SLQS-Journal Vol. 1 - Slqs-uae.org

More documents

Recommendations

Info

SLQS JOURNALJanuary 2009Application of ANN in construction ManagementSampath Suriyaarachchi BSc(QS) Hons.Sampath Suriyaarachchi is a Quantity Surveyor graduated from University of Moratuwa Sri Lanka in 2006with a second class upper division for the Honors Degree of Batchelor of Science in Quantity Surveying. Aftergraduation, he also assisted the Department of Building Economics of University of Moratuwa for neuralnetwork methodology related researches. Since 2007, he has served as a Project Quantity Surveyor for AlNaboodha Engineering LLC, Dubai, UAE.“Artificial Neural Networks (ANNs) offer an approach to computationthat is different from conventional analytic methods. ANNs are aninformation processing technology that simulates the human brainand the nervous system. Like the human brain, neural networks learnfrom experience, generalize from previous examples to new ones andabstract essential characteristics from inputs containing irrelevantdata” (Boussabaine, 1996).What is an ANN: An ANN is an information processing paradigmthat is inspired by the way biological nervous systems, such asthe brain, process information. The key element of this paradigmis the novel structure of the information processing system. It iscomposed of a large number of highly interconnected processingelements (neurones) working in unison to solve specific problems.ANNs, like people, learn by example. An ANN is configured for aspecific application, such as forecasting, pattern recognition or dataclassification, through a learning process. Learning in biologicalsystems involves adjustments to the synaptic connections that existbetween the neurones. This is true of ANNs as well.Historical background: The first artificial neuron was produced in1943 by the neurophysiologist Warren McCulloch and the logicianWalter Pits. But the technology available at that time did not allowthem to do too much. However, this field was established before theadvent of computers, and has survived at least one major setback andseveral eras.Why use neural networks: Neural networks, with their remarkableability to derive meaning from complicated or imprecise data, can beused to extract patterns and detect trends that are too complex to benoticed by either humans or other computer techniques. A trainedneural network can be thought of as an “expert” in the category ofinformation it has been given to analyze. This expert can then be usedto provide projections given new situations of interest and answer“what if” questions.Other advantages include:Adaptive learning: An ability to learn how to do tasks based on thedata given for training or initial experience.Self-Organisation: An ANN can create its own organisation orrepresentation of the information it receives during learning time.Real Time Operation: ANN computations may be carried out inparallel, and special hardware devices are being designed andmanufactured which take advantage of this capability.Fault Tolerance via Redundant Information Coding: Partial destructionof a network leads to the corresponding degradation of performance.However, some network capabilities may be retained even with majornetwork damage.Neural networks vs. conventional computers: Neural networkstake a different approach to problem solving than that of conventionalcomputers. Conventional computers use an algorithmic approachi.e. the computer follows a set of instructions in order to solve aproblem. Unless the specific steps that the computer needs to followare known the computer cannot solve the problem. That restricts theproblem solving capability of conventional computers to problemsthat we already understand and know how to solve. But computerswould be so much more useful if they could do things that we don’texactly know how to do.A trained neural network can be thoughtof as an “expert” in the category ofinformation it has been given to analyze.This expert can then be used to provideprojections on given new situations ofinterest and answer “what if” questions.Neural networks process information in a similar way the humanbrain does. The network is composed of a large number of highlyinterconnected processing elements (neurones) working in parallelto solve a specific problem. Neural networks learn by example. Theycannot be programmed to perform a specific task. The examples mustbe selected carefully otherwise useful time is wasted or at worse thenetwork might be functioning incorrectly. The disadvantage is thatbecause the network finds out how to solve the problem by itself, itsoperation can be unpredictable.On the other hand, conventional computers use a cognitive approachto problem solving; the way the problem is to solved must be knownand stated in small unambiguous instructions. These instructionsare then converted to a high level language program and then intomachine code that the computer can understand. These machines aretotally predictable; if anything goes wrong it is due to a software orhardware fault.Neural networks and conventional algorithmic computers are not26
SLQS JOURNALJanuary 2009in competition but complement each other. There are tasks that aremore suited to an algorithmic approach like arithmetic operations andtasks that are more suited to neural networks. Even more, a largenumber of tasks, require systems that use a combination of the twoapproaches (normally a conventional computer is used to supervisethe neural network) in order to perform at maximum efficiency.There are number of activities where ANN can be deployed inconstruction management as discussed below.Predicting project cash flow and costs: An ANN system can beused to provide assistance to construction contractors in predicting,updating and managing (planning and controlling) project cash flowand cost. This will assist the contractor in taking necessary managerialaction to avoid a shortage of cash, bankruptcy and to give earlywarning of cost and time overrun. The input of such a system wouldbe based on the data collected by the cost system, cost estimatingand planning.Risk analysis: An ANN can be used as a tool to assist with decisionmaking in financial investments and in assessing situations