Search results

In the construction industry, various parties are involved in a project. Consequently, claims and disputes are inevitable in this industry. This paper aims to develop Integrated project delivery (IPD) practices including early involvement of stakeholders and multiparty contracts which its combination with advanced technologies such as blockchain can lead to better dispute management and improve the whole construction process.

Based on literature review, the alternative dispute resolution (ADR) for IPD contacts were identified, and three formats of IPD contracts were selected, and the dispute resolution process of them has been analyzed. Then, based on blockchain review, a conceptual blockchain-based dispute management (BDM) model was generated for ADR in IPD. Model validation was done by an interview. Experts were asked to compare the BDM model with the traditional system regarding the ADR duration.

Analyses of the collected data from the experts demonstrated that the BDM model has better function in terms of time and cost for ADR process when the project is facing serious and considerable number of disputes. The relation between blockchain technology (BCT) and building information modeling (BIM) has been examined through a framework, and the ability of the proposed model for administrating dispute resolution process has been verified using four different scenarios of construction claims that show the system can run successfully.

The current study proposes a truthful model, reliable framework to address the problem of project dispute management in IPD contracts. The system combines the ability to being unchangeable and the reliability characteristics of BCT with informative and automation aspects of BIM together to improve dispute resolution issue in the IPD system.

In this research, the authors demonstrate the advantage of reinforcement learning (RL) based intrusion detection systems (IDS) to solve very complex problems (e.g. selecting input features, considering scarce resources and constrains) that cannot be solved by classical machine learning. The authors include a comparative study to build intrusion detection based on statistical machine learning and representational learning, using knowledge discovery in databases (KDD) Cup99 and Installation Support Center of Expertise (ISCX) 2012.

The methodology applies a data analytics approach, consisting of data exploration and machine learning model training and evaluation. To build a network-based intrusion detection system, the authors apply dueling double deep Q-networks architecture enabled with costly features, k-nearest neighbors (K-NN), support-vector machines (SVM) and convolution neural networks (CNN).

Machine learning-based intrusion detection are trained on historical datasets which lead to model drift and lack of generalization whereas RL is trained with data collected through interactions. RL is bound to learn from its interactions with a stochastic environment in the absence of a training dataset whereas supervised learning simply learns from collected data and require less computational resources.

All machine learning models have achieved high accuracy values and performance. One potential reason is that both datasets are simulated, and not realistic. It was not clear whether a validation was ever performed to show that data were collected from real network traffics.

The study provides guidelines to implement IDS with classical supervised learning, deep learning and RL.

The research applied the dueling double deep Q-networks architecture enabled with costly features to build network-based intrusion detection from network traffics. This research presents a comparative study of reinforcement-based instruction detection with counterparts built with statistical and representational machine learning.

Against a background context of Construction 4.0, this study aims to explore what digital leadership in construction firms is, why it is necessary and what considerations it involves. It provides a digital leadership considerations framework to aid organisational digital transformations.

To address the research question informing what digital leadership involves, an interpretivist study was conducted. Eleven purposively selected expert practitioners, including construction business leaders and company digital champions were surveyed using in-depth semi-structured interviews. This obtained insights and reflections on how construction leaders can impel digital transformations.

How construction leaders can embed the skills to drive such transformation in their firms, what the essential digital skills for construction businesses now are in Construction 4.0 and how leaders can attempt to evaluate the value of digital technology for their business practices, are all crystallised. Impacts of digital transformations on the construction workplace are also highlighted.

Because of this work, business leaders will be better equipped to lead and support further digital transformation efforts in Construction 4.0. Future research directions aid academics.

Clear definitions of digital transformation and digital leadership are provided, and a digital leadership considerations framework is provided to support firm-level digital transformations for Construction 4.0.