Welcome to Francis Academic Press

International Journal of New Developments in Engineering and Society, 2019, 3(2); doi: 10.25236/IJNDES.19237.

Research on Construction Safety Management and Quality Management Based on Binary Decision Map


Hongtao Niu

Corresponding Author:
Hongtao Niu

Ankang University, Ankang, Shanxi 725000, China


With the rapid development of science, technology and economic strength in China, the construction industry has developed vigorously. Construction safety and quality management is a systematic project. The quality of construction will directly affect the safety of people's lives and property and the stable development of society. Construction quality and safety is the key to the success of construction projects. It is the fundamental guarantee to maintain the corporate image and obtain long-term economic benefits. Safety-critical system design needs to be guided and evaluated by security analysis. Fault tree based on binary decision graph is one of the commonly used methods of safety analysis in engineering. The risk assessment is based on the risk factor and the simulation application environment design experiment is performed. Correctly handling the relationship between construction safety and quality management plays an important role in the smooth completion of engineering projects and the stable development of society.


Construction; Construction Safety; Binary Decision-making; Safety Analysis

Cite This Paper

Hongtao Niu. Research on Construction Safety Management and Quality Management Based on Binary Decision Map. International Journal of New Developments in Engineering and Society (2019) Vol.3, Issue 2: 277-283. https://doi.org/10.25236/IJNDES.19237.


[1] Zhang S, Teizer J, Lee J K, et al.(2013). Building Information Modeling (BIM) and Safety: Automatic Safety Checking of Construction Models and Schedules. Automation in Construction, vol. 29, no.4, pp.183-195.
[2] Park C S, Kim H J(2013). A framework for construction safety management and visualization system. Automation in Construction, vol. 33, pp.95-103.
[3] Raheem A A, Hinze J W(2014). Disparity between construction safety standards, pp. A global analysis. Safety Science, no. 70, pp.276-287.
[4] Kim S, Shin D H, Woo S, et al. (2014). Identification of IT application areas and potential solutions for perception enhancement to improve construction safety. KSCE Journal of Civil Engineering, vol. 18, no.2, pp.365-379.
[5] Hinze J, Thurman S, Wehle A(2013). Leading indicators of construction safety performance. Safety Science, vol. 51, no.1,vol. pp.23-28.
[6] Perlman A, Sacks R, Barak R(2014). Hazard recognition and risk perception in construction. Safety Science, vol. 64, no.4, pp.22-31.
[7] Zhou Z, Goh Y M, Li Q(2015). Overview and analysis of safety management studies in the construction industry. Safety Science, vol. 72, pp.337-350.
[8] Zou P X W, Sunindijo R Y, Dainty A R J(2014). A mixed methods research design for bridging the gap between research and practice in construction safety. Safety Science, no. 70, pp.316-326.
[9] Chen J, Song X, Lin Z(2016). Revealing the “Invisible Gorilla“ in construction: Estimating construction safety through mental workload assessment. Automation in Construction, no. 63, pp.173-183.
[10] Park J, Park S, Oh T(2015). The development of a web-based construction safety management information system to improve risk assessment. KSCE Journal of Civil Engineering, vol. 19, no.3, pp.528-537.