Uncertainty and risks have been the inherent characteristics of large-scale projects. Although practitioners have applied different project risk management standards, numerous uncertainties, and risks in large-scale construction projects have led to significant failures in fulfilling a project’s goals. Therefore, in this study, a hybrid approach based on failure mode effects analysis (FMEA)/ISO 31000 has been proposed to identify, evaluate, and control the problem effectively. This hybrid approach is not a very accurate approach in providing an appropriate risk response; hence, a mixed-integer programming (MIP) model has been proposed to select the optimized risk response strategies for the project. In the present study, a model based on synergies among project risk responses was developed that is capable of considering the various criteria in the objective function and optimizing them based on the defined projects. Risk response selection for a large-scale project is a complex problem. Because of the nondeterministic polynomial time (NP)-hardness of the presented model, two metaheuristic algorithms, namely, the self-adaptive imperialist competitive algorithm and invasive weed optimization, were developed to solve the proposed MIP model. A large-scale high-rise residential building was evaluated as a case study to investigate the model proposed in this study empirically.

This paper proposes a structure for long-term energy demand forecasting. The proposed hybrid approach, called HPLLNF, uses the local linear neuro-fuzzy (LLNF) model as the forecaster and utilizes the Hodrick–Prescott (HP) filter for extraction of the trend and cyclic components of the energy demand series. Besides, the sophisticated technique of mutual information (MI) is employed to select the most relevant input features with least possible redundancies for the forecast model. Each generated component by the HP filter is then modeled through an LLNF model. Starting from an optimal least square estimation, the local linear model tree (LOLIMOT) learning algorithm increases the complexity of the LLNF model as long as its performance is improved. The proposed HPLLNF model with MI-based input selection is applied to the problem of long-term energy forecasting in three different case studies, including forecasting of the gasoline, crude oil and natural gas demand over the next 12 months. The obtained forecasting results reveal the noteworthy performance of the proposed approach for long-term energy demand forecasting applications.

This study addresses the problem of design of systems for recoverability and discusses that a modularisation of the system architecture can effectively facilitate the recovering operations for a damaged system. To modularise the system architecture, Design Structure Matrix (DSM) methodology along with Axiomatic Design (AD) theory are employed. The study discusses that preventing propagation of the failure(s) to the rest of the system architecture can significantly contribute to the recovery of the system and proposes a methodology and utilises the Mean Time to Recovery (MTTR) as a good indicator reflecting the stochastic behaviour of system recoverability. The present study mathematically shows that modularisation of the system architecture can considerably augment the probability of success in recovering and fulfilling the physical components following experiencing a failure in the system, significantly reduce the length of recovery time and improve the system recoverability. To verify the presented method, architecture of information flow in a real contract is addressed. Results from the application of the method indicated that modularisation of the architecture of the information flow in the contract significantly reduces the volume of time and effort required for adjusting content of the clauses and improves the system recoverability considerably.