Building Information Modeling

Automated Code Checking and Compliance Processes

Nawari O. Nawari

• Publisher: CRC Press
• Year: 2018
• ISBN:
  9781351200974
• Edition: 1
• Topic: Technology & Engineering
• Subtopic: Construction & Architectural Engineering
• Index: Technology & Engineering

"Many researchers and software developers have put a lot of effort into finding solutions for automated code checking. This book is a good summary of these efforts and provides readers with a comprehensive understanding of the status of such technologies in the industry. It also guides readers on implementation of such techniques using the platforms and tools currently available in the industry."
— Issa Ramaji, University of North Florida, USA

Building Information Modeling: Automated Code Checking and Compliance Processes covers current and emerging trends in automating the processes of examining building design against codes and standards of practice. The role of Building Information Modeling (BIM) technologies in these processes is thoroughly analyzed and explains how this new technology is significantly transforming modern architecture, engineering, and construction (AEC) domains. The book also introduces the theoretical background of computerizing compliance verification, including domain knowledge representations, building model representations, and automated code checking systems. An underlying goal for the material covered is to present the use of BIM technology as an integral part of the automated auditing process that can lead to a more comprehensive, intelligent, and integrated building design– a design where an optimized solution can be achieved in harmony with the current codes and standards of practice.

This new proposed BIM-based framework for automating code conformance checking is one of the most powerful methods presently available to reflect actual building code requirements, and the methods described in the book offer significant benefits to the AEC industry such as: ?

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• Providing consistency in interpretation of regulatory provisions

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• Reducing code compliance validation errors, and the cost and time associated with compliance checking

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• Allows for the ability to self-check required aspects before bidding

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• Reduces the amount of time and resources required during design review

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• Allows for optimal design, along with faster turnaround on feedback, and potentially faster approvals for construction permits by building and infrastructure authorities

1	Introduction

General

The advancement of technology has continuously engaged the design profession over the past several centuries. From the ancient Egyptians—who used models in the form of drawings and physical objects, as demonstrated in the plans of the tomb of Rameses IV and the drawing of the shrine from Ghorâb 4000 years ago—to the use of the mouse in the early 1970s, to the development of building information modeling (BIM) in the mid-1990s, technology has had a radical impact on building design and construction.

During the middle ages, models were used increasingly to verify the design and construction of cathedrals (Kostof, 1977) before building the full-scale facility. These models were an integral part of the design and decoration of building exteriors and interiors. The history of buildings finds its origins in the work of the Roman architect Vitruvius, who traced the origination of construction to the imitation or modeling of nature. He observed that, seeking shelter, humans learned lessons from swallows and bees, who built their own habitations. Then, humans started using natural materials to create forms based on shapes and proportions found in nature. An example of this is the “Vitruvian Man,” which affirms that the figure of a man could be inscribed in both the circle and the square; this fundamental technological advancement represents the geometrical forms on which the design universe was ordered.

Later on, Romans created their structures on paper and then built physical models as replicas of the intended project before full-scale construction, to verify they would stand. Bad designs fell down; good designs stood (some of which are still around today). This practice of trial and error has come a long way over the past couple of thousand years, and the construction industry continues to seek tools that improve design before construction begins (Figure 1.1). Integrating BIM with computable building codes is a major step in this evolution.

FIGURE 1.1 Evolution of building design tools and technology.

Recently, the construction industry has started to engage information technologies more effectively to incorporate its design, construction, and operational processes. However, there are still manual processes, the duplication of business functions, and the sustained dependence on paper-based information management to record and exchange data among project participants, as well as the review and verification of compliance with regulations.

In efforts to resolve such issues, engineering ontology was introduced in the late nineties as a process that focuses on cognition-related activities to facilitate the creation, capture, exchange, conversion, and use of knowledge, with the ultimate goal of leveraging automation in engineering design and analysis decisions to achieve optimum solutions. In building design, there has been much research in the area of design knowledge reuse. In the past few decades, limited research efforts have focused on handling the complexity of building code provisions and integrating different digital design tools to achieve automation. Most of the earlier research works were focused on the area of design knowledge reuse in building design, with most of the approaches previously tested originating from artificial intelligence (AI). In the seventies and eighties, a great number of rule-based expert systems were developed for the purpose of knowledge reprocessing. However, these systems were not entirely successful because of the difficulties in producing a formal representation of the information and keeping up with the frequent updating of knowledge. The knowledge first had to be acquired from building regulations and experienced designers and then generalized and transformed into rules. Although many of these systems are useful in solving the specific problem they are intended for, they are rarely used in practice.

The recent challenging global economic downturn accompanied by the continued growth in the complexity of building regulations and standards in a fragmented construction industry make the designing and delivering of a facility that meets the owner’s objectives within budget and schedule a cumbersome task. The notion of computerization within the context of facility design in the twenty-first century offers key solutions to optimizing the building design process. By offering more accurate information in an open and asynchronous data format, computerization provides engineers, architects, and consultants with efficient and innovative methods to collaborate, investigate a large number of design alternatives, and validate design assumptions and requirements against code specifications in a virtual environment before construction to achieve optimum design objectives.

