 424 includes a second costing strategy. In certain cases, components have a predetermined fixed cost. For instance, a standard-sized door made of a certain material, has a fixed cost for procurement in the market. In this case, the material master dataset 308 specifies a fixed cost for the material, which is directly used by the estimator module 424 to determine the cost of that component.

The estimator module 424 includes a third costing strategy. In certain cases, the cost of a system or a structure in the building is determined based on the aggregate area of the plinth of the building or an area corresponding to a particular space in the building. For example, the cost of the electrical system in a building is estimated based on the plinth area of that building, without consideration of every individual component involved in that electrical system. This strategy leads to a simplification in the costing approach for a complex or distributed feature within the building.

In this case, the material master dataset 308 specifies an abstract material with a material cost-per-unit and a labour cost-per-unit associated with the same. Further, the ABM 328 specifies an aggregate area for a structure, sub-feature, or a feature in the building. The estimator module 424 multiplies the aggregate area with the cost-per-unit value of the corresponding abstract material to determine the actual material and labour cost for that structure, sub-feature, or feature.

In the present invention, the estimator module 424 is also responsible to determine the ‘desired cost apportionment’ for every feature in the building. When processing each feature, the controller 220 invokes the estimator module 424 in order to compute the ‘desired cost apportionment ’ for that feature. In this case, the estimator module 424 analyses the percent budget allocation as specified for that feature in the abstract building model dataset 324 and the total budget indicated by the user 101 in order to compute the ‘desired cost apportionment ’ for that feature.

Further, the estimator module 424 also assists the intelligent engine 216 to select an optimal feature mutation for every feature in the building. An optimal feature mutation is a specific mutation of that feature, where the actual cost of that mutation comes closest to the ‘desired cost apportionment ’ for that feature. The cost of every feature in the building is determined by the cost of the various materials used in the construction of that feature. As there may be multiple material permutations available to construct a particular feature, resulting in various mutations of that feature, each such mutation bears a different cost.

For every feature mutation generated by the reactor module 420, the controller 220 invokes the estimator module 424 to determine the aggregate cost of that mutation. Further, the estimator module 424 compares the cost of this feature mutation with the costs of other possible mutations of this feature to eventually select the most optimal mutation for this feature.

After all the features in the building have been processed by the reactor module 420, the controller 220 also invokes the estimator module 424 in order to compute the aggregate cost of the building. In the present invention, the rendering engine is a sub module within the output module 212.

Now referring to FIGS 1 to 4A, the output module 212 is described hereinafter in accordance with the present invention. The output module 212 includes a 3D rendering engine module 448, a 2D rendering engine module 452, a costing sheet generator module 456, a bill of materials generator module 460 and an energy efficiency evaluator module 464. The Rendering Engine is responsible for generating a photorealistic 3 -Dimensional rendering of the building model and the 2-Dimensional design representations of the building’s plans, elevations, and sections. The Rendering Engine parses and interprets this final building model, and further, uses WebGL (Web Graphics Library) or OpenGL (Open Graphics Library) technologies to render the 3 -Dimensional structure visually on the electronic devices 104. To do this, the Rendering Engine traverses the entire hierarchy of the final building model and constructs a 3 -Dimensional mesh geometry in the WebGL or OpenGL environment for every component of the building.

Further, every material incorporated in the final building model is associated with a corresponding texture file in the texture dataset 320. This texture file is a photorealistic image representation of the material itself. The rendering engine performs a lookup in the texture dataset 320 to determine the corresponding texture file to be applied to every component in the final building model. Further, the rendering engine applies the corresponding texture on top of the 3-Dimensional mesh geometry of every component. The result is a photorealistic 3 -Dimensional representation of the entire building.

The electronic devices 104 provides interactive capabilities for the user 101, such as the ability to select, zoom, or pan in the rendered 3 -Dimensional output. This allows the user 101 to explore and understand the output generated by the system 100 much more intuitively. Every ABM 328 in the abstract building model dataset 324 also includes a specification for the various orthographic projection planes to be used for the purposes of generating a 2-Dimensional render of that model. Further, the texture dataset 320 also includes the specification for the various representation styles to be applied to every type of component in the building, when generating the 2-Dimensional rendering. To generate a 2-Dimensional view, the Rendering Engine first parses and interprets the final building model, and further, constructs the 3 -Dimensional mesh geometry for every component in the building using the WebGL (Web Graphics Library) or OpenGL (Open Graphics Library). It then applies the representation style for every component in the model based on the component type. Further, it performs an orthographic projection of this 3 -Dimensional model onto the 2- Dimensional plane.

