In the field of transportation, data analysis is probably the most important and widely used research tool available. In the data analysis universe, there are two ‘schools of thought’; the first uses statistics as the tool of choice, while the second – one of the many methods from – Computational Intelligence. Although the goal of both approaches is the same, the two have kept each other at arm’s length. Researchers frequently fail to communicate and even understand each other’s work. In this paper, we discuss differences and similarities between these two approaches, we review relevant literature and attempt to provide a set of insights for selecting the appropriate approach.

Statistical methods versus neural networks in transportation research: Differences, similarities and some insights

The first journal article on neural network application in civil/structural engineering was published by in this journal in 1989. This article reviews neural network articles published in archival research journals since then. The emphasis of the review is on the two fields of structural engineering and construction engineering and management. Neural networks articles published in other civil engineering areas are also reviewed, including environmental and water resources engineering, traffic engineering, highway engineering, and geotechnical engineering. The great majority of civil engineering applications of neural networks are based on the simple backpropagation algorithm. Applications of other recent, more powerful and efficient neural networks models are also reviewed. Recent works on integration of neural networks with other computing paradigms such as genetic algorithm, fuzzy logic, and wavelet to enhance the performance of neural network models are presented.

Neural Networks in Civil Engineering: 1989–2000

class, 190, 194 Activation function, 67, 162, 270 Activity-on-arrow diagram, 121 Activity-on-node diagram, 121, 134, 171 Adaptive conjugate gradient learning algorithm, 37, 267 Adding new task, CONSCOM, 245 Application programming interface (API), 195 Architecture, engineering, construction (AEC), 183, 184 Architecture, see Software architecture Attribute, 232, see Project attributes Backpropagation (BP) algorithm, 8, 29, 37, 267 Bid, 39, 178 Buffer constraints, 119, 123, 128, 150, 172, 183, 220, 240, 253 Distance buffer, 119, 124, 150, 253 Time buffer, 119, 124, 151, 172 Building construction example, 134 Change order, 2, 5, 97, 111, 176, 178, 217, 224, 259 After bid, 178 Before bid, 179 Causes of, 178 Change order management, 108, 111, 178, see Change order Example, 254 Interaction diagram, 227 Model, 224 Owner’s perspective, 181 Use case diagram, 225 Classes, 187, 192, 201, 206, 229 Abstract, 190, 194 Concrete, 190, 203, 208, 213, 129, 224 Components, 185–188, 190, 191, 199 Computer integrated construction (CIC), 183 Concrete class, 190, 203, 208, 213, 129, 224 Cold-formed steel design optimization, 46, 47 Formulation, 47 Global optimum design, 79 Examples, 69–78 Neural dynamics model for, 64 Concurrent and collaborative engineering, 1, 97, 102, 183 CONSCOM, 4, 201, 205, 239 Adding a task, 245 Architecture, 201 Example, 254 Features, 239 Framework, 205 Integrated management environment, 241 Modifying a task, 243, 245 Object model, 208 User interface, 185, 194, 200, 208, 241, 244 Constraint function, 46, 62, 66, 156, 161 Constraint layer, 66–68 Constraints Buffer, 119, 123, 128, 150, 172, 183, 220, 240, 253 Equality, 156, 159, 161, 233, 253 Inequality, 60, 63, 117, 156, 161, 235, 253 Logic, 112, 117 Resource Scheduling Construction scheduling, see Scheduling Contractor, 2, 3, 39, 95–97, 102–104, 143, 177, 181, 184, 224, 227, 229, 243, 259 Cost, see Project cost Cost estimation, 5, 10, 106, 108, 231, 261 Cost optimization, 1, 110, 139, 155, 185 Cost-duration relationship, 142, 143, 165, 175, 183, 244, 254 Discrete, 142, 144, 230 Linear, 144, 183, 240, 245 Nonlinear, 142, 144, 169, 183, 240, 245 CPM, 2, 4, 126, 139, 141, 142, 146, 176, 183, 237–240, 244

Construction Scheduling, Cost Optimization and Management

A fuzzy system for concrete bridge damage diagnosis

Manipulation of perceptions is a remarkable human capability in a wide variety of physical and mental tasks under fuzzy or uncertain surroundings. Possibilistic reasoning can be treated as a mechanism that mimics human inference mechanisms with uncertain information. Petri nets are a graphical and mathematical modeling tool with powerful modeling and analytical ability. The focus of this paper is on the integration of Petri nets with possibilistic reasoning to reap the benefits of both formalisms. This integration leads to a possibilistic Petri nets model (PPN) with the following features. A possibilistic token carries information to describe an object and its corresponding possibility and necessity measures. Possibilistic transitions are classified into four types: inference transitions, duplication transitions, aggregation transitions, and aggregation-duplication transitions. A reasoning algorithm, based on possibilistic Petri nets, is also presented to improve the efficiency of possibilistic reasoning and an example related to diagnosis of cracks in reinforced concrete structures is used to illustrate the proposed approach.

Modeling uncertainty reasoning with possibilistic Petri nets

An efficient knowledge-acquisition support method is required for improvement and maintenance of the knowledge base in durability evaluation of an RC bridge deck. Such a method is proposed in this paper to automatically acquire fuzzy production rules. This method makes joint use of genetic algorithms and a neural network. Using a neural network as a subsystem, the evaluation function of genetic algorithms can be provided with the weights of the neural network. Introducing a neural network into genetic algorithms, it is possible to acquire new knowledge so that the method is useful when it is difficult to acquire knowledge in the field.

A Fuzzy Expert System for Damage Assessment Using Genetic Algorithms and Neural Networks

鉄筋コンクリート床版の耐用性評価に関する知識の増加, 知識ベースの拡大につれ, ルールベースの改良, 保守のための効率的な知識獲得支援手法が必要となってきている. そこで, 本研究では, 遺伝的アルゴリズムとニューラルネットワークを用いることにより, ファジィ推論ルールを自動的に獲得する手法を提案する. サブシステムとしてニューラルネットワークを用いることにより, そのネットワークの重みを遺伝的アルゴリズムの評価関数として用いることができる. ニューラルネットワークを遺伝的アルゴリズムに導入することによって, 新たな知識の獲得を行うことができ, 知識ベースの構築が困難であった対象領域に有効であると思われる.

https://www.jstage.jst.go.jp/article/jscej1984/1996/537/1996_537_145/_pdf

A knowledge acquisition support method in durability evaluation of rc bridge deck using genetic algorithms and neural network

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A Fuzzy Expert System for Damage Assessment Using Genetic Algorithms and Neural Networks

A knowledge acquisition support method in durability evaluation of rc bridge deck using genetic algorithms and neural network