whereopportunities for alternative contractual arrangements are availableinvolving different allocations of risk and reward. In addition, it ispossible to develop an ANN system to assess risks of projectcost overruns and assist in developing contingency managementstrategies. Such a system could have sources of risk as (an) input/inputs (for example, project complexity, unrealistic estimates, poorproject specification, etc.) and a classification of risk and the range oflikely estimates as the outputs.Decision making: Many decisions take place under conditions ofuncertainty. Construction managers take decisions with incompleteknowledge or knowing that the outcomes of these decisions areuncertain. While there are a number of mathematical and statisticalmodels for assisting managers to take decisions, these models arebased on probability and regression techniques whereby the bestfit is sought. In addition, these models lack the ability to learn orgeneralize solutions from incomplete or previously unseen data. Here,the characteristics of ANNs offer a chance to match the problem’scomplexity.Resource optimization: In construction management manyproblems are encountered that require minimizing costs by optimizingthe contending factors. For example, one of the problems inconstruction is resource allocation. Many methods have been used tosolve this problem. However, none of them have the complete abilityto predict the effect of factors that affect the allocation of resources(e.g. design change, site conditions, plant conditions, etc.) and learnfrom experience. For such a complex problem, an ANN approach topredict and determine the priority ranking of resources would be of agreat benefit to construction managers and planners.Prediction of tendering outcomes: A neural network system canbe developed to assist with the deliberation of the circumstancesin which competitive or other type of bids should or should not bemade and to help contractors to predict the results of a bid beforeembarking on the costly process of estimating and costing of the bid,as a result of assessing the risk involved in a particular bid or contractaccurately.Classification and selection: The selection and classification ofconstruction material, plant, construction methods, etc., is a complexproblem and has to be based on experience and judgment. Informationand knowledge about the processes of selecting and classifying theseresources together with data about the strengths and weaknesses,in meeting operational requirements and the general priorities andpreference of industry uses of existing comparable constructiontechnologies could be used to train a probabilistic neural networksystem to assist in the selection and classification of these expensiveresources.Because of valuable characteristics, which are unavailable inconventional prediction and optimization techniques, ANNs havebeen successfully applied to a number of diverse fields of constructionmanagement. Boussabaine (1996) identified several applications ofANNs to construction management. They can be listed as follows:1. An ANN- based approach for predicting the adoption potentialor acceptability of a new construction technology2. An application of three-layered back-propagation (BP) networkto forecast the construction duration of buildings at the predesignstage3. ANN-based optimizing method for the sequencing ofconstruction tasks with the objective of minimizing productiontime4. ANNs for mark-up estimation5. An ANN model forecasting residential construction demand6. Selecting vertical formwork systems7. An ANN base model for identifying the key management factorsaffecting budget performance in a project8. A model for estimating construction cost.9. A trial neural network system for optimum mark-up estimationunder different bid situations10. A back-propagation ANN model to predict the changes inconstruction cost indexes11. Two ANN modules for estimating excavation capacity based onjob conditions and estimating excavator efficiency based on theattributes of operation elements12. A model for decision making about construction modularizationusing ANNsTherefore the ANN technology which can assist in decision makingand can serve to preserve scarce expertise in organizations will bevery useful for success of construction management.Reference:1. BHOKHA, S. & OGUNLANA, S.O., 1999. Application of artificial neuralnetwork to forecast construction duration of buildings at the pre designstage. Engineering Construction and Architectural Management, 6(2), 133-144.2. BOUSSABAINE, A, H., 1996. The use of artificial neural networks inconstriction management: a review. Construction Management and Economics,14, 427-436.3. BOUSSABAINE, A, H., 2001a. Neurofuzzy modeling of constructionprojects’ duration II application. Engineering Construction and ArchitecturalManagement, 8(2), 104-113.4. BOUSSABAINE, A, H., 2001b. Neurofuzzy modeling of constructionprojects’ duration II application. Engineering Construction and ArchitecturalManagement, 8(2), 114-129.5. BOUSSABAINE, A. H. & DUFF, R.A. 1996. An expert-simulation system forconstruction productivity forecasting. Building research and information journal,1,279-28627
Page 1: SLQS JOURNALSRI LANKAN QUANTITY SUR
Page 4 and 5: SLQS JOURNALJanuary 2009CONTENTSPag
Page 7: SLQS JOURNALJanuary 2009Impact of P
Page 10 and 11: SLQS JOURNALJanuary 2009The Role of
Page 12 and 13: SLQS JOURNALJanuary 2009Professiona
Page 14 and 15: SLQS JOURNALJanuary 2009Concurrent
Page 16 and 17: SLQS JOURNALJanuary 2009In this con
Page 18 and 19: SLQS JOURNALJanuary 2009Value Manag
Page 20 and 21: SLQS JOURNALJanuary 2009are categor
Page 22 and 23: SLQS JOURNALLeed and Pearl Rating S
Page 24 and 25: SLQS JOURNALToday’s Emerging Need
Page 26 and 27: SLQS JOURNALJanuary 2009Can a Proje
Page 30 and 31: SLQS JOURNALJanuary 2009What is thi
Page 32 and 33: SLQS JOURNALJanuary 2009Simulated E
Page 34 and 35: SLQS JOURNALJanuary 2009management
Page 36 and 37: SLQS JOURNALJanuary 2009ACKNOWLEDME
Page 38 and 39: SLQS JOURNALJanuary 2009EventPaymen
Page 40: SLQS JOURNALJanuary 2009a deviation

SLQS-Journal Vol. 1 - Slqs-uae.org

You also want an ePaper? Increase the reach of your titles

Delete template?

Save as template?