The 2-Dimensional plane thus captures either the plan-view, sectionalview, or elevation-view of the 3-Dimensional model based on the specific orientation of that projection plane. This 2-Dimensional projection image is then displayed to the user on the electronic device 104. The electronic device 104 provides interactive capabilities for the user 101, such as the ability to select, zoom, or pan in the rendered 2-Dimensional output.

On selecting any individual component in the 2-Dimensional output, the Rendering Engine displays additional information about that specific component such as, the type of component i.e., wall, window, steps, slab, the exact dimensions of that component, and the specific material of that component. In addition, it also presents the room names and internal spatial dimensions of every space/room of the desired building. This allows the user 101 to understand the output generated by the system 100 in a greater detail.

In the present invention, it is necessary to provide the user 101 with a cost breakdown of their desired building in addition to the customized design. These costing details are presented to the user 101 in two tabular formats firstly the costing sheet and secondly the BOM. They are generated by the estimator module 424 and the bill of materials generator module 460 respectively, which are a part of the output module 212.

The costing sheet is a detailed sub-feature wise breakdown of costs and quantities calculated for every feature of the building. These costing details include the material and labour costs in correlation to their quantities expressed individually for each sub-feature along with the composite and sub-material used to construct them. The unit of measure for this output may be changed to either the imperial or the metric scale, to suit the user’s 101 preference. The total cost of construction of the building, which is essentially a summation of all feature costs, is shown at the end of the costing sheet.

The controller 220 feeds the final building model from the intelligent engine 216 into the estimator module 424. This final building model includes information about the materials and costs associated with every individual component in the building. The costing sheet generation module 456 analyses this final building model to compute the aggregate cost for each feature in sequence. While doing so, it also aggregates the quantities and the costs corresponding to every composite material used in that feature. The resultant is the final costing sheet associated with this building model.

In context of the present invention, the BOM is a table that gives a featurewise list of all sub materials to be used during the construction process. This list includes the quantity, the per-unit cost based on the prevalent market rate, and the total cost of each sub-material. This assists the user to procure the material from the market as the construction process progresses.

The controller 220 feeds the finalized costing sheet from the costing sheet generator module 456 into the bill of materials generator module 460. The bill of materials generator module 460 analyses and processes every line item in the costing sheet for each feature in sequence. Here, it determines the sub-materials associated with every composite material in the costing sheet, by performing a lookup in the material master dataset 308. This lookup also provides information about the respective fractional proportionality of a sub-material within a given composite material.

This information is used by the bill of materials generator module 460 to determine the exact quantity of the sub-material required in each feature. Further, the bill of materials generator module 460 looks up the per-unit costs of each submaterial in the material master dataset 308 and computes the total cost of each submaterial required in every feature. All the computed values are organized into a tabular result which is the final BOM of the building.

In the present invention, the energy efficiency evaluator (EEE) module 464 generates the EEL The EEI is an index that represents the carbon impact on our planet caused by the construction of a particular building designed using the system 100. The EEE module 464 calculates the energy impact based on two factors: firstly, the embodied energy encumbered during the manufacturing of a particular material and secondly, the carbon impact caused due to the use of a particular material in the building design through the lifecycle of that building. These emission values are colour coded ranging from red to green to express how environmentally friendly the use of a particular material is. This helps the user 101 to evaluate the carbon impact of the construction of their desired building and may help them opt for environmentally friendly alternative materials during the construction process of this building.

The controller 220 feeds the finalized costing sheet from the costing sheet generator module 456 and the BOM from the bill of materials generator module 460 into the EEE Module 464. The EEE module 464 analyses the quantities of every material and calculates the carbon impact for each one of them. The results of this computation are aggregated into a tabular format, which is the EEI of the building.

