Develop a construction plan for the excavation of steel sheet piles and cofferdams for deep foundation pits on bridge platforms,and design construction parameters such as on-site labor and mate-rials based on the construction plan;Using the chicken swarm algorithm to optimize the weights and bi-ases of the extreme learning machine,input the optimized weights and biases into the extreme learning machine model,and construct the best extreme learning machine model;Input construction parameters into the optimal limit learning machine model,output construction cost prediction results,and complete the cost prediction of steel sheet pile cofferdam construction for deep foundation pit excavation of bridge bearing platforms.Relevant experimental tests were conducted on a certain bridge as the research object.Experimental results have shown that this method can effectively predict the labor and material costs during the construction of steel sheet pile cofferdam for deep foundation pit excavation of bridge bearing platforms,as well as the labor and material costs during different construction stages such as the steel sheet pile cofferdam driving stage and the foundation pit dredging and support stage.The residual and relative errors in cost prediction are relatively low.
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