Can the building industry jump from the Flintstones to Jetsons?

Artificial Intelligence and it's Place in Construction Related to 4D and 5D

CPM schedulers, planners, oversight wizards, and Clueless Emperor, CFOs and public capital construction directors, have been under servicing the building industry to alarming levels of incompetence ever since a time before most of us can recall. By many measures, less than one-quarter of all work set in place in a given year meets budget or targeted deadline. Add to this torpor negative productivity rates, and you have the perfect breeding ground for disruption, and that disruption could be artificial intelligence.

Along Comes Integrated BIM e.g. iTWO 4.0

Recent developments in BIM platforms – specifically, 4D, 5D, 6D, and 7D are already poised for disrupting the project controls industry by displacing estimators and schedulers from the planning, R&D, D&D, and critical oversight processes. Let it be said, that if less than 10% of all work in place was “BIM driven” – a loosely defined term, that it cannot be considered disruptive, but strongly emphasises the difference between BIM and Integrated BIM. Tracking true AI rates of adoption will be equally elusive. When BIM is used integrated, and as such properly, projects can realize up to 25% savings on their bottom line, and finish with float to spare: 16%  of users reported improved schedule. S

4D, 5D, 6D, and 7D are nascent platforms, that will shape up as the BIM models they are based on become more sophisticated. Indeed, cost-loaded CPM schedule animated models are invariably inaccurate because they are not properly plotted based on resources and productivity rates, only estimated costs or durations, mostly from cost guide books, which are only able to issue average duration. These durations are plugged into 4-5D BIM interfaces to output simple renderings of time over the CAD/model layers. The shift to Integrated BIM (e.g. iTWO 4.0) alliviated this by default. Furthermore, Artificial Intelligence enables project controls will be gradual, as the technology finds its bearings through adoption, and continued advancement and investment in the platforms.

Design standardizations would mean that Artificial Intelligence could

• Maintain design libraries that were easier to manage and access on a global scale
• Optimize algorithms into design criteria for entire groups of structures over a Wide Area Networks
• Develop 4D and 5D BIM models from which to draw estimates and CPM/LOB schedules
• Make global program BIM changes and regenerated models
• Deliver benchmarks and performance metrics from a master database
• Launch 6D sustainability and 7D BIM lifecycle models

At what point does Artificial Intelligence adoption become ‘disruptive’?

Before AI can do its best work, it needs a basis of analysis. If a project is fairly unique and customized, AI won’t have a knowledge-base to draw from. The challenge is “abstracting design goals and parameters into quantifiable representations" is the first level of abstraction that design optimization demands from users.

As AI gradually takes over more of the intuitive and cognitive processes, e.g. most CPM schedulers can be phased out, and their duties delegated to clerical level data-gathering personnel. I daresay most CPM schedulers will become obsolete in ten years time, save for those who have prized knowledge in highly specific and customized work, such as chemical, processing, manufacturing, processing, nuclear, hospitals and airports, that don’t lend themselves well to standardization or machine learning.

AI disruption in any industry is predicated on the success of the Genrative Processs

• AE loads design parameters, materials, into a generative design platform
• AI BIM uses algorithms and reasoning to generate design options, as well as performance analysis for each of the designs
• AE modifies goals and constraints for AI to come up with relevant solutions
• AI learns algorithms and creates MR immersion options, and finally, 3D printed models to finalize the design process

AI will be able to design, estimate cost, and generate a CPM cost-loaded model for mundane projects, such as big-box retailers, factories, plants, schools, dormitories, housing, prisons, and warehouses, because it can draw data from a vast library of typical or similar structures that can easily be standardized, in the cloud.

AI 5D BIM Automation

The failure of BIM to mainstream begs the question as to whether to blame the operators, the platforms, or both. The answer isn’t simple, but the solution lies in AI automated and semi-automated platforms, that will overtake current BIM platforms. AI 4D and 5D BIM will be markedly different than what’s available now, with a level of exactitude heretofore unachievable.

The degree of achievable automation is contingent on several criteria with semi-automated as the alternative:

1. Degree of design standardization: many mundane structures can be AI designed in toto, or in part. BIM 4D CPM scheduling requires the 5D platform of cost, in order to calculate variable costs and rates of productivity. 
2. Available resources: optimized algorithms for particular program as they become available, that AI needs to populate its database with.
3. Degree of customizations. For the sake of efficiency and economy, unnecessary customizations to mundane structures can be standardized, or based on a limited number of models that AI can render.
4. Ability of AEC industries to facilitate the integration of optimized algorithms into developmental level models

Optimizing Risk Positions

A point-cloud map will look familiar to risk-assessors (RAs), as it will to any point-cloud modeler. RAs model risk with their Monte Carlo simulations using CPM schedules and risk registers; however, these schedules are invariably flawed, as they are not based on constrained resources and productivity rates. Yet RAs are vastly underutilized in the building industry, even for the largest capital projects lead by our most Clueless Emperors. Thus, the large majority of building projects are risk-managed exclusively from CPM timelines, i.e., sans risk assessment. If that were not so, it would be easier to model time, cost, and quality aspects directly from BIM.

Projects should be risk-managed, but the folks calling the shots often think a Risk management is a board game strategy. This means the folks invited to the risk-register meeting need to be the ones providing the input. It follows, that best practice dictates that the CPM schedule be created in tandem with the risk assessment, which was derived from a BIM model that was based on available resources and real productivity rates.

The introduction of a risk-optimized CPM baseline into the project BIM master model is a necessary step toward risk-mitigation. The data generated today from AE BIM efforts for the 4D BIM will invariably be poorly suited to risk modeling without critical input from the risk and project critical path resources. Rather, the risk-optimize baseline can either be created in tandem with design optimizations, or it can be backed into the BIM model post-design completion; however, this latter defeats the purpose of having risk-optimized foreknowledge in the planning phase.

Backtracking from 5D, to 4D, to BIM

Before we can discuss AI CPM scheduling, we must revert to the 3D BIM model that generates the parameters that will yield the data to build the schedule, and estimate the cost. Without these, AI has no data set to draw from. These models are by and large owned and used by AEs, who rarely integrate 4D or 5D BIM, and seldom partner with the project controls team in the way they need to.

Generous data sets for construction schedules are extant in cloud databases, categorized by project type, and benchmarked according to performance and logic integrity. This data could be part of the knowledgebase that AI BIM will use and reuse to design and schedule similar projects.

It is from this set of data that AI will derive variables in sequences of operations for complex activities based on nature, volume, and rate of production it finds there. For example, 5,000’ of copper water piping in an assisted living project would be done in a fraction of the time as the same amount of pipe in a medical laboratory based on factoring from as-built schedules and standardized models. From mean values degrees of difficulty can be weighted from less or more than average, and assigned a multiplier.

Once parameters and optimizations have been set, AI 5D BIM can compare multitudes of designs simultaneously. This potential represents exponentially higher build-model times that can significantly reduce design and development stages, while offering the maximum range of optimized choices. D&D program changes will be made on demand, in lieu of tedious and costly redesign work ups that drain the design budget.

The future jobs?

As such, jobs and functions will disapear as rutementary work, mundane tasks and tacit knowledge is captured by AI. However, a new role shall emerge - the "Starchitect" who drives data farming, fosters supervised learning, who understands process integration with technology, but also the tectile qualities that the projekcts ultimately are catering for. The Starchitect will be the bridge builder between quanitities of data and realised value, soft and hard, and freedom and rigour.