Journal of Construction Engineering, Management & Innovation - Golden Light Publishing ® | Trabzon

Journal of Construction Engineering, Management & Innovation


Gökhan Kazar Ahmed Almhamdawee Onur Behzat Tokdemir

Due to poor performances in construction projects, there is a necessity to apply novel project management approaches instead of traditional ones. Recently, although new project management methods have emerged and applied in different industries, the construction industry has stayed behind in implementing these techniques. The agile Project Management (APM) approach is one of these methods, and it has been highly discussed recently. As developing countries considerably suffer from poor performance in construction projects, applying new project management methods has gained more importance in these territories. Even though different studies attempted to understand the applicability of the APM, it is also necessary to explore the potential benefits of the agile methods for the construction industry. For this purpose, we surveyed 140 construction experts and received responses on the potential advantages of the APM regarding team and project performance in construction. The results demonstrate that the APM could be feasible for the construction industry and could effectively bring solutions to the project performance issues. We also concluded that the adverse effects of the poor project performance, such as quality defects, schedule variances, cost overruns, and low productivity, could be eliminated especially in the construction industry of developing countries, through APM approaches.

Semih Caglayan Beliz Ozorhon Emre Ilicali

Advances in technology give rise to buildings with complicated systems and components. Many building systems fail to perform as intended owing to lack of a systematic approach adopted during construction. Commissioning is known to enhance the building operational efficiency through bringing a holistic perspective to design, construction, and operation. This study aims to investigate the building commissioning practices in Turkey with the intention of promoting energy efficient building communities in developing countries. Within this context, a questionnaire survey was designed and administered to the commissioning practitioners to explore their perceptions and experiences regarding a number of critical commissioning issues. The findings reveal that (i) most frequently encountered challenges are non-technical rather than technical; (ii) benefits realized in the post-occupancy period overweigh the pre-occupancy benefits; and (iii) experiential features of the commissioning agents are more important for the commissioning agent selection process than the technical and managerial features. The study contributes to the body of knowledge by presenting the main observations on building commissioning practices in a developing country and providing recommendations to enhance the commissioning performance. It is expected to provide a better understanding of the critical issues in building commissioning process and promote its implementation in developing countries.

Huseyin Erol Mehmet Salih Dede

Due to the recent natural disasters and the rapidly increasing need for construction, the concept of building inspection has gained considerable importance in Turkey. However, the literature lacks a stakeholder-based review of the building inspection system despite the fact that its success depends largely on the performance of the actors involved. In order to fill this gap, a survey was conducted with 110 participants representing six stakeholder groups that take an active role in the inspection process. The results of the study revealed that the system has problems in terms of overall performance and employee satisfaction, the stakeholders, especially the contractors and construction managers, do not fulfill their responsibilities, and the sanctions stipulated in the law are insufficient. Moreover, a significant positive correlation was found between the performance of the stakeholders and the sanctions imposed on them. Based on the research findings and synthesis of best practices, it has been suggested to strengthen the penal provisions in the law, increase the qualification and competence of the stakeholders, standardize the inspection procedures, improve communication, and use innovative technologies. Policymakers and practitioners can benefit from the findings of this study to enhance the building inspection system in Turkey.

Levent Sumer David Arditi

Contract administration is one of the most important pillars of a construction project. Its effect starts at bidding and lasts until the end of the guarantee period. A common mistake made by construction owners is structuring the construction contract in a way that minimizes the owner’s risks by relaxing the timing of owner payments, including heavy penalty clauses, requesting bank guarantees with indefinite durations, etc. However, a well-balanced construction contract may protect the work against any unexpected events, avoid potential disputes between the parties, and provide contract clauses that are fair to both parties. Being one of the pioneers in this area, the aim of this study is comparing the time and payment-related clauses of the Turkish bespoken construction contracts with standard FIDIC contracts and provide important insights and guidelines to practitioners. In this study, 304 bespoken contracts undertaken in the Turkish building construction market are analyzed and compared against standard FIDIC conditions. The results show that the timing of the payments is similar to those in FIDIC general conditions, yet time extension is mostly awarded for force majeure only. The majority of the projects were delayed in most cases observed independently from the type of contract and wording of the time extension clause. Recommendations are made to structure a more balanced building construction contract and more successful construction project management in Turkey.

Sami Shams Aldin Hatice Sözer

The study aims to propose a suitable prediction model to deliver the full heating season’s thermal performance dataset by using short-term measured data during the system operation period. Two machine learning-based models, BackPropagation Artificial Neural Network and Adaptive Neuro-Fuzzy Inference System are compared by utilizing the measured data of indoor temperature and relative humidity. The independent variables of the prediction are obtained from the weather data, in addition to the building energy simulation model. Conversely, the data of the dependent variable are obtained from the real measurements from inside of the building for 31,5 days of the heating season, starting from February 22nd, which is called the first heating season. Moreover, the entire heating season of the building is evaluated between November 15th and March 21st, which is called the second heating season when the building’s monthly consumption exceeds 14 kW/m2. The first prediction approach is the feed-forward Artificial Neural Network (ANN) with Back Propagation Learning System (BPS). Four ANN models are structured by input-output and one hidden layer is performed. The second prediction approach is the Adaptive Neuro-Fuzzy Inference System (ANFIS). The Sugeno ANFIS method is utilized in this prediction work. Eight ANFIS models are structured by 6 layers are performed to achieve the prediction. Besides, the main motivation for approaching ANFIS is to avoid the stochasticity of the measured temperature and humidity data. The prediction results are compared with the measured data of the second heating season. The comparison showed that the ANFIS model is more efficient since it achieved an 85% accuracy rate for the indoor temperature and 81% for the humidity prediction. While the ANN prediction accuracy is 81%, 80% relatively for the temperature and humidity. Then the comparison is scaled by selecting the most ordinary period in the measured data to be the data sample that will be used in the comparison. The second comparison showed that the ANFIS model is once again better than the ANN model since the ANFIS prediction accuracy becomes 88% for temperature and 90% for humidity, while the ANN prediction accuracy becomes 83% for temperature and 87% for humidity. Nevertheless, the stochasticity of the measured affected the prediction results in accuracy rates. Hence, according to the achieved accuracy rates, both the ANFIS and ANN approaches are highly validated in this type of prediction.