Now referring to FIGS. 1 - 5B, the operational flow of the system 100 is described hereinafter. In operation, in an initial step 502, the system 100 is initialized. In the next step 504, the users 101 enters the preferred user parameters and inputs 102. In the next step 506, the controller 220 determines the exact geolocation of the construction site by geocoding the construction address provided by the user in the input 102. In the next step 508, the exact geo-climatic zone of the building is determined with the help of the climatic zone selector module 404, which performs a lookup in the climatic zone dataset 316. In the next step 510, the controller 220 determines a suitable building typology based on the user input parameters 101 and the geo-climatic zone from step 508. In the next step 512, a suitable material rules dataset 312 and the suitable material master dataset 308 is selected by the controller 220, based on the typology finalized previously in step 510. In the next step 514, the controller 220 selects a suitable ABM 328, from the abstract building model dataset 324, based on the building typology finalized previously in step 501.

In a further step 516, the controller 220 reads the relevant material compatibility rules and material attributes from the material rules dataset 312 and from the material master dataset 308 chosen previously in steps 512 and 514. In the next step 518, the controller 220 reads the selected ABM 328 from the abstract building model dataset 324. In the next step 520, the controller 220 determines the sequential feature list of the proposed building by analysing ABM 328 and the material rules dataset 312.

In subsequent steps from 522 to 544, the controller 220 processes each feature of the ABM 328 in the sequence generated by step 520. In step 522, the controller 220 selects the first / next feature to be processed from the ABM 328. In a further step 524, the expected feature budget is determined based on the configured percentage budget allocation for that feature. In the next step 526, the material permutation generator module 416 generates all possible, valid, material permutations for the feature mentioned in step 522. In the next step 528, one of those material permutations is chosen for further processing. In the next step 530, the attributes of the materials for the chosen material permutation are extracted from the material master dataset 308. Further, these material attributes are combined with the user parameters to generate a set of catalyst variables. In the next step 532, the reactor module 420 injects these catalyst variables into the Abstract Feature Model which triggers the process of creating a mutation for the current feature. In this step 532, the reactor module 420 also triggers the structural generator module 412 to produce complex geometric structures where necessary.

In step 534, one feature mutation of the current feature is produced for the material permutation that was chosen in step 528. In a further step 536, the budget of the feature mutation from step 534 is computed. Further, in step 538 if the computed budget of the feature mutation is optimally close to the expected budget of that feature from step 524, then the current feature mutation is saved as an optimal mutation. Further, in step 540, the process checks if there are additional material permutations available for the current feature.

If so, it repeats the process from step 528 to step 540 until an optimal feature mutation is identified to fit within the user’s expected budget. In a further step 542, this optimal feature mutation is incorporated into the output building model. Further, in step 544, the process checks if there are any additional features in the ABM 328 yet to be processed. If so, it repeats the process from step 522 through 544, until all the features of the ABM 328 are processed and are incorporated into the output building model. Subsequent steps in the process focus on the generation of the final output 103 for the user 101. In step 546, the controller 220 analyses the output building model to generate the output costing sheet, which includes the material and labour costs to construct each feature and sub-feature of the building. In the next step 548, the controller 220 analyses the output building model to generate a detailed BOM for each feature. In a further step 550, the controller 220 optimizes the output building model to generate a final building model. Further, this final building model is rendered as an interactive 3D and 2D model on the electronic device 104.

Referring to FIGS. 6 - 6A the representation of the system 100 in accordance with the present invention is described. The system shows a first panel 605, a second panel 610, a third panel 615, a fourth panel 620, a fifth panel 625 and a sixth panel 630. The first panel 605 shows the parameters entered by the user 101. In the second panel 610 rendered view of the three-dimensional house is shown. In the third panel 615 a blueprint of the house is shown. In the fourth panel 620 and the fifth panel 625, the estimate of the materials that will be required for constructing the house is shown. In the sixth panel 630, the material cost is shown.

The embodiments were chosen and described in order to best explain the principles of the present invention and its practical application, to thereby enable others, skilled in the art to best utilize the present invention and various embodiments with various modifications as are suited to the particular use contemplated. It is understood that various omissions and substitutions of equivalents are contemplated as circumstance may suggest or render expedient, but such are intended to cover the application or implementation without departing from the scope of the present invention.