The concept of automation in the building design systems described in this book focuses on the mechanisms of checking building regulation compliance, which are defined by the relationship among various design and engineering information management systems and BIM, and how this computerization will assist in streamlining the communication and dissemination of building design information among a breadth of stakeholders. Specifically, it represents the confluence of multilayered concepts ranging from logic theories and cybernetics to BIM.

In the architecture, engineering, and construction (AEC) industry, specifications and regulations are written by professionals to be read and applied by people. They typically take the form of written texts, tables, and equations. In general, these rules have lawful status. However, the cognitive and analytic ability of the human brain is dissimilar to anything implemented in computer systems. Thus, the automation of this process poses a real challenge to the AEC industry (Nawari and Alsaffar, 2015). For example, how can the interpretation of these rules into a computer-interpretable format be performed in a manner such that the implementation can be validated as consistent with the written regulations? Quite often, the process counts on the computer programmer’s interpretation and translation of the written rules into computer code. In other cases, the logic of the human language statements is formally interpreted and then encoded into computer instructions.

Fortunately, new developments in AI research and BIM offer practical solutions to resolve these problems. AEC building standards and regulations commonly endeavor to organize, categorize, label, and define the rules, actions, and patterns of the building environment to attain efficiency, safety against any kind of failure, and overall economy. Nevertheless, their best-laid plans are overwhelmed by inevitable change, growth, innovation, progress, evolution, diversity, and entropy (Nawari, 2012b). Quite often, regulations can amend provisions and interpretive standards, which normally leads to massive volumes of semistructured documents that amend, complement, and potentially conflict with one another. These issues, which indicate complications for both young architects and engineers as well as experienced professionals, are also far more disorderly for the fragile traditional knowledge bases in computer systems. Notwithstanding that precise definitions and specifications are essential for encoding building regulations, many building code provisions aren’t precisely defined and are often characterized by high subjectivity. Furthermore, some code provisions are characterized by continuous progressions and open-ended ranges of exceptions that make it difficult to give complete, exact definitions for any concepts that are learned through experience.

OVERVIEW OF AUTOMATED RULE-CHECKING SYSTEMS

Automated rule verification systems are generally organized into four phases. Phase one is comprised of rule interpretation and development and the logical arranging of rules for their application. In the second phase, building model data generation is performed. This phase commences checking the necessary information required. Phase three is the execution phase, which carries out the actual rule compliance verification. The last phase is the reporting of the compliance-checking results.

This issue of automating rules and regulations checking has interested many researchers and practitioners over the years. Historically, more than 2000 years ago, efforts were made to develop intelligent classification and verification systems, as depicted in Aristotle’s categories and his system of syllogisms for reasoning about the categories. These were the most highly developed systems of logic and ontology (Sowa, 2006). The syllogisms are rules of reasoning based on four sentence arrays, each of which relates one class in the subject to another category in the predicate (Nawari, 2012b). These rules are as follows:

• Universal affirmative: An example would be “Every truss is a frame.”

• Particular affirmative: An example would be: “Some trusses are space frames.”

• Universal negative: An example would be “No truss is a deep foundation.”

• Particular negative: An example would be “Some space frames are not trusses.”

In an effort to computerize the evaluation of Aristotle’s syllogisms, in 1666, Leibniz was intrigued enough to try and develop the first computable model to appraise Aristotle’s syllogisms. Leibniz analyzed the possibilities of the binary system to encode Aristotle’s syllogisms. He demonstrated the four binary fundamental operations of calculation—addition, subtraction, multiplication, and division—expressing the conviction that one day in the future, machines would use this system to compute logical rules (Figure 1.2). Leibniz (1666) stated, “The only way to rectify our reasonings is to make them as tangible as those of the Mathematicians, so that we can find our error at a glance, and when there are disputes among persons, we can simply say: Let us verify by computation, without further argument, in order to see who is correct.”

FIGURE 1.2 Leibniz’s habilitation thesis in philosophy (1666). (a) Thesis cover page: “Dissertation on the Art of Combinations” and (b) Excerpt from Leibniz’s thesis illustrating the binary system.

In regard to the construction industry, the first successful effort to automate design compliance was demonstrated by the work of Fenves (1966), when he investigated the application of decision tables to represent the American Institute of Steel Construction (AISC) standard specifications. He made the remark that decision tables, an if–then novel programming and program documentation technique, could be used to represent design standard provisions in a precise and unambiguous form. The concept was given a practical application in the 1969 AISC specification, represented as a set of interrelated decision tables. Later, other researchers tried to build on Fenves’s work, such as Lopez et al., who implemented the Standards Interface for Computer Aided Design (SICA...

Table of contents

• Cover
• Half-Title
• Title
• Copyright
• Dedication
• Contents
• Preface
• Author
• Chapter 1 Introduction
• Chapter 2 Domain Knowledge Representations
• Chapter 3 Building Information Modeling and Code Checking
• Chapter 4 Automated Rule-Based Checking Systems
• Chapter 5 Practical Approaches
• Index