Predicting the outcome of construction litigation using neural networks

Date

1998-01-01

Author

Arditi, David
Oksay, Fatih E.
Tokdemir, Onur Behzat

Metadata

Show full item record

This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

Item Usage Stats

153
views

0
downloads

In this study, neural networks were used to predict the outcome of construction litigation. Disagreements between the owner and the contractor can arise from such considerations as interpretation of the contract, changes made by the owner, differing site conditions, acceleration and suspension of work, and so forth. When there are disagreements between the contractor and the owner, the result is the inefficient use of resources and higher costs for both the owner and the contractor, as well as damage to the reputation of both sides. Neural networks may help to predict the outcome of construction claims that are normally affected by a large number of complex and interrelated factors. Data composed of characteristics of cases and circuit and appellate court decisions were extracted from cases filed in Illinois appellate courts in the last 12 years. A network was trained using these data, and a rate of prediction of 67% was obtained. If the parties to a dispute know with some certainty how the case would be resolved if it were taken to court, it is believed that the number of disputes could be reduced greatly.

URI

https://hdl.handle.net/11511/37466

Journal

Computer-Aided Civil and Infrastructure Engineering

DOI

https://doi.org/10.1111/0885-9507.00087

Collections

Department of Civil Engineering, Article

Suggestions

OpenMETU
Core

Prediction of slip in cable-drum systems using structured neural networks KILIÇ, Ergin; Dölen, Melik (SAGE Publications, 2014-02-01) This study focuses on the slip prediction in a cable-drum system using artificial neural networks for the prospect of developing linear motion sensing scheme for such mechanisms. Both feed-forward and recurrent-type artificial neural network architectures are considered to capture the slip dynamics of cable-drum mechanisms. In the article, the network development is presented in a progressive (step-by-step) fashion for the purpose of not only making the design process transparent to the readers but also hig...
Predicting the shear strength of reinforced concrete beams using artificial neural networks Mansour, MY; Dicleli, Murat; Lee, JY; Zhang, J (Elsevier BV, 2004-05-01) The application of artificial neural networks (ANNs) to predict the ultimate shear strengths of reinforced concrete (RC) beams with transverse reinforcements is investigated in this paper. An ANN model is built, trained and tested using the available test data of 176 RC beams collected from the technical literature. The data used in the ANN model are arranged in a format of nine input parameters that cover the cylinder concrete compressive strength, yield strength of the longitudinal and transverse reinforc...
Effective feature reduction for link prediction in location-based social networks Bayrak, Ahmet Engin; Polat, Faruk (SAGE Publications, 2019-10-01) In this study, we investigated feature-based approaches for improving the link prediction performance for location-based social networks (LBSNs) and analysed their performances. We developed new features based on time, common friend detail and place category information of check-in data in order to make use of information in the data which cannot be utilised by the existing features from the literature. We proposed a feature selection method to determine a feature subset that enhances the prediction perform...
COMPUTATIONAL STUDIES ON NOVEL ENERGETIC MATERIALS: TETRANITRO-[2,2]PARACYCLOPHANES Tuerker, Lemi; Atalar, Taner; Guemues, Selcuk (Informa UK Limited, 2009-01-01) Computational studies on tetranitro derivatives of [2,2]paracyclophane are carried out at B3LYP/6-31G(d,p) level of theory. Optimized geometries, electronic structures and some thermodynamic properties have been obtained in their ground states. Also, detonation performances were evaluated by the Kamlet-Jacobs equations, based on the quantum-chemical calculated densities and heat of formation values. Aromaticities were investigated by performing NICS (nucleus independent chemical shift) calculations using th...
Modeling and implementation of local volatility surfaces in Bayesian framework Animoku, Abdulwahab; Uğur, Ömür; Yolcu-Okur, Yeliz (2018-06-01) In this study, we focus on the reconstruction of volatility surfaces via a Bayesian framework. Apart from classical methods, such as, parametric and non-parametric models, we study the Bayesian analysis of the (stochastically) parametrized volatility structure in Dupire local volatility model. We systematically develop and implement novel mathematical tools for handling the classical methods of constructing local volatility surfaces. The most critical limitation of the classical methods is obtaining negativ...

Citation Formats

D. Arditi, F. E. Oksay, and O. B. Tokdemir, “Predicting the outcome of construction litigation using neural networks,” Computer-Aided Civil and Infrastructure Engineering, pp. 75–81, 1998, Accessed: 00, 2020. [Online]. Available: https://hdl.handle.net/11511/37466.